Auditable Machine Unlearning for Privacy-Compliant Ransomware Detection Using Multi-Shard SISA and Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2607.06860v1
• Date: Tue, 07 Jul 2026 23:23:52 GMT
• Title: Auditable Machine Unlearning for Privacy-Compliant Ransomware Detection Using Multi-Shard SISA and Deep Reinforcement Learning
• Abstract summary: We propose an auditable ransomware detection and unlearning framework that integrates deep reinforcement learning with multi-shard SISA retraining.<n>In the proposed system, a Double Deep Q-Network (DDQN) learns a reward-guided detection policy from behavioral features under asymmetric security costs.<n>We show that single-shard unlearning maintains minimal utility degradation and low oracle disagreement, whereas moderate shard counts provide the best efficiency-performance trade-off.
• Score: 1.8024397171920885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ransomware poses an escalating cybersecurity threat as attackers continuously modify behavioral patterns to evade static defenses. Although existing machine learning-based detectors often achieve strong predictive performance, they generally assume fixed training data and do not support the selective removal of previously learned samples. This limitation conflicts with privacy regulations such as the GDPR and CCPA, which require the removal of sensitive user data upon request. To address this challenge, we propose an auditable ransomware detection and unlearning framework that integrates deep reinforcement learning with multi-shard SISA retraining. In the proposed system, a Double Deep Q-Network (DDQN) learns a reward-guided detection policy from behavioral features under asymmetric security costs, while multi-shard SISA enables privacy-compliant selective sample removal through shard-level retraining. The framework was evaluated using four criteria: utility preservation, oracle-based forgetting validation, membership inference auditing, and computational efficiency. On a balanced Windows 11 behavioral dataset comprising 2,000 samples and 103 features, the baseline DDQN detector achieved an F1 score of 0.9925 and an AUC of 0.9983. The experimental results show that single-shard unlearning maintains minimal utility degradation and low oracle disagreement, whereas moderate shard counts (M = 5-10) provide the best efficiency-performance trade-off, reducing retraining time to 5-30 s compared with 80-330 s for full retraining. In addition, the membership inference scores remain close to 0.5 across most configurations, indicating limited privacy leakage after unlearning. These findings demonstrate that a privacy-compliant ransomware detection framework can jointly achieve high detection performance, auditable deletion verification, and efficient sample removal.

Related papers

• Cybersecurity Detection Classification with Reasoning-enabled Language Models [50.72675435043546]
  A major issue in Security Operations Centers (SOCs) is alert fatigue.<n>Prior work prompts or fine-tunes large language models (LLMs) to emit a triage label directly, but does not train them to reason about whether a detection is a genuine threat.<n>We train a chain-of-thought (CoT) reasoning-enabled triage classifier on real, human-labeled Windows endpoint detections.
  arXiv  Detail & Related papers  (2026-07-30T16:22:13Z)
• SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design [1.8024397171920885]
  This study proposes a Security-Aware Deep Reinforcement Learning framework that embeds false-negative and false-positive cost asymmetry into the reinforcement learning reward signal.<n>Four deep reinforcement learning agents were evaluated using a symmetric baseline reward (R1) and a security-aware asymmetric reward (R2)<n>Results show that asymmetric reward shaping improves security-oriented detection performance.
  arXiv  Detail & Related papers  (2026-07-08T00:43:20Z)
• When Certificates Fail: A Unified Safety Framework for Embedded Neural Interface Models [0.0]
  We propose a unified empirical audit framework organized around three failures: verification insufficiency, proxy-fidelity divergence, and latent information exfiltration.<n>We instantiate the framework on BCI Competition IV 2a and SEED-IV using multiple deep and classical EEG decoders, official session-level validation, null controls, and paired statistical tests.<n>Our results establish that operational safety auditing, not certificate verification alone, is necessary for responsible neural-interface deployment.
  arXiv  Detail & Related papers  (2026-07-07T13:03:45Z)
• Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing [8.293465530619246]
  We propose a two-stage decoupled evaluation framework that separates exploit execution from reconnaissance.<n>We empirically evaluate five open-source penetration-testing agents, covering multiagent, monolithic, and graph-driven architectures.
  arXiv  Detail & Related papers  (2026-06-24T02:51:58Z)
• TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models [76.92890872338235]
  Large-scale pre-trained Vision-Language models (VLMs) exhibit strong zero-shot generalization, yet are highly vulnerable to imperceptible adversarial perturbations.<n>To enhance robustness without requiring downstream task-specific retraining, we propose TAME, a novel test-time defense.
  arXiv  Detail & Related papers  (2026-05-17T18:07:08Z)
• Privacy-Aware Machine Unlearning with SISA for Reinforcement Learning-Based Ransomware Detection [1.8024397171920885]
  We present a privacy-aware machine unlearning evaluation framework for ransomware detection built on Sharded, Isolated, Sliced, and Aggregated training.<n>The framework enables efficient data deletion by retraining only the affected model shards rather than the entire detector.
  arXiv  Detail & Related papers  (2026-04-18T00:19:20Z)
• Robust Federated Learning for Malicious Clients using Loss Trend Deviation Detection [0.0]
  Federated Learning (FL) facilitates collaborative model training among distributed clients while ensuring that raw data remains on local devices.<n>Such clients can interfere with the training process by sending misleading updates, which can negatively affect the performance and reliability of the global model.<n>We propose the Federated Learning with Loss Trend Detection (FL-LTD), a lightweight and privacy-preserving defense framework that detects and mitigates malicious behavior by monitoring temporal loss dynamics rather than model gradients.
  arXiv  Detail & Related papers  (2026-01-28T18:09:53Z)
• A Granular Study of Safety Pretraining under Model Abliteration [64.24346997570275]
  We study model abliteration, a lightweight projection technique designed to remove refusal-sensitive directions.<n>We issue 100 prompts with balanced harmful and harmless cases, classify responses as **Refusal** or **Non-Refusal** using multiple judges, and validate judge fidelity.<n>Our study produces a checkpoint-level characterization of which data-centric safety components remain robust under abliteration.
  arXiv  Detail & Related papers  (2025-10-03T07:01:45Z)
• Lie Detector: Unified Backdoor Detection via Cross-Examination Framework [68.45399098884364]
  We propose a unified backdoor detection framework in the semi-honest setting.<n>Our method achieves superior detection performance, improving accuracy by 5.4%, 1.6%, and 11.9% over SoTA baselines.<n> Notably, it is the first to effectively detect backdoors in multimodal large language models.
  arXiv  Detail & Related papers  (2025-03-21T06:12:06Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation [69.75513501757628]
  FedGT is a novel framework for identifying malicious clients in federated learning with secure aggregation. We show that FedGT significantly outperforms the private robust aggregation approach based on the geometric median recently proposed by Pillutla et al.
  arXiv  Detail & Related papers  (2023-05-09T14:54:59Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)

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.