Showing 1–26 of 26 results for author: Gope, P

CDN Tsunami: Exploiting HTTP/3-HTTP/1.1 Conversion for DoS Attacks

Authors: Ziyu Lin, Tianlong Su, Yingjie Lin, Prosanta Gope, Yinzhi Cao, Ximeng Liu, Biplab Sikdar

Abstract: Content Delivery Networks (CDNs) provide high availability, accelerate content delivery for their host websites, but are also vulnerable to different types of Denial-of-Service (DoS) attacks. Prior works have studied a variety of DoS attacks with HTTP/1.1 or HTTP/2 connections, but most of them are being fixed, making CDNs robust against such attacks. One unexplored research area is how the recent… ▽ More Content Delivery Networks (CDNs) provide high availability, accelerate content delivery for their host websites, but are also vulnerable to different types of Denial-of-Service (DoS) attacks. Prior works have studied a variety of DoS attacks with HTTP/1.1 or HTTP/2 connections, but most of them are being fixed, making CDNs robust against such attacks. One unexplored research area is how the recent introduction of HTTP/3 at CDNs affects the DoS attack landscape, especially when there are heterogeneous deployments of HTTP/3 and HTTP/1.1 between CDNs and host websites. In this paper, we design the first study of DoS attacks against HTTP/3 protocols deployed at CDNs. Our key insight is that when the CDN adopts HTTP/3 but the host websites use HTTP/1.1, an adversary can utilize the disparity to amplify a small amount of traffic to the CDN using HTTP/3 to a large amount from the CDN to the host website using HTTP/1.1. More specifically, we design two attack variations-HTTP/3 Bandwidth Amplification (HBA) and HTTP/3 Connection Amplification (HCA)-targeting the bandwidth and the number of connections, respectively. Furthermore, we conduct a large-scale measurement upon the Tranco Top 1M domain list to quantify the real-world impact of these attacks, identifying 42,330 subdomains that are potentially vulnerable to our attacks. Finally, we responsibly disclose the details of our attacks to the affected CDN vendors: so far, two vendors have already acknowledged their vulnerabilities with bounties and have deployed our mitigations. △ Less

Submitted 29 July, 2026; originally announced July 2026.

Comments: This paper has been accepted for publication at the IEEE International Symposium on Reliable Distributed Systems (SRDS) 2026

Automated Stealthy Wear-Out Attack on Digital Twins With Deep Reinforcement Learning

Authors: Joshua Haworth, Aryan Pasikhani, George Pavlides, Prosanta Gope, John Clark

Abstract: Digital Twins (DTs) have emerged as pivotal enablers of Industry 4.0, offering transformative capabilities such as real-time monitoring, advanced simulation, and precise control of physical assets. By bridging the physical and virtual domains, DTs facilitate seamless integration of data-driven decision-making and operational optimisation. However, this seamless interaction significantly expands th… ▽ More Digital Twins (DTs) have emerged as pivotal enablers of Industry 4.0, offering transformative capabilities such as real-time monitoring, advanced simulation, and precise control of physical assets. By bridging the physical and virtual domains, DTs facilitate seamless integration of data-driven decision-making and operational optimisation. However, this seamless interaction significantly expands the attack surface of industrial systems, creating vulnerabilities that adversaries can exploit. This paper introduces a novel and stealthy wear-out attack leveraging Deep Reinforcement Learning (DRL) to target DT-enabled infrastructures. The adversary strategically and covertly manipulates control signals, inducing increased torque on a specific joint to accelerate wear and tear while evading detection by a state-of-the-art anomaly detection system. Extensive benchmarking of reinforcement learning algorithms - including Twin Delayed Deep Deterministic Policy Gradient (TD3), Soft Actor-Critic (SAC), Proximal Policy Optimisation (PPO), and Advantage Actor-Critic (A2C) - revealed that SAC consistently outperformed its counterparts in terms of sample efficiency, stability, and overall attack effectiveness. We evaluate the proposed adversary in an industrial setting using the UR10e robotic arm. Results demonstrate the adversary's ability to significantly elevate torque levels on the targeted joint, leading to accelerated degradation and increased maintenance costs, all while operating stealthily and avoiding detection. Our findings highlight the substantial risks posed by DRL-driven adversaries to DT-enabled environments and emphasise the critical need for robust defence mechanisms to protect critical industrial systems. △ Less

Submitted 12 July, 2026; originally announced July 2026.

Selection Integrity for LLM Graph Memory: An Accumulability Criterion for Information-Flow-Blind Retrieval

Authors: Zeming Fei, Hongming Fei, Xiaoyang Wang, Yang yang, Prosanta Gope, Biplab Sikdar, Ying Zhang

Abstract: Agent memory is moving to graphs, and the provenance defenses now being built for it all check one thing: the provenance of the records an agent retrieves. We show that this entire class of defense is blind by construction. A long-term graph memory runs a global selection step over writable graph structure, so structure that an untrusted principal writes changes \emph{which} authenticated facts ar… ▽ More Agent memory is moving to graphs, and the provenance defenses now being built for it all check one thing: the provenance of the records an agent retrieves. We show that this entire class of defense is blind by construction. A long-term graph memory runs a global selection step over writable graph structure, so structure that an untrusted principal writes changes \emph{which} authenticated facts are selected while the cited evidence stays fully authenticated; faithful information-flow control (IFC), checking the provenance of what the reader uses (all of it authenticated), makes the byte-identical decision to no defense at all, across document-QA substrates and real multi-session agent memory. In the most consequential instance, a no-source structural write silently misdirects $28$ irreversible ledger transfers over $499$ live actions: faithful IFC permits every one, and \authselect\ prevents every one. We then characterize exactly which memories are exposed: a selector admits the channel when its structural term can reallocate an $Ω(1)$ share of top-$k$ membership past a selected fact's margin. Personalized PageRank can, since a sourceless write reroutes conserved random-walk mass; a content-fixed reranker cannot, and Graphiti's node-distance, which leans on structure \emph{more} than PageRank does, stays immune. Reallocatability, not reliance, is the predictor. We prove the immune case in general and the open case under a chokepoint condition we verify. Closing the channel forces any provenance defense to recompute selection on the authenticated subgraph, which is what \authselect\ does, at zero over-block and $2$--$3\%$ latency. △ Less

Submitted 10 June, 2026; originally announced June 2026.

