Improving IoT Intrusion Detection through SMOTE-Based Oversampling and Extended Multi-Model Evaluation on Side-Channel Power Data

The detection of intrusions in IoT-based networks poses challenges that cannot be overcome using traditional machine learning methods. Perhaps the biggest of them is related to the presence of a class imbalance in the side-channel dataset, where the number of samples in the normal class compared ...

Neurosymbolic Learning for Advanced Persistent Threat Detection under Extreme Class Imbalance

The growing deployment of Internet of Things IoT devices in smart cities and industrial environments increases vulnerability to stealthy, multi-stage advanced persistent threats APTs that exploit wireless communication. Detection is challenging due to severe class imbalance in network traffic,...

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...

Comparative Evaluation of VAE, GAN, and SMOTE for Tor Detection in Encrypted Network Traffic

Encrypted network traffic poses significant challenges for intrusion detection due to the lack of payload visibility, limited labeled datasets, and high class imbalance between benign and malicious activities. Traditional data augmentation methods struggle to preserve the complex temporal and...

SD-CGAN: Conditional Sinkhorn Divergence GAN for DDoS Anomaly Detection in IoT Networks

The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated Denial-of-Service DoS attacks and zero-day exploits under highly dynamic and imbalanced traffic conditions. This paper proposes SD-CGAN, a Conditional...

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning

As IoT ecosystems continue to expand across critical sectors, they have become prominent targets for increasingly sophisticated and large-scale malware attacks. The evolving threat landscape, combined with the sensitive nature of IoT-generated data, demands detection frameworks that are both...

Network Threat Detection: Addressing Class Imbalanced Data with Deep Forest

With the rapid expansion of Internet of Things IoT networks, detecting malicious traffic in real-time has become a critical cybersecurity challenge. This research addresses the detection challenges by presenting a comprehensive empirical analysis of machine learning techniques for malware detecti...

MalVis: a Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification

As technology advances, Android malware continues to pose significant threats to devices and sensitive data. The open-source nature of the Android OS and the availability of its SDK contribute to this rapid growth. Traditional malware detection techniques, such as signature-based, static, and...

Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions

Phishing detection on Ethereum has increasingly leveraged advanced machine learning techniques to identify fraudulent transactions. However, limited attention has been given to understanding the effectiveness of feature selection strategies and the role of graph-based models in enhancing detectio...