PsybORG+: Modeling and Simulation for Detecting Cognitive Biases in Advanced Persistent Threats

• URL: http://arxiv.org/abs/2408.01310v3
• Date: Tue, 13 Aug 2024 14:00:25 GMT
• Title: PsybORG+: Modeling and Simulation for Detecting Cognitive Biases in Advanced Persistent Threats
• Authors: Shuo Huang, Fred Jones, Nikolos Gurney, David Pynadath, Kunal Srivastava, Stoney Trent, Peggy Wu, Quanyan Zhu,
• Abstract summary: This work introduces PsybORG$+$, a multi-agent cybersecurity simulation environment designed to model APT behaviors influenced by cognitive vulnerabilities. A classification model is built for cognitive vulnerability inference and a simulator is designed for synthetic data generation. Results show that PsybORG$+$ can effectively model APT attackers with different loss aversion and confirmation bias levels.
• Score: 10.161416622040722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advanced Persistent Threats (APTs) bring significant challenges to cybersecurity due to their sophisticated and stealthy nature. Traditional cybersecurity measures fail to defend against APTs. Cognitive vulnerabilities can significantly influence attackers' decision-making processes, which presents an opportunity for defenders to exploit. This work introduces PsybORG$^+$, a multi-agent cybersecurity simulation environment designed to model APT behaviors influenced by cognitive vulnerabilities. A classification model is built for cognitive vulnerability inference and a simulator is designed for synthetic data generation. Results show that PsybORG$^+$ can effectively model APT attackers with different loss aversion and confirmation bias levels. The classification model has at least a 0.83 accuracy rate in predicting cognitive vulnerabilities.

Related papers

• CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models [8.236058439213473]
  Concept Bottleneck Models (CBMs) have emerged as a key approach to improve interpretability by leveraging high-level semantic information. CBMs are susceptible to security threats, particularly backdoor attacks, which can covertly manipulate model behaviors. We introduce CAT (Concept-level Backdoor ATtacks), a methodology that leverages the conceptual representations within CBMs to embed triggers during training. An enhanced attack pattern, CAT+, incorporates a correlation function to systematically select the most effective and stealthy concept triggers.
  arXiv  Detail & Related papers  (2024-10-07T08:14:17Z)
• Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense [3.685395311534351]
  Federated Learning (FL) is a distributed machine learning diagram that enables multiple clients to collaboratively train a global model without sharing their private local data. FL systems are vulnerable to attacks that are happening in malicious clients through data poisoning and model poisoning. Existing defense methods typically focus on mitigating specific types of poisoning and are often ineffective against unseen types of attack.
  arXiv  Detail & Related papers  (2024-08-05T20:27:45Z)
• The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks [90.52808174102157]
  In safety-critical applications such as medical imaging and autonomous driving, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks. A notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. This study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks.
  arXiv  Detail & Related papers  (2024-05-14T18:05:19Z)
• Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspective [0.0]
  In adversarial machine learning, malicious users try to fool the deep learning model by inserting adversarial perturbation inputs into the model during its training or testing phase. We empirically test the black-box adversarial transferability phenomena in cyber attack detection systems. The results indicate that any deep learning model is highly susceptible to adversarial attacks, even if the attacker does not have access to the internal details of the target model.
  arXiv  Detail & Related papers  (2024-04-15T06:56:28Z)
• Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning [4.475514208635884]
  This study systematically profiles the (in)feasibility of learning for privacy-preserving cyber threat detection in terms of effectiveness, byzantine resilience, and efficiency. It shows that FL-trained detection models can achieve a performance that is comparable to centrally trained counterparts. Under a realistic threat model, FL turns out to be adversary-resistant to attacks of both data poisoning and model poisoning.
  arXiv  Detail & Related papers  (2024-04-08T01:16:56Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks [58.29502185344086]
  In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks. It is important to provide provable guarantees for deep learning models against semantically meaningful input transformations. We propose a new universal probabilistic certification approach based on Chernoff-Cramer bounds.
  arXiv  Detail & Related papers  (2021-09-22T12:46:04Z)
• Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data [3.2458203725405976]
  Adversarial attacks aim at producing adversarial examples, in other words, slightly modified inputs that induce the AI system to return incorrect outputs. In this paper we illustrate a novel approach to modify and adapt state-of-the-art algorithms to imbalanced data, in the context of fraud detection. Experimental results show that the proposed modifications lead to a perfect attack success rate. When applied to a real-world production system, the proposed techniques shows the possibility of posing a serious threat to the robustness of advanced AI-based fraud detection procedures.
  arXiv  Detail & Related papers  (2021-01-20T08:58:29Z)
• Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning [71.17774313301753]
  We explore the robustness of self-supervised learned high-level representations by using them in the defense against adversarial attacks. Experimental results on the ASVspoof 2019 dataset demonstrate that high-level representations extracted by Mockingjay can prevent the transferability of adversarial examples.
  arXiv  Detail & Related papers  (2020-06-05T03:03:06Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.