Quantifying Memory Cells Vulnerability for DRAM Security

Authors: Zilong Hu, Hongming Fei, Prosanta Gope, Jack Miskelly, Owen Millwood, Biplab Sikdar

Abstract: Dynamic Random Access Memory (DRAM) is pervasive in computer systems. Cell vulnerabilities caused by unintended phenomena (forced retention failure, latency alteration, rowhammer and rowpress) lead to unintended bit flips in memory. These phenomena have been explored as attacks to violate data integrity and confidentiality during normal operation, but also exploited as a benefit in security system… ▽ More Dynamic Random Access Memory (DRAM) is pervasive in computer systems. Cell vulnerabilities caused by unintended phenomena (forced retention failure, latency alteration, rowhammer and rowpress) lead to unintended bit flips in memory. These phenomena have been explored as attacks to violate data integrity and confidentiality during normal operation, but also exploited as a benefit in security systems as a method to generate random secret keys and unique device fingerprints (e.g. Physically Unclonable Functions). In both cases, attackers may wish to exploit knowledge of individual cell flip vulnerability to predict the current/future data contents of a set of cells, which can be utilised to break security systems. In this work, we develop a quantitative, cell-level circuit framework that models DRAM vulnerability directly from its physical charge leakage and disturbance pathways. By linking these device-layer behaviours to system-level security properties, our framework enables systematic evaluation of DRAM with respect to volatility (retention), integrity (disturbance-induced modification), and confidentiality (pattern-dependent leakage). We further demonstrate how the framework can be applied to well-known failure modes, revealing non-uniform and context-dependent vulnerability patterns. This work provides both theoretical foundations and practical evaluation tools for evaluating the suitability of DRAM use within security applications. △ Less

Submitted 19 March, 2026; originally announced March 2026.

Drawing the LINE: Cryptographic Analysis and Security Improvements for the LINE E2EE Protocol

Authors: Benjamin Dowling, Prosanta Gope, Mehr U Nisa, Bhagya Wimalasiri

Abstract: LINE has emerged as one of the most popular communication platforms in many East Asian countries, including Thailand and Japan, with millions of active users. Therefore, it is essential to understand its security guarantees. In this work, we present the first provable security analysis of the LINE version two (LINEv2) messaging protocol, focusing on its cryptographic guarantees in a real-world set… ▽ More LINE has emerged as one of the most popular communication platforms in many East Asian countries, including Thailand and Japan, with millions of active users. Therefore, it is essential to understand its security guarantees. In this work, we present the first provable security analysis of the LINE version two (LINEv2) messaging protocol, focusing on its cryptographic guarantees in a real-world setting. We capture the architecture and security of the LINE messaging protocol by modifying the Multi-Stage Key Exchange (MSKE) model, a framework for analysing cryptographic protocols under adversarial conditions. While LINEv2 achieves basic security properties such as key indistinguishability and message authentication, we highlight the lack of forward secrecy (FS) and post-compromise security (PCS). To address this, we introduce a stronger version of the LINE protocol, introducing FS and PCS to LINE, analysing and benchmarking our results. △ Less

Submitted 20 February, 2026; originally announced February 2026.

Baiting AI: Deceptive Adversary Against AI-Protected Industrial Infrastructures

Authors: Aryan Pasikhani, Prosanta Gope, Yang Yang, Shagufta Mehnaz, Biplab Sikdar

Abstract: This paper explores a new cyber-attack vector targeting Industrial Control Systems (ICS), particularly focusing on water treatment facilities. Developing a new multi-agent Deep Reinforcement Learning (DRL) approach, adversaries craft stealthy, strategically timed, wear-out attacks designed to subtly degrade product quality and reduce the lifespan of field actuators. This sophisticated method lever… ▽ More This paper explores a new cyber-attack vector targeting Industrial Control Systems (ICS), particularly focusing on water treatment facilities. Developing a new multi-agent Deep Reinforcement Learning (DRL) approach, adversaries craft stealthy, strategically timed, wear-out attacks designed to subtly degrade product quality and reduce the lifespan of field actuators. This sophisticated method leverages DRL methodology not only to execute precise and detrimental impacts on targeted infrastructure but also to evade detection by contemporary AI-driven defence systems. By developing and implementing tailored policies, the attackers ensure their hostile actions blend seamlessly with normal operational patterns, circumventing integrated security measures. Our research reveals the robustness of this attack strategy, shedding light on the potential for DRL models to be manipulated for adversarial purposes. Our research has been validated through testing and analysis in an industry-level setup. For reproducibility and further study, all related materials, including datasets and documentation, are publicly accessible. △ Less

Submitted 13 January, 2026; originally announced January 2026.

