The suggested DP-FAL model is a privacy-conserving, scalable, and robust intrusion prevention system that can be used real-time V2X conditions and has the potential to be deployed safely, reliably, and sustainably in next-generation transportation systems.
Abstract
Vehicular networks are a continuation of vehicle-to-everything (V2X) communication, which is becoming the foundation of intelligent transportation systems and allows vehicles, roadside units, and grid infrastructure to communicate reliably. Nevertheless, the decentralized aspect of V2X renders it very susceptible to adversarial cyberattacks, both on a large scale such as DDoS attacks, spoofing, and Sybil attacks. Conventional centralized intrusion detection systems (IDSs) have limitations in terms of latency, bandwidth overhead, and privacy risks. In this study, we introduce a Differentially Private Federated Adversarial Learning (DP-FAL) model that integrates federated learning to train decentralized models with adversarial defence schemes and differential privacy tools to address gradient leakage. The proposed DP-FAL framework achieves a detection accuracy of 94.2% and a communication overhead reduction of up to 23%. This shows that it can be used in bandwidth-limited and latency-sensitive V2X systems because of its ability to ensure high detection reliability with low communication costs. The suggested DP-FAL model is a privacy-conserving, scalable, and robust intrusion prevention system that can be used real-time V2X conditions. These results indicate that DP-FAL has the potential to be deployed safely, reliably, and sustainably in next-generation transportation systems.
This review underscores the potential of FL to become a foundational technology in next-generation cybersecurity systems, enabling scalable and privacy-preserving threat mitigation across distributed infrastructures.
The proliferation of Internet of Things (IoT) devices has amplified the attack surface for large-scale cyber threats, with rapidly evolving malware families such as Mirai posing significant detection challenges. Existing side-channel and host-based approaches are limited by poor generalization to unseen variants and the absence of robust privacy protections during training. In this paper, we propose SecShield, a federated deep learning framework for privacy-preserving malware detection in resource-constrained IoT environments. SecShield employs a three-party secure computation model, where clients locally train a shared deep neural network on power side-channel traces and transmit only noisy parameter updates to a central server via an aggregator, preventing raw data exposure. Differential privacy is incorporated by injecting calibrated noise into gradients, mitigating inference risks such as data reconstruction and poisoning attacks. This decentralized architecture enables continuous adaptation to zero-day malware, reduces communication overhead, and ensures robustness against adversarial manipulation. Experimental evaluations on IoT power trace datasets demonstrate that SecShield achieves high detection accuracy over 98% while preserving client data confidentiality and maintaining computational efficiency.
Damodar Dhital, Sabir Ahmed Khan, Almustapha A. Wakili et al.· International Conference on...· 0 citations
As vehicular networks move toward 5G/6G edge intelligence, federated learning (FL) is widely promoted as a privacy-preserving way for vehicles and infrastructure to train shared models without exposing raw sensor data. Yet the updates clients transmit still leak enough information to identify who sent them, which threatens the anonymity that safety-critical V2X applications assume and adds to existing concerns over adversarial ML, model poisoning, and backdoor attacks. We study server-side client identity inference from transmitted weight deltas using inertial (IMU) measurements, evaluated on the UCI Human Activity Recognition (HAR) benchmark as an accessible proxy for the IMU streams produced onboard connected vehicles. Across five attack classifiers and five non-IID partitions, an honest-but-curious server recovers client identity with near-perfect accuracy (approximately 1.000) from undefended updates, confirming a concrete identifiability risk. We then quantify the privacy-utility trade-off of a lightweight clip-then-noise defense by sweeping Gaussian noise (sigma in {0.00, 0.05, 0.10, 0.20, 0.50, 1.00}) at fixed clipping (C=1.0), and report formal (epsilon, delta)-DP budgets through Renyi accounting. A practical region (sigma in [0.1, 0.2]) drives attack accuracy to near-random while costing under 5% relative FL accuracy. Ensemble FL supplies complementary structural privacy with a 1/K anonymity-set bound and no noise penalty. Results are supported by cryptographic (SHA-256) train/evaluation gradient disjointness, three seeds, and a count-normalized attacker-advantage metric. We position HAR explicitly as a proxy and discuss what validation on true vehicular telemetry would require.
