Don't Trust Us: A Privacy-By-Design Android Malware Detection Pipeline

Android malware detection increasingly relies on collecting and processing sensitive user data, including device identifiers, network artifacts, and runtime traces, while privacy is too often treated as a secondary concern. Existing privacy-aware approaches typically enforce privacy after data...

A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems

AI-powered edge computing security is moving Intelligent Transportation Systems ITS from passive, rule-based protections to proactive, smart, zero-touch, self-sufficient safeguards that neutralize threats in milliseconds. As transportation becomes more connected with edge computing, massive IoT,...

On the Possible Detectability of Image-In-Image Steganography

This paper investigates the detectability of popular imagein-image steganography schemes 1, 2, 3, 4, 5. In this paradigm, the payload is usually an image of the same size as the Cover image, leading to very high embedding rates. We first show that the embedding yields a mixing process that is...

Empirical Evaluation of SMOTE in Android Malware Detection with Machine Learning: Challenges and Performance in CICMalDroid 2020

Malware, malicious software designed to damage computer systems and perpetrate scams, is proliferating at an alarming rate, with thousands of new threats emerging daily. Android devices, prevalent in smartphones, smartwatches, tablets, and IoTs, represent a vast attack surface, making malware...

Quantum AI for Cybersecurity: A Hybrid Quantum-Classical Models for Attack Path Analysis

Modern cyberattacks are increasingly complex, posing significant challenges to classical machine learning methods, particularly when labeled data is limited and feature interactions are highly non-linear. In this study we investigates the potential of hybrid quantum-classical learning to enhance...

Towards Eco Friendly Cybersecurity: Machine Learning Based Anomaly Detection with Carbon and Energy Metrics

The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection framework that unifies machine learning based network...

Efficient Jailbreak Mitigation Using Semantic Linear Classification in a Multi-Staged Pipeline

Prompt injection and jailbreaking attacks pose persistent security challenges to large language model LLM-based systems. We present an efficient and systematically evaluated defense architecture that mitigates these threats through a lightweight, multi-stage pipeline. Its core component is a...

Cyber Threat Detection Enabled by Quantum Computing

Threat detection models in cybersecurity must keep up with shifting traffic, strict feature budgets, and noisy hardware, yet even strong classical systems still miss rare or borderline attacks when the data distribution drifts. Small, near-term quantum processors are now available, but existing...

Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions

The increasing number of cyber threats and rapidly evolving tactics, as well as the high volume of data in recent years, have caused classical machine learning, rules, and signature-based defence strategies to fail, rendering them unable to keep up. An alternative, Quantum Machine Learning QML, h...

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...

Unsupervised Anomaly Detection for Smart IoT Devices: Performance and Resource Comparison

The rapid expansion of Internet of Things IoT deployments across diverse sectors has significantly enhanced operational efficiency, yet concurrently elevated cybersecurity vulnerabilities due to increased exposure to cyber threats. Given the limitations of traditional signature-based Anomaly...

Adaptive Dual-Layer Web Application Firewall (ADL-WAF) Leveraging Machine Learning for Enhanced Anomaly and Threat Detection

Web Application Firewalls are crucial for protecting web applications against a wide range of cyber threats. Traditional Web Application Firewalls often struggle to effectively distinguish between malicious and legitimate traffic, leading to limited efficacy in threat detection. To overcome these...

Detecting Vulnerabilities from Issue Reports for Internet-Of-Things

Timely identification of issue reports reflecting software vulnerabilities is crucial, particularly for Internet-of-Things IoT where analysis is slower than non-IoT systems. While Machine Learning ML and Large Language Models LLMs detect vulnerability-indicating issues in non-IoT systems, their I...

Android Malware Detection: A Machine Learning Approach

This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and...

An Intrusion Detection System in Internet of Things Using Grasshopper Optimization Algorithm and Machine Learning Algorithms

The Internet of Things IoT has emerged as a foundational paradigm supporting a range of applications, including healthcare, education, agriculture, smart homes, and, more recently, enterprise systems. However, significant advancements in IoT networks have been impeded by security vulnerabilities...

Machine Learning-Based AES Key Recovery Via Side-Channel Analysis on the ASCAD Dataset

Cryptographic algorithms like AES and RSA are widely used and they are mathematically robust and almost unbreakable but its implementation on physical devices often leak information through side channels, such as electromagnetic EM emissions, potentially compromising said theoretically secure...

Busting the Paper Ballot: Voting Meets Adversarial Machine Learning

We show the security risk associated with using machine learning classifiers in United States election tabulators. The central classification task in election tabulation is deciding whether a mark does or does not appear on a bubble associated to an alternative in a contest on the ballot. Barrett...

Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders

Currently, digital security mechanisms like Anomaly Detection Systems using Autoencoders AE show great potential for bypassing problems intrinsic to the data, such as data imbalance. Because AE use a non-trivial and nonstandardized separation threshold to classify the extracted reconstruction...

Fingerprinting Deep Learning Models Via Network Traffic Patterns in Federated Learning

Federated Learning FL is increasingly adopted as a decentralized machine learning paradigm due to its capability to preserve data privacy by training models without centralizing user data. However, FL is susceptible to indirect privacy breaches via network traffic analysis-an area not explored in...

Optimizing DDoS Detection in SDNs through Machine Learning Models

The emergence of Software-Defined Networking SDN has changed the network structure by separating the control plane from the data plane. However, this innovation has also increased susceptibility to DDoS attacks. Existing detection techniques are often ineffective due to data imbalance and accurac...