Unified Framework for Qualifying Security Boundary of PUFs Against Machine Learning Attacks

Authors: Hongming Fei, Zilong Hu, Prosanta Gope, Biplab Sikdar

Abstract: Physical Unclonable Functions (PUFs) serve as lightweight, hardware-intrinsic entropy sources widely deployed in IoT security applications. However, delay-based PUFs are vulnerable to Machine Learning Attacks (MLAs), undermining their assumed unclonability. There are no valid metrics for evaluating PUF MLA resistance, but empirical modelling experiments, which lack theoretical guarantees and are h… ▽ More Physical Unclonable Functions (PUFs) serve as lightweight, hardware-intrinsic entropy sources widely deployed in IoT security applications. However, delay-based PUFs are vulnerable to Machine Learning Attacks (MLAs), undermining their assumed unclonability. There are no valid metrics for evaluating PUF MLA resistance, but empirical modelling experiments, which lack theoretical guarantees and are highly sensitive to advances in machine learning techniques. To address the fundamental gap between PUF designs and security qualifications, this work proposes a novel, formal, and unified framework for evaluating PUF security against modelling attacks by providing security lower bounds, independent of specific attack models or learning algorithms. We mathematically characterise the adversary's advantage in predicting responses to unseen challenges based solely on observed challenge-response pairs (CRPs), formulating the problem as a conditional probability estimation over the space of candidate PUFs. We present our analysis on previous "broken" PUFs, e.g., Arbiter PUFs, XOR PUFs, Feed-Forward PUFs, and for the first time compare their MLA resistance in a formal way. In addition, we evaluate the currently "secure" CT PUF, and show its security boundary. We demonstrate that the proposed approach systematically quantifies PUF resilience, captures subtle security differences, and provides actionable, theoretically grounded security guarantees for the practical deployment of PUFs. △ Less

Submitted 8 January, 2026; originally announced January 2026.

Comments: 13 pages, 8 figures

MSC Class: 94A60; 68T05 ACM Class: D.4.6; K.6.5

Towards Irreversible Machine Unlearning for Diffusion Models

Authors: Xun Yuan, Zilong Zhao, Jiayu Li, Aryan Pasikhani, Prosanta Gope, Biplab Sikdar

Abstract: Diffusion models are renowned for their state-of-the-art performance in generating synthetic images. However, concerns related to safety, privacy, and copyright highlight the need for machine unlearning, which can make diffusion models forget specific training data and prevent the generation of sensitive or unwanted content. Current machine unlearning methods for diffusion models are primarily des… ▽ More Diffusion models are renowned for their state-of-the-art performance in generating synthetic images. However, concerns related to safety, privacy, and copyright highlight the need for machine unlearning, which can make diffusion models forget specific training data and prevent the generation of sensitive or unwanted content. Current machine unlearning methods for diffusion models are primarily designed for conditional diffusion models and focus on unlearning specific data classes or features. Among these methods, finetuning-based machine unlearning methods are recognized for their efficiency and effectiveness, which update the parameters of pre-trained diffusion models by minimizing carefully designed loss functions. However, in this paper, we propose a novel attack named Diffusion Model Relearning Attack (DiMRA), which can reverse the finetuning-based machine unlearning methods, posing a significant vulnerability of this kind of technique. Without prior knowledge of the unlearning elements, DiMRA optimizes the unlearned diffusion model on an auxiliary dataset to reverse the unlearning, enabling the model to regenerate previously unlearned elements. To mitigate this vulnerability, we propose a novel machine unlearning method for diffusion models, termed as Diffusion Model Unlearning by Memorization (DiMUM). Unlike traditional methods that focus on forgetting, DiMUM memorizes alternative data or features to replace targeted unlearning data or features in order to prevent generating such elements. In our experiments, we demonstrate the effectiveness of DiMRA in reversing state-of-the-art finetuning-based machine unlearning methods for diffusion models, highlighting the need for more robust solutions. We extensively evaluate DiMUM, demonstrating its superior ability to preserve the generative performance of diffusion models while enhancing robustness against DiMRA. △ Less

Submitted 3 December, 2025; originally announced December 2025.

Privacy-preserving Robotic-based Multi-factor Authentication Scheme for Secure Automated Delivery System

Authors: Yang Yang, Aryan Mohammadi Pasikhani, Prosanta Gope, Biplab Sikdar

Abstract: Package delivery is a critical aspect of various industries, but it often incurs high financial costs and inefficiencies when relying solely on human resources. The last-mile transport problem, in particular, contributes significantly to the expenditure of human resources in major companies. Robot-based delivery systems have emerged as a potential solution for last-mile delivery to address this ch… ▽ More Package delivery is a critical aspect of various industries, but it often incurs high financial costs and inefficiencies when relying solely on human resources. The last-mile transport problem, in particular, contributes significantly to the expenditure of human resources in major companies. Robot-based delivery systems have emerged as a potential solution for last-mile delivery to address this challenge. However, robotic delivery systems still face security and privacy issues, like impersonation, replay, man-in-the-middle attacks (MITM), unlinkability, and identity theft. In this context, we propose a privacy-preserving multi-factor authentication scheme specifically designed for robot delivery systems. Additionally, AI-assisted robotic delivery systems are susceptible to machine learning-based attacks (e.g. FGSM, PGD, etc.). We introduce the \emph{first} transformer-based audio-visual fusion defender to tackle this issue, which effectively provides resilience against adversarial samples. Furthermore, we provide a rigorous formal analysis of the proposed protocol and also analyse the protocol security using a popular symbolic proof tool called ProVerif and Scyther. Finally, we present a real-world implementation of the proposed robotic system with the computation cost and energy consumption analysis. Code and pre-trained models are available at: https://drive.google.com/drive/folders/18B2YbxtV0Pyj5RSFX-ZzCGtFOyorBHil △ Less

Submitted 26 November, 2024; originally announced November 2024.