Ali Akarma, Toqeer Ali Syed, Muhammad Khan et al.· 0 citations
Vehicle-to-Everything (V2X) communication enables vehicles to exchange safety-critical messages, but its reliance on temporary pseudonymous identities makes it vulnerable to Sybil attacks, where a single attacker fabricates multiple identities to inject false information into the network. This paper presents a lightweight cryptographic mechanism that combines SHA-256 Proof-of-Work token mining with a time-windowed ratecontrol layer to limit the number of new identities a vehicle can activate within a given interval. The mechanism was implemented in Java and evaluated through a parametric simulation across three independent variables: PoW difficulty, rate limit, and attack intensity. Results show that a difficulty of 4 and a rate limit of 5 tokens per 10-second window provide an effective balance between Sybil resistance and legitimate vehicle access in an 8-vehicle scenario, with an average mining time of 0.227 seconds. The evaluation identifies the rate-control layer as the primary security mechanism, while PoW difficulty increases the computational cost per identity without independently capping accepted tokens. The proposed approach is infrastructure-free and suitable for regulated V2X deployments where attackers represent a minority of the network.
Maher Fayyad, Abdullah Awad, Edison Pignaton De Freitas et al.· International Conference on...· 0 citations
This research proposes a novel framework for anomaly detection in WSNs that leverages federated deep learning and prioritizes real-time adaptation and data privacy, and offers a promising path forward for securing WSNs by enabling distributed, privacy-preserving anomaly detection with real-time adaptation capabilities.
N. Karthick, K. R. Singh· International journal of com...· 0 citations
With the proliferation of Internet of Things (IoT), critical infrastructures such as smart cities, industrial automation, precision healthcare, and intelligent transportation have seen a significant shift in their security landscape. As these critical infrastructures are increasingly depending on OneM2M standards for interoperability and scalable service management, they are simultaneously presenting a larger and more structured attack surface. One of the most serious and realistic threats in such scenarios is black-box adversarial attacks, in which an attacker can compromise Machine Learning (ML) and Deep Learning (DL)-based intrusion detection systems (IDS) without having prior knowledge of the ML/DL model's architecture, parameters, and training data. To mitigate such a critical challenge, we propose a multi-stage framework specific to OneM2M-based IoT networks that can effectively counter transfer-based and query-based black-box adversarial attacks. Our framework's effectiveness will be tested on a dataset of 1.25 million labeled network flows collected from Azure IoT Hub-based IoT devices over a 10-day period. We will test the robustness of our framework against three adversarial attacks: FGSM with $\varepsilon=0.05$, PGD with 40 iterations and step size 0.01, and C&W with L2 optimization and 1,000 iterations. The proposed pipeline consists of four different mechanisms: (1) adversarial training with the use of mixed clean and adversarial samples, (2) traffic sanitization to minimize malicious and/or suspicious traffic flows before processing by the Common Service Entity (CSE), (3) the use of a black-box adversarial detector based on feature transformation, and (4) the use of an ensemble-based IDS to make use of the results of the Random Forest, XGBoost, MLP, and 1D-CNN-based IDSs via majority voting. The experimental results show that the proposed Ensemble + multi-defense configuration results in 98.1% accuracy, 98.3% TPR, and 1.7% FNR. Most notably, the proposed Ensemble + multi-defense configuration results in an ASR of 11.2%, which is down by 86% compared to the 82.3% ASR achieved by the baseline IDS. Notably, the proposed framework results in limited overheads in terms of RTT (increased by 8 ms), CPU (increased by 17%), and RAM (increased by 1.6 GB).
Hamza Jamiri, A. Zyane· International Conference on...· 0 citations
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