AIX360

AI Explainability 360 v0.3.0 ✨ NEW: IBM Research has a new In-Context Explainability 360 ICX360 toolkit that extends explainability to LLMs , specifically in terms of the input given to the LLM. Please check it out! 🚀✨ The AI Explainability 360 toolkit is an open-source library that supports...

SecML

SecML: A library for Secure and Explainable Machine Learning SecML is an open-source Python library for the security evaluation of Machine Learning ML algorithms. It comes with a set of powerful features: Wide range of supported ML algorithms. All supervised learning algorithms supported by...

ThreatDetect

ThreatDetect ThreatDetect is a Streamlit-based insider threat detection prototype that analyzes employee activity data and flags potentially risky behavior. It uses a trained XGBoost classifier together with an Isolation Forest anomaly detector to produce organisation-level risk summaries and...

X-WAD: EXplainable Web Anomaly Detection

The rapid growth of web-based services, particularly API-driven architectures, reflects an increasing reliance on distributed systems, exposing sensitive data to security risks and making the adoption of automated defensive mechanisms essential. In this context, where benign traffic predominates ...

Beyond Gradient-Based Attacks: Adversarial Robustness and Explainability Stability in Cybersecurity Classifiers

Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that security analysts rely on to understand and triage alerts. We extend our prior MLP conference study to Random Forest and XGBoost across four tabular securi...

Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks

Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things IIoT networks due to their suitability for resource-constrained edge deployment. Most reported results evaluate these models only within their training network, leaving behavior ...

Explainability-Aware Frustum Attack: Exposing Structural Vulnerabilities in LiDAR-Based 3D Object Detectors

The structural vulnerabilities of point cloud-based 3D object detectors remain poorly understood. Prior work has studied adversarial robustness primarily on isolated 3D object models, while recent LiDAR spoofing attacks target richer and more realistic driving scenes but focus mainly on physical...

Cybersecurity Is the True Frontier for Generative AI Success or Failure

Cybersecurity is a real-life test-bed for many machine learning problems at once, especially when considering modern strides in using Large Language Models LLMs to automate processes as "agents.'' Cybersecurity workflows require orchestrating hundreds of standard and bespoke tools through various...

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...