Leveraging A New GAN-based Transformer with ECDH Crypto-system for Enhancing Energy Theft Detection in Smart Grid

Authors: Yang Yang, Xun Yuan, Arwa Alromih, Aryan Mohammadi Pasikhani, Prosanta Gope, Biplab Sikdar

Abstract: Detecting energy theft is vital for effectively managing power grids, as it ensures precise billing and prevents financial losses. Split-learning emerges as a promising decentralized machine learning technique for identifying energy theft while preserving user data confidentiality. Nevertheless, traditional split learning approaches are vulnerable to privacy leakage attacks, which significantly th… ▽ More Detecting energy theft is vital for effectively managing power grids, as it ensures precise billing and prevents financial losses. Split-learning emerges as a promising decentralized machine learning technique for identifying energy theft while preserving user data confidentiality. Nevertheless, traditional split learning approaches are vulnerable to privacy leakage attacks, which significantly threaten data confidentiality. To address this challenge, we propose a novel GAN-Transformer-based split learning framework in this paper. This framework leverages the strengths of the transformer architecture, which is known for its capability to process long-range dependencies in energy consumption data. Thus, it enhances the accuracy of energy theft detection without compromising user privacy. A distinctive feature of our approach is the deployment of a novel mask-based method, marking a first in its field to effectively combat privacy leakage in split learning scenarios targeted at AI-enabled adversaries. This method protects sensitive information during the model's training phase. Our experimental evaluations indicate that the proposed framework not only achieves accuracy levels comparable to conventional methods but also significantly enhances privacy protection. The results underscore the potential of the GAN-Transformer split learning framework as an effective and secure tool in the domain of energy theft detection. △ Less

Submitted 26 November, 2024; originally announced November 2024.

Strong Privacy-Preserving Universally Composable AKA Protocol with Seamless Handover Support for Mobile Virtual Network Operator

Authors: Rabiah Alnashwan, Yang Yang, Yilu Dong, Prosanta Gope, Behzad Abdolmaleki, Syed Rafiul Hussain

Abstract: Consumers seeking a new mobile plan have many choices in the present mobile landscape. The Mobile Virtual Network Operator (MVNO) has recently gained considerable attention among these options. MVNOs offer various benefits, making them an appealing choice for a majority of consumers. These advantages encompass flexibility, access to cutting-edge technologies, enhanced coverage, superior customer s… ▽ More Consumers seeking a new mobile plan have many choices in the present mobile landscape. The Mobile Virtual Network Operator (MVNO) has recently gained considerable attention among these options. MVNOs offer various benefits, making them an appealing choice for a majority of consumers. These advantages encompass flexibility, access to cutting-edge technologies, enhanced coverage, superior customer service, and substantial cost savings. Even though MVNO offers several advantages, it also creates some security and privacy concerns for the customer simultaneously. For instance, in the existing solution, MVNO needs to hand over all the sensitive details, including the users' identities and master secret keys of their customers, to a mobile operator (MNO) to validate the customers while offering any services. This allows MNOs to have unrestricted access to the MVNO subscribers' location and mobile data, including voice calls, SMS, and Internet, which the MNOs frequently sell to third parties (e.g., advertisement companies and surveillance agencies) for more profit. Although critical for mass users, such privacy loss has been historically ignored due to the lack of practical and privacy-preserving solutions for registration and handover procedures in cellular networks. In this paper, we propose a universally composable authentication and handover scheme with strong user privacy support, where each MVNO user can validate a mobile operator (MNO) and vice-versa without compromising user anonymity and unlinkability support. Here, we anticipate that our proposed solution will most likely be deployed by the MVNO(s) to ensure enhanced privacy support to their customer(s). △ Less

Submitted 7 September, 2024; originally announced September 2024.

VFLGAN-TS: Vertical Federated Learning-based Generative Adversarial Networks for Publication of Vertically Partitioned Time-Series Data

Authors: Xun Yuan, Zilong Zhao, Prosanta Gope, Biplab Sikdar

Abstract: In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, often original data cannot be shared due to privacy concerns and regulations. A potential solution is to release a synthetic dataset with a similar distribution to the private dataset. Nevertheless, in some scenarios, the attributes required to tra… ▽ More In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, often original data cannot be shared due to privacy concerns and regulations. A potential solution is to release a synthetic dataset with a similar distribution to the private dataset. Nevertheless, in some scenarios, the attributes required to train an AI model are distributed among different parties, and the parties cannot share the local data for synthetic data construction due to privacy regulations. In PETS 2024, we recently introduced the first Vertical Federated Learning-based Generative Adversarial Network (VFLGAN) for publishing vertically partitioned static data. However, VFLGAN cannot effectively handle time-series data, presenting both temporal and attribute dimensions. In this article, we proposed VFLGAN-TS, which combines the ideas of attribute discriminator and vertical federated learning to generate synthetic time-series data in the vertically partitioned scenario. The performance of VFLGAN-TS is close to that of its counterpart, which is trained in a centralized manner and represents the upper limit for VFLGAN-TS. To further protect privacy, we apply a Gaussian mechanism to make VFLGAN-TS satisfy an $(ε,δ)$-differential privacy. Besides, we develop an enhanced privacy auditing scheme to evaluate the potential privacy breach through the framework of VFLGAN-TS and synthetic datasets. △ Less

Submitted 5 September, 2024; originally announced September 2024.

VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication

Authors: Xun Yuan, Yang Yang, Prosanta Gope, Aryan Pasikhani, Biplab Sikdar

Abstract: In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, good data is not a free lunch and is always hard to access due to privacy regulations like the General Data Protection Regulation (GDPR). A potential solution is to release a synthetic dataset with a similar distribution to that of the private data… ▽ More In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, good data is not a free lunch and is always hard to access due to privacy regulations like the General Data Protection Regulation (GDPR). A potential solution is to release a synthetic dataset with a similar distribution to that of the private dataset. Nevertheless, in some scenarios, it has been found that the attributes needed to train an AI model belong to different parties, and they cannot share the raw data for synthetic data publication due to privacy regulations. In PETS 2023, Xue et al. proposed the first generative adversary network-based model, VertiGAN, for vertically partitioned data publication. However, after thoroughly investigating, we found that VertiGAN is less effective in preserving the correlation among the attributes of different parties. This article proposes a Vertical Federated Learning-based Generative Adversarial Network, VFLGAN, for vertically partitioned data publication to address the above issues. Our experimental results show that compared with VertiGAN, VFLGAN significantly improves the quality of synthetic data. Taking the MNIST dataset as an example, the quality of the synthetic dataset generated by VFLGAN is 3.2 times better than that generated by VertiGAN w.r.t. the Fréchet Distance. We also designed a more efficient and effective Gaussian mechanism for the proposed VFLGAN to provide the synthetic dataset with a differential privacy guarantee. On the other hand, differential privacy only gives the upper bound of the worst-case privacy guarantee. This article also proposes a practical auditing scheme that applies membership inference attacks to estimate privacy leakage through the synthetic dataset. △ Less

Submitted 15 April, 2024; originally announced April 2024.

Journal ref: Proceedings on Privacy Enhancing Technologies Symposium 4 (2024) 840-858

Quantum-Secure Certificate-Less Conditional Privacy-Preserving Authentication for VANET

Authors: Girraj Kumar Verma, Nahida Majeed Wani, Prosanta Gope

Abstract: Vehicular Ad-hoc Networks (VANETs) marked a pronounced change in the Intelligent Transport System and Smart Cities through seamless vehicle communication to intensify safety and efficacy. However, a few authentication schemes have been devised in the literature to ensure the authenticity of the source and information in the post-quantum era. The most popular base for such construction is lattice-b… ▽ More Vehicular Ad-hoc Networks (VANETs) marked a pronounced change in the Intelligent Transport System and Smart Cities through seamless vehicle communication to intensify safety and efficacy. However, a few authentication schemes have been devised in the literature to ensure the authenticity of the source and information in the post-quantum era. The most popular base for such construction is lattice-based cryptography. However, existing lattice-based authentication schemes fall short of addressing the potential challenges of the leakage of the master secret key and key-escrow problem. By ingeniously addressing both issues, the paper proposes the \emph{first} quantum secure authentication scheme to eliminate the flaws while maintaining the system's overall efficiency intact. Compared to the state-of-the-art schemes, the provable security and overall performance assessment highlight the suitability of the proposed approach. △ Less

Submitted 20 March, 2024; originally announced March 2024.

Comments: Paper submitted to IEEE TDSC under review

UniHand: Privacy-preserving Universal Handover for Small-Cell Networks in 5G-enabled Mobile Communication with KCI Resilience

Authors: Rabiah Alnashwan, Prosanta Gope, Benjamin Dowling

Abstract: Introducing Small Cell Networks (SCN) has significantly improved wireless link quality, spectrum efficiency and network capacity, which has been viewed as one of the key technologies in the fifth-generation (5G) mobile network. However, this technology increases the frequency of handover (HO) procedures caused by the dense deployment of cells in the network with reduced cell coverage, bringing new… ▽ More Introducing Small Cell Networks (SCN) has significantly improved wireless link quality, spectrum efficiency and network capacity, which has been viewed as one of the key technologies in the fifth-generation (5G) mobile network. However, this technology increases the frequency of handover (HO) procedures caused by the dense deployment of cells in the network with reduced cell coverage, bringing new security and privacy issues. The current 5G-AKA and HO protocols are vulnerable to security weaknesses, such as the lack of forward secrecy and identity confusion attacks. The high HO frequency of HOs might magnify these security and privacy concerns in the 5G mobile network. This work addresses these issues by proposing a secure privacy-preserving universal HO scheme ($\UniHand$) for SCNs in 5G mobile communication. $\UniHand$ can achieve mutual authentication, strong anonymity, perfect forward secrecy, key-escrow-free and key compromise impersonation (KCI) resilience. To the best of our knowledge, this is the \textit{first} scheme to achieve secure, privacy-preserving universal HO with \textit{KCI} resilience for roaming users in 5G environment. We demonstrate that our proposed scheme is resilient against all the essential security threats by performing a comprehensive formal security analysis and conducting relevant experiments to show the cost-effectiveness of the proposed scheme. △ Less

Submitted 4 April, 2024; v1 submitted 12 March, 2024; originally announced March 2024.

Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting

Authors: Asif Iqbal, Prosanta Gope, Biplab Sikdar

Abstract: Accurate load forecasting is crucial for energy management, infrastructure planning, and demand-supply balancing. Smart meter data availability has led to the demand for sensor-based load forecasting. Conventional ML allows training a single global model using data from multiple smart meters requiring data transfer to a central server, raising concerns for network requirements, privacy, and securi… ▽ More Accurate load forecasting is crucial for energy management, infrastructure planning, and demand-supply balancing. Smart meter data availability has led to the demand for sensor-based load forecasting. Conventional ML allows training a single global model using data from multiple smart meters requiring data transfer to a central server, raising concerns for network requirements, privacy, and security. We propose a split learning-based framework for load forecasting to alleviate this issue. We split a deep neural network model into two parts, one for each Grid Station (GS) responsible for an entire neighbourhood's smart meters and the other for the Service Provider (SP). Instead of sharing their data, client smart meters use their respective GSs' model split for forward pass and only share their activations with the GS. Under this framework, each GS is responsible for training a personalized model split for their respective neighbourhoods, whereas the SP can train a single global or personalized model for each GS. Experiments show that the proposed models match or exceed a centrally trained model's performance and generalize well. Privacy is analyzed by assessing information leakage between data and shared activations of the GS model split. Additionally, differential privacy enhances local data privacy while examining its impact on performance. A transformer model is used as our base learner. △ Less

Submitted 6 July, 2025; v1 submitted 3 March, 2024; originally announced March 2024.

