Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response

• URL: http://arxiv.org/abs/2408.01999v1
• Date: Sun, 4 Aug 2024 11:55:24 GMT
• Title: Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response
• Authors: Dipo Dunsin, Mohamed Chahine Ghanem, Karim Ouazzane, Vassil Vassilev,
• Abstract summary: This research focused on enhancing post-incident malware forensic investigation using reinforcement learning RL. We proposed an advanced MDP post incident malware forensics investigation model and framework to expedite post incident forensics. We then implement our RL Malware Investigation Model based on structured MDP within the proposed framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This research focused on enhancing post-incident malware forensic investigation using reinforcement learning RL. We proposed an advanced MDP post incident malware forensics investigation model and framework to expedite post incident forensics. We then implement our RL Malware Investigation Model based on structured MDP within the proposed framework. To identify malware artefacts, the RL agent acquires and examines forensics evidence files, iteratively improving its capabilities using Q Table and temporal difference learning. The Q learning algorithm significantly improved the agent ability to identify malware. An epsilon greedy exploration strategy and Q learning updates enabled efficient learning and decision making. Our experimental testing revealed that optimal learning rates depend on the MDP environment complexity, with simpler environments benefiting from higher rates for quicker convergence and complex ones requiring lower rates for stability. Our model performance in identifying and classifying malware reduced malware analysis time compared to human experts, demonstrating robustness and adaptability. The study highlighted the significance of hyper parameter tuning and suggested adaptive strategies for complex environments. Our RL based approach produced promising results and is validated as an alternative to traditional methods notably by offering continuous learning and adaptation to new and evolving malware threats which ultimately enhance the post incident forensics investigations.

Related papers

• A Novel Reinforcement Learning Model for Post-Incident Malware Investigations [0.0]
  This Research proposes a Novel Reinforcement Learning model to optimise malware forensics investigation during cyber incident response. It aims to improve forensic investigation efficiency by reducing false negatives and adapting current practices to evolving malware signatures.
  arXiv  Detail & Related papers  (2024-10-19T07:59:10Z)
• Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning [0.0]
  This paper investigates the application of Deep Reinforcement Learning (DRL) for attributing malware to specific Advanced Persistent Threat (APT) groups. By analysing over 3500 malware samples from 12 distinct APT groups, the study utilise sophisticated tools like Cuckoo to extract data. The research that the DRL model significantly outperforms traditional machine learning approaches, achieving an impressive test accuracy of 89.27 %.
  arXiv  Detail & Related papers  (2024-10-15T10:10:33Z)
• Multi-agent Reinforcement Learning-based Network Intrusion Detection System [3.4636217357968904]
  Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks. We propose a novel multi-agent reinforcement learning (RL) architecture, enabling automatic, efficient, and robust network intrusion detection. Our solution introduces a resilient architecture designed to accommodate the addition of new attacks and effectively adapt to changes in existing attack patterns.
  arXiv  Detail & Related papers  (2024-07-08T09:18:59Z)
• Leveraging LSTM and GAN for Modern Malware Detection [0.4799822253865054]
  This paper proposes the utilization of the Deep Learning Model, LSTM networks, and GAN classifiers to amplify malware detection accuracy and speed. The research outcomes come out with 98% accuracy that shows the efficiency of deep learning plays a decisive role in proactive cybersecurity defense.
  arXiv  Detail & Related papers  (2024-05-07T14:57:24Z)
• Analyzing Adversarial Inputs in Deep Reinforcement Learning [53.3760591018817]
  We present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. We introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations.
  arXiv  Detail & Related papers  (2024-02-07T21:58:40Z)
• CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning [56.20123080771364]
  We develop a model-agnostic Contrastive-Curiosity-Driven Learning Framework (CCLF) for reinforcement learning. CCLF fully exploit sample importance and improve learning efficiency in a self-supervised manner. We evaluate this approach on the DeepMind Control Suite, Atari, and MiniGrid benchmarks.
  arXiv  Detail & Related papers  (2022-05-02T14:42:05Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)
• Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning [56.17667147101263]
  In real-world tasks, reinforcement learning agents encounter situations that are not present during training time. To ensure reliable performance, the RL agents need to exhibit robustness against worst-case situations. We propose the Robust Hallucinated Upper-Confidence RL (RH-UCRL) algorithm to provably solve this problem.
  arXiv  Detail & Related papers  (2021-03-18T16:50:17Z)
• Detection of Insider Attacks in Distributed Projected Subgradient Algorithms [11.096339082411882]
  We show that a general neural network is particularly suitable for detecting and localizing malicious agents. We propose to adopt one of the state-of-art approaches in federated learning, i.e., a collaborative peer-to-peer machine learning protocol. In our simulations, a least-squared problem is considered to verify the feasibility and effectiveness of AI-based methods.
  arXiv  Detail & Related papers  (2021-01-18T08:01:06Z)
• Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning [93.1435980666675]
  We show how optimistic exploration can be easily combined with state-of-the-art reinforcement learning algorithms. Our experiments demonstrate that optimistic exploration significantly speeds-up learning when there are penalties on actions.
  arXiv  Detail & Related papers  (2020-06-15T18:37:38Z)

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.