c4-meta-system

C4-META System v1.0.0 research prototype Research prototype for multi-layer AI defense: C4 explainability, ensemble classifiers, deobfuscation, O₂ security engine, ThoughtVirus defense, and SVETILO value alignment. Alpha-grade — validated internally, external audit pending. Version: 1.0.0-alpha |...

DVDR_LLM

DVDR-LLM: When Are LLMs Better Together for Software Vulnerability Detection and Repair? Official implementation and artifact for the research paper: "DVDR-LLM: When Are LLMs Better Together for Software Vulnerability Detection and Repair?" 🎯 Overview DVDR-LLM is an ensemble framework that...

Bridging the Closed-World Gap: Interpretable Anomaly Detection Via a Hybrid TSK-FCM Ensemble on the BETH Dataset

A fundamental paradox plagues the deployment of Machine Learning in cybersecurity: supervised models generalize poorly to novel, Out-of-Distribution OoD attacks, whereas unsupervised models often suffer from poor separability, leading to unmanageable false-alarm rates. This challenge is exemplifi...

CLEANSTART-2026-ZA11117 unauthenticated attacker can inject arbitrary fake log lines into Apache ZooKeeper's operational log by sending a crafted add_auth("ensemble",

CVE-2026-84501 affects multiple packages. An unauthenticated attacker can inject arbitrary fake log lines into Apache ZooKeeper's operational log by sending a crafted addauth"ensemble",. See references for individual vulnerability details...

Johnny Still Receives Spam SMS: Assessing the Robustness of SMS Spam Detection

SMS spam detection systems often achieve high accuracy in controlled environments but struggle against adversarial attacks and increasingly sophisticated spam tactics in real-world deployments. In this paper, we evaluate the robustness of SMS anti-spam systems that end users actually rely on,...

Enhancing Web Application Firewalls with Machine Learning for SQL Injection Detection

Detecting SQL Injection SQLi attacks ranks among the most critical challenges in web application security. This research conducted a systematic literature review to identify the research gaps in this domain and responsively designed and optimised a DistilBERT-Stacked Ensemble pipeline to improve...

Cross-Corpus Evaluation of Generalizable Vulnerability Detection in IoT Firmware

IoT firmware vulnerability detection remains challenging due to heterogeneous firmware ecosystems, resource-constrained platforms, and limitations in existing benchmarks. Many datasets are synthetic or general-purpose and lack human-verified, contamination-screened annotations, limiting evidence ...

On the Performance of Malware Detection Classifiers Using Hardware Performance Counters

Malware detection using Hardware Performance Counters HPC has emerged as a promising solution to improve the security of computing systems as a complement to antivirus software. Hardware-based malware detectors HMD use Machine Learning ML classifiers to detect malicious application patterns. The...

Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis

Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours. While large language models LLMs demonstrate impressive capabilities for technical artifact interpretation, the opacity and escalating API costs of closed-weight...

Enhanced Multi-Class DDoS Attack Identification Using a Meta-Learning Ensemble

Distributed Denial of Service DDoS attacks continue to pose significant threats to network availability and security. While many detection systems focus on binary classification attack vs. benign, effective mitigation often requires identifying the specific type of DDoS attack. This paper...

LLM: LSTM Look-Ahead Moving Target Defense Based on Historical Malicious Scan

Network scanning is a critical preliminary step for most adversaries to gain essential information before launching cyber attacks. Moving Target Defense MTD based on IP shuffling has emerged as a proactive defense strategy to counteract these reconnaissance efforts. Unlike static, reactive defens...

Meta-Quantum Ensemble Framework for Robust Network Intrusion Detection

Intrusion Detection Systems IDSs must maintain high detection sensitivity while operating under strict false-positive constraints, a challenge intensified by class imbalance and heterogeneous IoT traffic. This work investigates whether heterogeneous quantum learners can provide useful and...

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...

SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection

Music streaming fraud, where bad actors artificially inflate stream counts to manipulate chart rankings and royalty payments, poses a significant threat to streaming services and legitimate content creators. Traditional fraud detection approaches struggle with a critical challenge: many legitimat...

From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation

Machine-learning-based Intrusion Detection Systems IDS have achieved impressive accuracy in classifying network attacks, yet they consistently fall short on the question that matters most to a security analyst: what should I do next? This paper presents a unified, end-to-end framework that closes...

Gray-Box Poisoning of Continuous Malware Ingestion Pipelines

Modern malware detection pipelines rely on continuous data ingestion and machine learning to counter the high volume of novel threats. This work investigates a realistic gray-box poisoning threat model targeting these pipelines. Using the secmlmalware framework, we generate problem-space...

Toward Autonomous SOC Operations: End-To-End LLM Framework for Threat Detection, Query Generation, and Resolution in Security Operations

Security Operations Centers SOCs face mounting operational challenges. These challenges come from increasing threat volumes, heterogeneous SIEM platforms, and time-consuming manual triage workflows. We present an end-to-end threat management framework that integrates ensemble-based detection,...

SDNGuardStack: An Explainable Ensemble Learning Framework for High-Accuracy Intrusion Detection in Software-Defined Networks

Software-Defined Networking SDN is another technology that has been developing in the last few years as a relevant technique to improve network programmability and administration. Nonetheless, its centralized design presents a major security issue, which requires effective intrusion detection...

Robustness Analysis of Machine Learning Models for IoT Intrusion Detection under Data Poisoning Attacks

Ensuring the reliability of machine learning-based intrusion detection systems remains a critical challenge in Internet of Things IoT environments, particularly as data poisoning attacks increasingly threaten the integrity of model training pipelines. This study evaluates the susceptibility of fo...

Optimizing IoT Intrusion Detection with Tabular Foundation Models for Smart City Forensics

Security operations in smart cities demand detection systems that balance accuracy with response time. While ensemble methods like Random Forest achieve high accuracy, their computational overhead impedes real-time forensic triage. We present the first systematic evaluation of TabPFNv2.5, a...