Attacking Delay-based PUFs with Minimal Adversary Model

Authors: Hongming Fei, Owen Millwood, Prosanta Gope, Jack Miskelly, Biplab Sikdar

Abstract: Physically Unclonable Functions (PUFs) provide a streamlined solution for lightweight device authentication. Delay-based Arbiter PUFs, with their ease of implementation and vast challenge space, have received significant attention; however, they are not immune to modelling attacks that exploit correlations between their inputs and outputs. Research is therefore polarized between developing modelli… ▽ More Physically Unclonable Functions (PUFs) provide a streamlined solution for lightweight device authentication. Delay-based Arbiter PUFs, with their ease of implementation and vast challenge space, have received significant attention; however, they are not immune to modelling attacks that exploit correlations between their inputs and outputs. Research is therefore polarized between developing modelling-resistant PUFs and devising machine learning attacks against them. This dichotomy often results in exaggerated concerns and overconfidence in PUF security, primarily because there lacks a universal tool to gauge a PUF's security. In many scenarios, attacks require additional information, such as PUF type or configuration parameters. Alarmingly, new PUFs are often branded `secure' if they lack a specific attack model upon introduction. To impartially assess the security of delay-based PUFs, we present a generic framework featuring a Mixture-of-PUF-Experts (MoPE) structure for mounting attacks on various PUFs with minimal adversarial knowledge, which provides a way to compare their performance fairly and impartially. We demonstrate the capability of our model to attack different PUF types, including the first successful attack on Heterogeneous Feed-Forward PUFs using only a reasonable amount of challenges and responses. We propose an extension version of our model, a Multi-gate Mixture-of-PUF-Experts (MMoPE) structure, facilitating multi-task learning across diverse PUFs to recognise commonalities across PUF designs. This allows a streamlining of training periods for attacking multiple PUFs simultaneously. We conclude by showcasing the potent performance of MoPE and MMoPE across a spectrum of PUF types, employing simulated, real-world unbiased, and biased data sets for analysis. △ Less

Submitted 1 March, 2024; originally announced March 2024.

Comments: 13 pages, 6 figures, journal

MSC Class: 68M25 ACM Class: I.2.8

AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks

Authors: Soumadeep Das, Aryan Mohammadi Pasikhani, Prosanta Gope, John A. Clark, Chintan Patel, Biplab Sikdar

Abstract: Underwater Acoustic Sensor Networks (UW-ASNs) are predominantly used for underwater environments and find applications in many areas. However, a lack of security considerations, the unstable and challenging nature of the underwater environment, and the resource-constrained nature of the sensor nodes used for UW-ASNs (which makes them incapable of adopting security primitives) make the UW-ASN prone… ▽ More Underwater Acoustic Sensor Networks (UW-ASNs) are predominantly used for underwater environments and find applications in many areas. However, a lack of security considerations, the unstable and challenging nature of the underwater environment, and the resource-constrained nature of the sensor nodes used for UW-ASNs (which makes them incapable of adopting security primitives) make the UW-ASN prone to vulnerabilities. This paper proposes an Adaptive decentralised Intrusion Detection and Prevention System called AIDPS for UW-ASNs. The proposed AIDPS can improve the security of the UW-ASNs so that they can efficiently detect underwater-related attacks (e.g., blackhole, grayhole and flooding attacks). To determine the most effective configuration of the proposed construction, we conduct a number of experiments using several state-of-the-art machine learning algorithms (e.g., Adaptive Random Forest (ARF), light gradient-boosting machine, and K-nearest neighbours) and concept drift detection algorithms (e.g., ADWIN, kdqTree, and Page-Hinkley). Our experimental results show that incremental ARF using ADWIN provides optimal performance when implemented with One-class support vector machine (SVM) anomaly-based detectors. Furthermore, our extensive evaluation results also show that the proposed scheme outperforms state-of-the-art bench-marking methods while providing a wider range of desirable features such as scalability and complexity. △ Less

Submitted 14 September, 2023; originally announced September 2023.

A Novel DDPM-based Ensemble Approach for Energy Theft Detection in Smart Grids

Authors: Xun Yuan, Yang Yang, Asif Iqbal, Prosanta Gope, Biplab Sikdar

Abstract: Energy theft, characterized by manipulating energy consumption readings to reduce payments, poses a dual threat-causing financial losses for grid operators and undermining the performance of smart grids. Effective Energy Theft Detection (ETD) methods become crucial in mitigating these risks by identifying such fraudulent activities in their early stages. However, the majority of current ETD method… ▽ More Energy theft, characterized by manipulating energy consumption readings to reduce payments, poses a dual threat-causing financial losses for grid operators and undermining the performance of smart grids. Effective Energy Theft Detection (ETD) methods become crucial in mitigating these risks by identifying such fraudulent activities in their early stages. However, the majority of current ETD methods rely on supervised learning, which is hindered by the difficulty of labelling data and the risk of overfitting known attacks. To address these challenges, several unsupervised ETD methods have been proposed, focusing on learning the normal patterns from honest users, specifically the reconstruction of input. However, our investigation reveals a limitation in current unsupervised ETD methods, as they can only detect anomalous behaviours in users exhibiting regular patterns. Users with high-variance behaviours pose a challenge to these methods. In response, this paper introduces a Denoising Diffusion Probabilistic Model (DDPM)-based ETD approach. This innovative approach demonstrates impressive ETD performance on high-variance smart grid data by incorporating additional attributes correlated with energy consumption. The proposed methods improve the average ETD performance on high-variance smart grid data from below 0.5 to over 0.9 w.r.t. AUC. On the other hand, our experimental findings indicate that while the state-of-the-art ETD methods based on reconstruction error can identify ETD attacks for the majority of users, they prove ineffective in detecting attacks for certain users. To address this, we propose a novel ensemble approach that considers both reconstruction error and forecasting error, enhancing the robustness of the ETD methodology. The proposed ensemble method improves the average ETD performance on the stealthiest attacks from nearly 0 to 0.5 w.r.t. 5%-TPR. △ Less

Submitted 13 January, 2024; v1 submitted 30 July, 2023; originally announced July 2023.

