PassGAN

PassGAN Этот репозиторий содержит код для статьи PassGAN: A Deep Learning Approach for Password Guessing. Модель из PassGAN взята из статьи Improved Training of Wasserstein GANs, и предполагается, что авторы PassGAN использовали реализацию improvedwgantraining на TensorFlow в своей работе. По это...

ALYCON-Threat-Landscape

ALYCON: 高度な脅威ランドスケープ分析 情報理論的位相空間分析を用いたマルチドメイン脅威検出 概要 ALYCON の位相空間可視化は、マルチドメインの脅威検出と異常分類を示しています。 フレームワーク: トレーニング不要で、シャノンエントロピー H、フィッシャー情報量 F、ワッサースタイン距離 W を用いた普遍的な異常検出。システム状態を幾何学的に異なる領域にマッピングします。 可視化コンポーネント 脅威ランドスケープマップは以下を示します: 位相空間座標: H, F, W 検出軸 状態クラスタリング: 正常状態 vs 異常状態 マルチドメイン分析: クロスドメイン脅威検出能力...

BGA: A Noise-Immune Neural Distillation Framework for Malicious Signature Extraction in High-Entropy Encrypted Flows

To mitigate attention dilution in high-entropy TLS 1.3 flows, we propose BGA, a noise-immune neural distillation framework for encrypted threat intelligence.The methodology first employs Analysis of Variance ANOVA to decouple high-discriminatory control-plane features - specifically industrial...

A Novel Solution for Zero-Day Attack Detection in IDS Using Self-Attention and Jensen-Shannon Divergence in WGAN-GP

The increasing sophistication of cyber threats, especially zero-day attacks, poses a significant challenge to cybersecurity. Zero-day attacks exploit unknown vulnerabilities, making them difficult to detect and defend against. Existing approaches patch flaws and deploy an Intrusion Detection Syst...

False Data-Injection Attack Detection in Cyber-Physical Systems: a Wasserstein Distributionally Robust Reachability Optimization Approach

Cyber-physical system CPS is the foundational backbone of modern critical infrastructures, so ensuring its security and resilience against cyber-attacks is of pivotal importance. This paper addresses the challenge of designing anomaly detectors for CPS under false-data injection FDI attacks and...

Private Evolution Converges

Private Evolution PE is a promising training-free method for differentially private DP synthetic data generation. While it achieves strong performance in some domains e.g., images and text, its behavior in others e.g., tabular data is less consistent. To date, the only theoretical analysis of the...

RAID: an In-Training Defense against Attribute Inference Attacks in Recommender Systems

In various networks and mobile applications, users are highly susceptible to attribute inference attacks, with particularly prevalent occurrences in recommender systems. Attackers exploit partially exposed user profiles in recommendation models, such as user embeddings, to infer private attribute...