AIX360

AI Explainability 360 v0.3.0 ✨ NUEVO: IBM Research tiene un nuevo kit de herramientas In-Context Explainability 360 ICX360 que extiende la explicabilidad a los LLM , específicamente en cuanto a la entrada proporcionada al LLM. ¡Échale un vistazo! 🚀✨ El kit de herramientas AI Explainability 360 es...

SecML

SecML: Una librería para Machine Learning Seguro y Explicable SecML es una librería de código abierto en Python para la evaluación de seguridad de algoritmos de Machine Learning ML. Viene con un conjunto de potentes características: Amplia gama de algoritmos ML soportados. Todos los algoritmos de...

ThreatDetect

ThreatDetect ThreatDetect 是一个基于 Streamlit 的内部威胁检测原型，用于分析员工活动数据并标记潜在风险行为。它使用训练好的 XGBoost 分类器结合孤立森林异常检测器，生成组织级风险摘要和可解释的员工级洞察。 演示 该应用可以使用 Streamlit 本地运行。之前发布了一个部署演示，地址为： https://threatdetectcos720.streamlit.app/ 主要功能 从上传的 CSV 文件进行批量风险检测 组织威胁摘要，包含数量、概率分布和风险等级 使用 SHAP 值对每位员工进行可解释性分析...

UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection

The detection of previously unseen network attacks remains a major challenge for intrusion detection systems. Although supervised learning methods often perform well on known attack classes, they are limited when new attack types are not represented in the training data. Unsupervised methods are...

Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets

This paper investigates a unexplored yet impactful vulnerability in AI explainability used in intrusion detection IDS: multicollinearity-induced instability. Despite extensive reliance on post-hoc explainability tools such as SHAP or LIME, the impact of correlated features on explanation robustne...

Static Attribution of Android Residential Proxy Malware Using Graph Kernels

Android residential proxy applications represent a growing class of potentially-unwanted programs PUPs that covertly route third-party traffic through end-user devices, enabling ad fraud, credential abuse, and evasion of geolocation controls by sophisticated threat actors. Attributing an unknown...

The Code Whisperer: LLM and Graph-Based AI for Smell and Vulnerability Resolution

Code smells and software vulnerabilities both increase maintenance cost, yet they are often handled by separate tools that miss structural context and produce noisy warnings. This paper presents The Code Whisperer, a hybrid framework that combines graph-based program analysis with large language...

Explainable Autonomous Cyber Defense Using Adversarial Multi-Agent Reinforcement Learning

Autonomous agents are increasingly deployed in both offensive and defensive cyber operations, creating high-speed, closed-loop interactions in critical infrastructure environments. Advanced Persistent Threat APT actors exploit "Living off the Land" techniques and targeted telemetry perturbations ...

Explainability-Guided Adversarial Attacks on Transformer-Based Malware Detectors Using Control Flow Graphs

Transformer-based malware detection systems operating on graph modalities such as control flow graphs CFGs achieve strong performance by modeling structural relationships in program behavior. However, their robustness to adversarial evasion attacks remains underexplored. This paper examines the...

Explainability-Aware Evaluation of Transfer Learning Models for IoT DDoS Detection under Resource Constraints

Distributed denial-of-service DDoS attacks threaten the availability of Internet of Things IoT infrastructures, particularly under resource-constrained deployment conditions. Although transfer learning models have shown promising detection accuracy, their reliability, computational feasibility, a...

Empirical Analysis of Adversarial Robustness and Explainability Drift in Cybersecurity Classifiers

Machine learning ML models are increasingly deployed in cybersecurity applications such as phishing detection and network intrusion prevention. However, these models remain vulnerable to adversarial perturbations small, deliberate input modifications that can degrade detection accuracy and...

Human-Centered Explainability in AI-Enhanced UI Security Interfaces: Designing Trustworthy Copilots for Cybersecurity Analysts

Artificial intelligence AI copilots are increasingly integrated into enterprise cybersecurity platforms to assist analysts in threat detection, triage, and remediation. However, the effectiveness of these systems depends not only on the accuracy of underlying models but also on the degree to whic...

Explainability Methods for Hardware Trojan Detection: A Systematic Comparison

Hardware trojan detection requires accurate identification and interpretable explanations for security engineers to validate and act on results. This work compares three explainability categories for gate-level trojan detection on the Trust-Hub benchmark: 1 domain-aware property-based analysis of...

ReGAIN: Retrieval-Grounded AI Framework for Network Traffic Analysis

Modern networks generate vast, heterogeneous traffic that must be continuously analyzed for security and performance. Traditional network traffic analysis systems, whether rule-based or machine learning-driven, often suffer from high false positives and lack interpretability, limiting analyst...

Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspective

Artificial Intelligence AI has revolutionized software development, particularly by automating repetitive tasks and improving developer productivity. While these advancements are well-documented, the use of AI-powered tools for Software Vulnerability Management SVM, such as vulnerability detectio...

A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation

Graph Neural Networks GNNs have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings togeth...

Enhancing Adversarial Robustness of IoT Intrusion Detection Via SHAP-Based Attribution Fingerprinting

The rapid proliferation of Internet of Things IoT devices has transformed numerous industries by enabling seamless connectivity and data-driven automation. However, this expansion has also exposed IoT networks to increasingly sophisticated security threats, including adversarial attacks targeting...

Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses

With the wide application of deep reinforcement learning DRL techniques in complex fields such as autonomous driving, intelligent manufacturing, and smart healthcare, how to improve its security and robustness in dynamic and changeable environments has become a core issue in current research...

Bridging Semantics and Structure for Software Vulnerability Detection Using Hybrid Network Models

Software vulnerabilities remain a persistent risk, yet static and dynamic analyses often overlook structural dependencies that shape insecure behaviors. Viewing programs as heterogeneous graphs, we capture control- and data-flow relations as complex interaction networks. Our hybrid framework...

Explainable Ensemble Learning for Graph-Based Malware Detection

Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks GNNs have shown promise in this domain by modeling rich structural dependencies in graph-based program representations such a...