An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks

With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments. This paper presents an improved CNN-LSTM based intrusion detection model that combines multi-class classification,...

A Hybrid Approach for Malware Classification Using Secondary Features Fusion

The number of malware either variant or novel is rapidly increasing, making malware detection and mitigation a complex problem. One approach to improving malware mitigation is automatic detection and malware family classification. However, traditional malware detection methods cannot classify...

Machine Learning-Based Detection of MCP Attacks

The Model Context Protocol MCP is a new and emerging technology that extends the functionality of large language models, improving workflows but also exposing users to a new attack surface. Several studies have highlighted related security flaws, but MCP attack detection remains underexplored. To...

Incremental Federated Learning for Intrusion Detection in IoT Networks under Evolving Threat Landscape

The expansion of Internet of Things IoT devices has increased the attack surface of networks, necessitating a robust and adaptive intrusion detection systems. Machine learning based systems have been considered promising in enhancing the detection performance. Federated learning settings enabled ...

Binary and Multiclass Cyberattack Classification on GeNIS Dataset

The integration of Artificial Intelligence AI in Network Intrusion Detection Systems NIDS is a promising approach to tackle the increasing sophistication of cyberattacks. However, since Machine Learning ML and Deep Learning DL models rely heavily on the quality of their training data, the lack of...

Contrastive-KAN: a Semi-Supervised Intrusion Detection Framework for Cybersecurity with Scarce Labeled Data

In the era of the Fourth Industrial Revolution, cybersecurity and intrusion detection systems are vital for the secure and reliable operation of IoT and IIoT environments. A key challenge in this domain is the scarcity of labeled cyber-attack data, as most industrial systems operate under normal...

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs

The rapid expansion of Internet of Things IoT networks has led to a surge in security vulnerabilities, emphasizing the critical need for robust anomaly detection and classification techniques. In this work, we propose a novel approach for identifying anomalies in IoT network traffic by leveraging...