A Privacy-Preserving Energy Theft Detection Model for Effective Demand-Response Management in Smart Grids

Authors: Arwa Alromih, John A. Clark, Prosanta Gope

Abstract: The detection of energy thefts is vital for the safety of the whole smart grid system. However, the detection alone is not enough since energy thefts can crucially affect the electricity supply leading to some blackouts. Moreover, privacy is one of the major challenges that must be preserved when dealing with clients' energy data. This is often overlooked in energy theft detection research as most… ▽ More The detection of energy thefts is vital for the safety of the whole smart grid system. However, the detection alone is not enough since energy thefts can crucially affect the electricity supply leading to some blackouts. Moreover, privacy is one of the major challenges that must be preserved when dealing with clients' energy data. This is often overlooked in energy theft detection research as most current detection techniques rely on raw, unencrypted data, which may potentially expose sensitive and personal data. To solve this issue, we present a privacy-preserving energy theft detection technique with effective demand management that employs two layers of privacy protection. We explore a split learning mechanism that trains a detection model in a decentralised fashion without the need to exchange raw data. We also employ a second layer of privacy by the use of a masking scheme to mask clients' outputs in order to prevent inference attacks. A privacy-enhanced version of this mechanism also employs an additional layer of privacy protection by training a randomisation layer at the end of the client-side model. This is done to make the output as random as possible without compromising the detection performance. For the energy theft detection part, we design a multi-output machine learning model to identify energy thefts, estimate their volume, and effectively predict future demand. Finally, we use a comprehensive set of experiments to test our proposed scheme. The experimental results show that our scheme achieves high detection accuracy and greatly improves the privacy preservation degree. △ Less

Submitted 23 March, 2023; originally announced March 2023.

Online Dynamic Reliability Evaluation of Wind Turbines based on Drone-assisted Monitoring

Authors: Sohag Kabir, Koorosh Aslansefat, Prosanta Gope, Felician Campean, Yiannis Papadopoulos

Abstract: The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. C… ▽ More The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach. △ Less

Submitted 23 November, 2022; originally announced November 2022.

Comments: A modified version of this work has been published in the 2022 International Conference on Computing, Electronics & Communications Engineering (iCCECE). This work is a draft author version

E-Tenon: An Efficient Privacy-Preserving Secure Open Data Sharing Scheme for EHR System

Authors: Zhihui Lin, Prosanta Gope, Jianting Ning, Biplab Sikdar

Abstract: The transition from paper-based information to Electronic-Health-Records (EHRs) has driven various advancements in the modern healthcare-industry. In many cases, patients need to share their EHR with healthcare professionals. Given the sensitive and security-critical nature of EHRs, it is essential to consider the security and privacy issues of storing and sharing EHR. However, existing security s… ▽ More The transition from paper-based information to Electronic-Health-Records (EHRs) has driven various advancements in the modern healthcare-industry. In many cases, patients need to share their EHR with healthcare professionals. Given the sensitive and security-critical nature of EHRs, it is essential to consider the security and privacy issues of storing and sharing EHR. However, existing security solutions excessively encrypt the whole database, thus requiring the entire database to be decrypted for each access request, which is a time-consuming process. On the other hand, the use of EHR for medical research (e.g., development of precision-medicine, diagnostics-techniques), as well as optimisation of practices in healthcare organisations, requires the EHR to be analysed, and for that, they should be easily accessible without compromising the privacy of the patient. In this paper, we propose an efficient technique called E-Tenon that not only securely keeps all EHR publicly accessible but also provides the desirable security features. To the best of our knowledge, this is the first work in which an Open Database is used for protecting EHR. The proposed E-Tenon empowers patients to securely share their EHR under multi-level, fine-grained access policies defined by themselves. Analyses show that our system outperforms existing solutions in terms of computational-complexity. △ Less

Submitted 12 July, 2022; originally announced July 2022.

PUF-Phenotype: A Robust and Noise-Resilient Approach to Aid Intra-Group-based Authentication with DRAM-PUFs Using Machine Learning

Authors: Owen Millwood, Jack Miskelly, Bohao Yang, Prosanta Gope, Elif Kavun, Chenghua Lin

Abstract: As the demand for highly secure and dependable lightweight systems increases in the modern world, Physically Unclonable Functions (PUFs) continue to promise a lightweight alternative to high-cost encryption techniques and secure key storage. While the security features promised by PUFs are highly attractive for secure system designers, they have been shown to be vulnerable to various sophisticated… ▽ More As the demand for highly secure and dependable lightweight systems increases in the modern world, Physically Unclonable Functions (PUFs) continue to promise a lightweight alternative to high-cost encryption techniques and secure key storage. While the security features promised by PUFs are highly attractive for secure system designers, they have been shown to be vulnerable to various sophisticated attacks - most notably Machine Learning (ML) based modelling attacks (ML-MA) which attempt to digitally clone the PUF behaviour and thus undermine their security. More recent ML-MA have even exploited publicly known helper data required for PUF error correction in order to predict PUF responses without requiring knowledge of response data. In response to this, research is beginning to emerge regarding the authentication of PUF devices with the assistance of ML as opposed to traditional PUF techniques of storage and comparison of pre-known Challenge-Response pairs (CRPs). In this article, we propose a classification system using ML based on a novel `PUF-Phenotype' concept to accurately identify the origin and determine the validity of noisy memory derived (DRAM) PUF responses as an alternative to helper data-reliant denoising techniques. To our best knowledge, we are the first to perform classification over multiple devices per model to enable a group-based PUF authentication scheme. We achieve up to 98\% classification accuracy using a modified deep convolutional neural network (CNN) for feature extraction in conjunction with several well-established classifiers. We also experimentally verified the performance of our model on a Raspberry Pi device to determine the suitability of deploying our proposed model in a resource-constrained environment. △ Less

