ETGuard: Malicious Encrypted Traffic Detection in Blockchain-based Power Grid Systems

• URL: http://arxiv.org/abs/2408.10657v1
• Date: Tue, 20 Aug 2024 08:53:42 GMT
• Title: ETGuard: Malicious Encrypted Traffic Detection in Blockchain-based Power Grid Systems
• Authors: Peng Zhou, Yongdong Liu, Lixun Ma, Weiye Zhang, Haohan Tan, Zhenguang Liu, Butian Huang,
• Abstract summary: We present a novel framework that is able to automatically detect malicious encrypted traffic in blockchain-based power grid systems. We mathematically derive incremental learning losses to resist the forgetting of old attack patterns. Our method achieves state-of-the-art performance on three different benchmark datasets.
• Score: 11.476439383002829
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The escalating prevalence of encryption protocols has led to a concomitant surge in the number of malicious attacks that hide in encrypted traffic. Power grid systems, as fundamental infrastructure, are becoming prime targets for such attacks. Conventional methods for detecting malicious encrypted packets typically use a static pre-trained model. We observe that these methods are not well-suited for blockchain-based power grid systems. More critically, they fall short in dynamic environments where new types of encrypted attacks continuously emerge. Motivated by this, in this paper we try to tackle these challenges from two aspects: (1) We present a novel framework that is able to automatically detect malicious encrypted traffic in blockchain-based power grid systems and incrementally learn from new malicious traffic. (2) We mathematically derive incremental learning losses to resist the forgetting of old attack patterns while ensuring the model is capable of handling new encrypted attack patterns. Empirically, our method achieves state-of-the-art performance on three different benchmark datasets. We also constructed the first malicious encrypted traffic dataset for blockchain-based power grid scenario. Our code and dataset are available at https://github.com/PPPmzt/ETGuard, hoping to inspire future research.

Related papers

• Securing Proof of Stake Blockchains: Leveraging Multi-Agent Reinforcement Learning for Detecting and Mitigating Malicious Nodes [0.2982610402087727]
  MRL-PoS+ is a novel consensus algorithm to enhance the security of PoS blockchains. We show that MRL-PoS+ significantly improves the attack resilience of PoS blockchains.
  arXiv  Detail & Related papers  (2024-07-30T17:18:03Z)
• The Latency Price of Threshold Cryptosystem in Blockchains [52.359230560289745]
  We study the interplay between threshold cryptography and a class of blockchains that use Byzantine-fault tolerant (BFT) consensus protocols. Existing approaches for threshold cryptosystems introduce a latency overhead of at least one message delay for running the threshold cryptographic protocol. We propose a mechanism to eliminate this overhead for blockchain-native threshold cryptosystems with tight thresholds.
  arXiv  Detail & Related papers  (2024-07-16T20:53:04Z)
• Understanding crypter-as-a-service in a popular underground marketplace [51.328567400947435]
  Crypters are pieces of software whose main goal is to transform a target binary so it can avoid detection from Anti Viruses (AVs) applications. The crypter-as-a-service model has gained popularity, in response to the increased sophistication of detection mechanisms. This paper provides the first study on an online underground market dedicated to crypter-as-a-service.
  arXiv  Detail & Related papers  (2024-05-20T08:35:39Z)
• Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study [2.5325901958283126]
  This paper proposes a novel 3-stage intrusion detection system inspired by a simplified version of the Lockheed Martin cyber kill chain. The proposed approach consists of three models, each responsible for detecting a group of attacks with common characteristics. Using the ToN IoT dataset, we achieved an average of 94% F1-Score among different stages, outperforming the benchmark approaches.
  arXiv  Detail & Related papers  (2024-04-28T22:11:24Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• Secure Routing Protocol To Mitigate Attacks By Using Blockchain Technology In Manet [0.0]
  MANET is an infrastructure-less network with a changeable topology. Malicious network nodes are the source of network-based attacks. This article explains the many sorts of assaults, their impact on MANET, and the MANET-based defence measures that are currently in place.
  arXiv  Detail & Related papers  (2023-04-09T15:19:51Z)
• Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Study [6.267890584151111]
  In post-COVID-19 environment, malicious traffic encryption is growing rapidly. We formulate a universal framework of machine learning based encrypted malicious traffic detection techniques. We implement and compare 10 encrypted malicious traffic detection algorithms.
  arXiv  Detail & Related papers  (2022-03-17T14:00:55Z)
• Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing [65.32148145602865]
  deep hashing networks are vulnerable to adversarial examples. We propose a novel prototype-supervised adversarial network (ProS-GAN) To the best of our knowledge, this is the first generation-based method to attack deep hashing networks.
  arXiv  Detail & Related papers  (2021-05-17T00:31:37Z)
• Online Adversarial Attacks [57.448101834579624]
  We formalize the online adversarial attack problem, emphasizing two key elements found in real-world use-cases. We first rigorously analyze a deterministic variant of the online threat model. We then propose algoname, a simple yet practical algorithm yielding a provably better competitive ratio for $k=2$ over the current best single threshold algorithm.
  arXiv  Detail & Related papers  (2021-03-02T20:36:04Z)
• An Empirical Review of Adversarial Defenses [0.913755431537592]
  Deep neural networks, which form the basis of such systems, are highly susceptible to a specific type of attack, called adversarial attacks. A hacker can, even with bare minimum computation, generate adversarial examples (images or data points that belong to another class, but consistently fool the model to get misclassified as genuine) and crumble the basis of such algorithms. We show two effective techniques, namely Dropout and Denoising Autoencoders, and show their success in preventing such attacks from fooling the model.
  arXiv  Detail & Related papers  (2020-12-10T09:34:41Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)

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.