Charging Ahead: A Hierarchical Adversarial Framework for Counteracting Advanced Cyber Threats in EV Charging Stations

• URL: http://arxiv.org/abs/2407.03729v1
• Date: Thu, 4 Jul 2024 08:23:03 GMT
• Title: Charging Ahead: A Hierarchical Adversarial Framework for Counteracting Advanced Cyber Threats in EV Charging Stations
• Authors: Mohammed Al-Mehdhar, Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha,
• Abstract summary: Electric vehicles (EVs) provide false information to gain higher charging priority, potentially causing grid instability. This paper introduces a hierarchical adversarial framework using DRL (HADRL), which effectively detects stealthy cyberattacks on EV charging stations.
• Score: 1.873794246359724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing popularity of electric vehicles (EVs) necessitates robust defenses against sophisticated cyber threats. A significant challenge arises when EVs intentionally provide false information to gain higher charging priority, potentially causing grid instability. While various approaches have been proposed in existing literature to address this issue, they often overlook the possibility of attackers using advanced techniques like deep reinforcement learning (DRL) or other complex deep learning methods to achieve such attacks. In response to this, this paper introduces a hierarchical adversarial framework using DRL (HADRL), which effectively detects stealthy cyberattacks on EV charging stations, especially those leading to denial of charging. Our approach includes a dual approach, where the first scheme leverages DRL to develop advanced and stealthy attack methods that can bypass basic intrusion detection systems (IDS). Second, we implement a DRL-based scheme within the IDS at EV charging stations, aiming to detect and counter these sophisticated attacks. This scheme is trained with datasets created from the first scheme, resulting in a robust and efficient IDS. We evaluated the effectiveness of our framework against the recent literature approaches, and the results show that our IDS can accurately detect deceptive EVs with a low false alarm rate, even when confronted with attacks not represented in the training dataset.

Related papers

• Defense against Joint Poison and Evasion Attacks: A Case Study of DERMS [2.632261166782093]
  We propose the first framework of IDS that is robust against joint poisoning and evasion attacks. We verify the robustness of our method on the IEEE-13 bus feeder model against a diverse set of poisoning and evasion attack scenarios.
  arXiv  Detail & Related papers  (2024-05-05T16:24:30Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats [3.560574387648533]
  We propose a one-class classification-driven IDS system structured on two tiers. The first tier distinguishes between normal activities and attacks/threats, while the second tier determines if the detected attack is known or unknown. This model not only identifies unseen attacks but also uses them for retraining them by clustering unseen attacks.
  arXiv  Detail & Related papers  (2024-03-17T12:26:30Z)
• usfAD Based Effective Unknown Attack Detection Focused IDS Framework [3.560574387648533]
  Internet of Things (IoT) and Industrial Internet of Things (IIoT) have led to an increasing range of cyber threats. For more than a decade, researchers have delved into supervised machine learning techniques to develop Intrusion Detection System (IDS) IDS trained and tested on known datasets fails in detecting zero-day or unknown attacks. We propose two strategies for semi-supervised learning based IDS where training samples of attacks are not required.
  arXiv  Detail & Related papers  (2024-03-17T11:49:57Z)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training [62.77129284830945]
  This paper considers a regression problem in a wireless setting and shows that adversarial attacks can break the DL-based approach. We also analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly.
  arXiv  Detail & Related papers  (2022-06-14T04:55:11Z)
• Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning [4.640835690336653]
  Adversarial attacks on Uncrewed Aerial Vehicle (UAV) agents operating in public are increasing. Deep Learning (DL) approaches to control and guide these UAVs can be beneficial in terms of performance but can add concerns regarding the safety of those techniques and their vulnerability against adversarial attacks. This paper proposes an innovative approach based on the explainability of DL methods to build an efficient detector that will protect these DL schemes and the UAVs adopting them from attacks.
  arXiv  Detail & Related papers  (2022-06-06T15:16:10Z)
• Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training [71.7750435554693]
  We show that several state-of-the-art RL agents proposed for power system control are vulnerable to adversarial attacks. Specifically, we use an adversary Markov Decision Process to learn an attack policy, and demonstrate the potency of our attack. We propose to use adversarial training to increase the robustness of RL agent against attacks and avoid infeasible operational decisions.
  arXiv  Detail & Related papers  (2021-10-18T00:50:34Z)
• Adversarial defense for automatic speaker verification by cascaded self-supervised learning models [101.42920161993455]
  More and more malicious attackers attempt to launch adversarial attacks at automatic speaker verification (ASV) systems. We propose a standard and attack-agnostic method based on cascaded self-supervised learning models to purify the adversarial perturbations. Experimental results demonstrate that the proposed method achieves effective defense performance and can successfully counter adversarial attacks.
  arXiv  Detail & Related papers  (2021-02-14T01:56:43Z)
• Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning [48.49658986576776]
  Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications. This paper presents emerging attacks in DRL-based systems and the potential countermeasures to defend against these attacks.
  arXiv  Detail & Related papers  (2020-01-27T10:53:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.