Submitted 11 July, 2022; originally announced July 2022.

Comments: 13 pages main text, 7 pages supplementary material (total 20 pages), 8 figures, submitted to IEEE Transactions on Information Forensics and Security

Decentralized Identifier-based Privacy-preserving Authenticated Key Exchange Protocol for Electric Vehicle Charging in Smart Grid

Authors: Rohini Poolat Parameswarath, Prosanta Gope, Biplab Sikdar

Abstract: The popularity of Electric Vehicles (EVs) has been rising across the globe in recent years. Smart grids will be the backbone for EV charging and enable efficient consumption of electricity by the EVs. With the demand for EVs, associated cyber threats are also increasing. Users expose their personal information while charging their EVs, leading to privacy threats. This paper proposes a user-empower… ▽ More The popularity of Electric Vehicles (EVs) has been rising across the globe in recent years. Smart grids will be the backbone for EV charging and enable efficient consumption of electricity by the EVs. With the demand for EVs, associated cyber threats are also increasing. Users expose their personal information while charging their EVs, leading to privacy threats. This paper proposes a user-empowered, privacy-aware authenticated key exchange protocol for EV charging in smart grid. The proposed protocol is based on the concept of Decentralized Identifier (DID) and Verifiable Credentials (VCs). The use of DIDs empowers users by helping them to have complete control over their identities. The charging station and the user verify that the other party is legitimate before proceeding with the charging services using VC. Key recovery is another issue we address in this paper. A method to recover lost keys is incorporated into the proposed protocol. We present formal security proof and informal analysis to show that protocol's robustness against several attacks. We also provide a detailed performance analysis to show that the proposed protocol is efficient. △ Less

Submitted 27 June, 2022; originally announced June 2022.

Comments: 13 pages

Privacy-aware Secure Region-based Handover for Small Cell Networks in 5G-enabled Mobile Communication

Authors: Rabiah Alnashwan, Prosanta Gope, Benjamin Dowling

Abstract: The 5G mobile communication network provides seamless communications between users and service providers and promises to achieve several stringent requirements, such as seamless mobility and massive connectivity. Although 5G can offer numerous benefits, security and privacy issues still need to be addressed. For example, the inclusion of small cell networks (SCN) into 5G brings the network closer… ▽ More The 5G mobile communication network provides seamless communications between users and service providers and promises to achieve several stringent requirements, such as seamless mobility and massive connectivity. Although 5G can offer numerous benefits, security and privacy issues still need to be addressed. For example, the inclusion of small cell networks (SCN) into 5G brings the network closer to the connected users, providing a better quality of services (QoS), resulting in a significant increase in the number of Handover procedures (HO), which will affect the security, latency and efficiency of the network. It is then crucial to design a scheme that supports seamless handovers through secure authentication to avoid the consequences of SCN. To address this issue, this article proposes a secure region-based handover scheme with user anonymity and an efficient revocation mechanism that supports seamless connectivity for SCNs in 5G. In this context, we introduce three privacy-preserving authentication protocols, i.e., initial authentication protocol, intra-region handover protocol and inter-region handover protocol, for dealing with three communication scenarios. To the best of our knowledge, this is the first paper to consider the privacy and security in both the intra-region and inter-region handover scenarios in 5G communication. Detailed security and performance analysis of our proposed scheme is presented to show that it is resilient against many security threats, is cost-effective in computation and provides an efficient solution for the 5G enabled mobile communication. △ Less

Submitted 20 June, 2022; originally announced June 2022.

Adaptive Hybrid Heterogeneous IDS for 6LoWPAN

Authors: Aryan Mohammadi Pasikhani, John A Clark, Prosanta Gope

Abstract: IPv6 over Low-powered Wireless Personal Area Networks (6LoWPAN) have grown in importance in recent years, with the Routing Protocol for Low Power and Lossy Networks (RPL) emerging as a major enabler. However, RPL can be subject to attack, with severe consequences. Most proposed IDSs have been limited to specific RPL attacks and typically assume a stationary environment. In this article, we propose… ▽ More IPv6 over Low-powered Wireless Personal Area Networks (6LoWPAN) have grown in importance in recent years, with the Routing Protocol for Low Power and Lossy Networks (RPL) emerging as a major enabler. However, RPL can be subject to attack, with severe consequences. Most proposed IDSs have been limited to specific RPL attacks and typically assume a stationary environment. In this article, we propose the first adaptive hybrid IDS to efficiently detect and identify a wide range of RPL attacks (including DIO Suppression, Increase Rank, and Worst Parent attacks, which have been overlooked in the literature) in evolving data environments. We apply our framework to networks under various levels of node mobility and maliciousness. We experiment with several incremental machine learning (ML) approaches and various 'concept-drift detection' mechanisms (e.g. ADWIN, DDM, and EDDM) to determine the best underlying settings for the proposed scheme. △ Less

Submitted 18 May, 2022; originally announced May 2022.