Gate AI: LLM Security Benchmark Evaluation Methodology and Results

Published evaluations of prompt-injection and jailbreak detectors for Large Language Models often suffer from two systematic weaknesses: per-dataset threshold tuning and undisclosed operating points. We describe an evaluation harness that addresses both. The detector under evaluation is scored...

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks

The rapid expansion of Internet of Things IoT deployments has enlarged the attack surface of modern digital infrastructure while exposing a key security mismatch: many intrusion detection systems IDSs remain too computationally expensive for constrained IoT environments. This paper presents...

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...

Privacy-Aware Machine Unlearning with SISA for Reinforcement Learning-Based Ransomware Detection

Ransomware detection systems increasingly rely on behavior-based machine learning to address evolving attack strategies. However, emerging privacy compliance, data governance, and responsible AI deployment demand not only accurate detection but also the ability to efficiently remove the influence...

Hyperparameter Tuning-Based Optimized Performance Analysis of Machine Learning Algorithms for Network Intrusion Detection

Network Intrusion Detection Systems NIDS are essential for securing networks by identifying and mitigating unauthorized activities indicative of cyberattacks. As cyber threats grow increasingly sophisticated, NIDS must evolve to detect both emerging threats and deviations from normal behavior. Th...

SHERLOCK: A Deep Learning Approach to Detect Software Vulnerabilities

The increasing reliance on software in various applications has made the problem of software vulnerability detection more critical. Software vulnerabilities can lead to security breaches, data theft, and other negative outcomes. Traditional software vulnerability detection techniques, such as...

Deep Reinforcement Learning for Phishing Detection with Transformer-Based Semantic Features

Phishing is a cybercrime in which individuals are deceived into revealing personal information, often resulting in financial loss. These attacks commonly occur through fraudulent messages, misleading advertisements, and compromised legitimate websites. This study proposes a Quantile Regression De...

A Secured Intent-Based Networking (SIBN) with Data-Driven Time-Aware Intrusion Detection

While Intent-Based Networking IBN promises operational efficiency through autonomous and abstraction-driven network management, a critical unaddressed issue lies in IBN's implicit trust in the integrity of intent ingested by the network. This inherent assumption of data reliability creates a blin...

Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications

This work empirically evaluates machine learning models on two imbalanced public datasets KDDCUP99 and Credit Card Fraud 2013. The method includes data preparation, model training, and evaluation, using an 80/20 train/test split. Models tested include eXtreme Gradient Boosting XGB, Multi Layer...

A Gradient-Optimized TSK Fuzzy Framework for Explainable Phishing Detection

Phishing attacks represent an increasingly sophisticated and pervasive threat to individuals and organizations, causing significant financial losses, identity theft, and severe damage to institutional reputations. Existing phishing detection methods often struggle to simultaneously achieve high...

Security Bulletin: WML CE Scikit-learn vulnerable to irresponsible usage

Summary WML containers include scikit-learn. Scikit-learn includes joblib and pickle to cache and load models. Pickle and joblib by extension, has some issues regarding maintainability and security. Because of this, usage of the joblib.load function in scikit-learn must be done in a responsible...