Octopii

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machine_learning_security

Machine Learning and Security Source codes about machine learning and security. Line up. Cyber security and Machine Learning course The elementary training course of Machine learning for security engineer. Vulnerabilities of Machine Learning Summary of Machine Learning vulnerability. Analytics...

DeepTraffic

Deep Learning models for network traffic classification For more information please read our papers. 🎓Wei Wang's Google Scholar Homepage Wei Wang, Xuewen Zeng, Xiaozhou Ye, Yiqiang Sheng and Ming Zhu,"Malware Traffic Classification Using Convolutional Neural Networks for Representation Learning,"...

SEVulDet

SEVulDet SEVulDet is a semantics-enhanced deep learning-based framework that can accurately pinpoint vulnerability patterns by extracting, preserving, and learning more semantics. E-mail for communication: [email protected] Details of SEVulDet Recent years have seen increased attention to de...

eyeballer

Eyeballer Give those screenshots of yours a quick eyeballing. Eyeballer is meant for large-scope network penetration tests where you need to find "interesting" targets from a huge set of web-based hosts. Go ahead and use your favorite screenshotting tool like normal EyeWitness or GoWitness and th...

Sec5GLoc

Sec5GLoc: Securing 5G Indoor Localization via Adversary-Resilient Deep Learning Architecture Our work addresses security and privacy challenges in 5G indoor localization by proposing an adversary-resilient deep learning architecture. Sec5GLoc combines Channel Impulse Response CIR fingerprinting...

MIG-COW: Transferable Adversarial Attacks on Deepfake Detectors Via Gradient Decomposition

Despite recent advances, deepfake detectors remain vulnerable to adversarial examples, particularly in diverse, real-world settings. We propose MIG-COW, a novel adversarial attack framework that generates highly generalizable and visually imperceptible adversarial examples. By combining...

Interflow: Breaking the Offline-Online Barrier for Private AI at the Edge

Privacy-preserving machine learning PPML offers a promising path toward bringing powerful AI services to the edge without exposing sensitive user data. However, its practical deployment remains constrained by the high communication, computation, and data-movement costs of secure multi-party...

Systematically Optimized CNN-Transformer with Focal Loss for Imbalanced Intrusion Detection on NSL-KDD

Intrusion Detection Systems IDS struggle with imbalanced datasets like NSL-KDD, especially in detecting rare R2L and U2R attacks. This work describes a systematically optimized and explainable framework using a CNN-Transformer architecture to improve performance on highly imbalanced data. We...

Adversarial RL for Port-Scan Evasion: Attacker Feature Visibility in Edge-Deployed IDS

Machine learning-based intrusion detection systems IDS are increasingly used in resource-constrained Internet of Things IoT environments, yet their robustness is often evaluated against static attacks rather than adversaries that adapt to detection feedback. This paper investigates adaptive...

Cybersecurity in Edge Computing: A Trust-Aware Federated Hybrid Intrusion Detection Framework

Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Internet of Things IoT devices. While this paradigm decentralizes processes, minimizes latency, and reduces backhaul bandwidth congestion, it exponentiall...

TP-CRIV: A Framework for Third-Party Challenge-Response Identity Verification of AI Models

Artificial intelligence AI models are increasingly deployed through remote services, making model misappropriation a growing concern. Existing approaches, including watermarking, fingerprinting, and model similarity analysis, primarily rely on predefined evidence or direct behavioral comparison a...

Topological Signatures of Cyber-Attack Classes in Natural Visibility Graph Representations of Network Traffic

Natural Visibility Graph NVG-based representations provide a promising approach for capturing structural patterns in sequential network traffic. However, whether different cyber-attack classes exhibit distinctive topological signatures in such representations remains insufficiently understood. Th...

Delphi Scanner: Efficient and Interpretable Static Malware Detection Via API Sequence Modeling

Static malware detection for Windows Portable Executable files demands a careful balance between detection effectiveness, computational efficiency, and analytical interpretability. This paper introduces Delphi Scanner, a static malware detection system for Windows PE files that balances efficienc...

acuity (6.18.0), acuitylite (>=6.6.0, <=6.21.0) +60 more potentially affected by CVE-2022-35985 via tensorflow (2.8.0)

tensorflow PYPI version 2.8.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - acuity 6.18.0 - acuitylite =6.6.0, =1.2.8, =1.0.43, =0.2.2, =0.0.1, =0.0.2, =0.2.8, =0.14.0, =0.1.3, =0.0.9, =0.2.27, =0.2.41 -...

acuity (6.18.0), acuitylite (>=6.6.0, <=6.21.0) +60 more potentially affected by CVE-2022-35965 via tensorflow (2.8.0)

tensorflow PYPI version 2.8.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - acuity 6.18.0 - acuitylite =6.6.0, =1.2.8, =1.0.43, =0.2.2, =0.0.1, =0.0.2, =0.2.8, =0.14.0, =0.1.3, =0.0.9, =0.2.27, =0.2.41 -...

acuity (6.18.0), acuitylite (>=6.6.0, <=6.21.0) +60 more potentially affected by CVE-2022-35963 via tensorflow (2.8.0)

tensorflow PYPI version 2.8.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - acuity 6.18.0 - acuitylite =6.6.0, =1.2.8, =1.0.43, =0.2.2, =0.0.1, =0.0.2, =0.2.8, =0.14.0, =0.1.3, =0.0.9, =0.2.27, =0.2.41 -...

acuity (6.18.0), acuitylite (>=6.6.0, <=6.21.0) +60 more potentially affected by CVE-2022-35959 via tensorflow (2.8.0)

tensorflow PYPI version 2.8.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - acuity 6.18.0 - acuitylite =6.6.0, =1.2.8, =1.0.43, =0.2.2, =0.0.1, =0.0.2, =0.2.8, =0.14.0, =0.1.3, =0.0.9, =0.2.27, =0.2.41 -...

acuity (6.18.0), acuitylite (>=6.6.0, <=6.21.0) +60 more potentially affected by CVE-2022-36005 via tensorflow (2.8.0)

tensorflow PYPI version 2.8.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - acuity 6.18.0 - acuitylite =6.6.0, =1.2.8, =1.0.43, =0.2.2, =0.0.1, =0.0.2, =0.2.8, =0.14.0, =0.1.3, =0.0.9, =0.2.27, =0.2.41 -...

acuity (6.18.0), acuitylite (>=6.6.0, <=6.21.0) +60 more potentially affected by CVE-2022-29205 via tensorflow (2.8.0)

tensorflow PYPI version 2.8.0 is affected by a known vulnerability. The following packages have a transitive dependency on tensorflow and may be impacted: - acuity 6.18.0 - acuitylite =6.6.0, =1.2.8, =1.0.43, =0.2.2, =0.0.1, =0.0.2, =0.2.8, =0.14.0, =0.1.3, =0.0.9, =0.2.27, =0.2.41 -...