Imbalanced Hardware Trojan Detection Based on Conditional Generative Adversarial Networks

Adversarially Robust Hardware Trojan Detection with Synthetic Data Augmentation

This paper evaluates the robustness of the Support Vector Machine (SVM) classifier, a leading algorithm in state-of-the-art HT detection frameworks, under gradient-based adversarial attacks, and highlights the need to reframe hardware security evaluations beyond nominal accuracy toward adversarial robustness.

Analyzing Malware Behavior Using Generative Neural Networks

Traditional detection techniques are struggling with ever-evolving malware threats like zero-day attacks, polymorphic malware, and adversarial samples. Current detection systems (signature-based, heuristic-based, conventional machine learning) fail to generalize to unseen/obfuscated malware variants. In an attempt to overcome these constraints, this paper investigates the possibilities of employing Generative Neural Networks (GNNs), in the form of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) for the purpose of malware be haviour analysis and detection. We aim to create a novel framework for detecting malware samples that provides some of the best performance in terms of accuracy, precision, and recall while remaining robust to new or unseen malware. This work aims to firstly implement a generative learning-based approach and to measure its adversarial robustness in comparison with the four existing detection techniques. Experimental results show the accuracy, precision, recall of the proposed model is found to be 96.5%, 95.9%, 94.6% with the false positive rate of the model which can be negligible and it is 3.2% which outperforms the traditional machine learning and deep learning models. Our results demonstrate that GNN-based malware detection not only addresses the limitations of conventional approaches in terms of scalability but also provides a more robust and adaptable framework that could be integrated into future real-time threat intelligence and automated defense systems.

Generative Adversarial Networks for Hardening Cloud Intrusion Detection Systems

Cloud computing has emerged as an important core to the contemporary digital services, facilitating scalable, on demand provisioning of resources across a variety of application fields. Nevertheless, this multi-tenant and dynamic environment of clouds and the amplified attack surface make the detection of intrusions through reliable methods a consistent issue that cloud security systems struggle with. The proposed work is a Generative Adversarial Network (GAN)-based hardening framework of cloud intrusion detection systems, targeting better resilience to changing and low-rate cyberattacks. The methodology combines a conditional generator which is used to generate realistic cloud-specific attack traffic, a discriminator used to refine the adversarial traffic, as well as a co-trained intrusion classifier trained on both clean and synthetic data in a closed-loop way. The feature-aware regularization is introduced to maintain the statistical consistency of network traffic, and optimize the attack diversity. The proposed approach is proved to yield better results in comparison with signature-based, machine learning, deep learning, and adversarial ML-based IDS models by experimental assessment. Significant gains in the accuracy of identifying, the ability to recall, stability, and minimizing errors are also noticed with quantifiable increases observed in all evaluation measures. These findings represent the usefulness of adversarial data-driven learning to develop robust, adaptive, and future-ready cloud intrusion detection systems.

An advanced framework for malware detection using adversarial learning

Generative Adversarial Networks for Anomaly and Malware Detection

Generative Adversarial Networks or GANs, have become a significant approach in deep learning along with Con-volutional and Recurrent Neural Networks, due to improvements in computing technology and more advanced ways to train these frameworks or models. Since GANs were first introduced in 2014, their application has expanded beyond image generation to include critical security tasks like anomaly detection and malware analysis. This paper offers a comprehensive survey of how GAN-based methods are utilized for identifying unusual and harmful activities in cyber settings. It examines key variants of GANs relevant to this field, explains their fundamental architectures and training methods, and explains their integration into systems to detect anomalies and malware. Additionally, the paper catalogs publicly accessible datasets and evaluation metrics frequently used in the reviewed studies to illustrate common experimental methodologies and research directions. Finally, it addresses ongoing challenges and potential future avenues for employing GANs to counteract emerging cybersecurity threats, highlighting their importance in developing more proactive and robust security measures.

Leak-Free Machine Learning for Hardware Trojan Detection at RTL

Hardware Trojans (HTs) are considered a significant challenge to contemporary IC production lines since any malicious changes made during either the design phase or IC fabrication process are impossible to identify post production. This study proposes an ML model that automatically detects HTs using only the Register Transfer Level (RTL) Verilog code without needing physical verification or having a golden design to compare against. While there are numerous RTL models available, they are limited in their use of features and may be prone to data leakage. The proposed model utilizes a pipeline that avoids data leakage by performing SMOTE operations only within training folds. Thirty five features are extracted from each design, ranging from structural properties to operational behavior and signal connections. Five classifiers Random Forest (RF), Gradient Boosting (GB), XGBoost, Support Vector Machine (SVM), and Logistic Regression (LR) are evaluated using stratified cross-validation on a dataset of 2,150 RTL designs (76.7% Trojan, 23.3% clean). Among them, the RF model achieves the best performance, with a cross-validation ROC-AUC of 0.9437 and a test ROC-AUC of 0.9687, along with 97% precision and 90% recall for Trojan detection. These results demonstrate that carefully designed static feature extraction combined with a robust ML pipeline can provide an effective, fast, and scalable solution for early stage hardware security analysis at the RTL level.