Artificial Intelligence Cybersecurity

Key Chapter Title List

1. Adversarial Malware and Benign Sample Generation Based on Reinforcement Learning
2. Application of Auxiliary Classifier Generative Adversarial Networks in Malware Analysis
3. Evaluating the Robustness of Image-Based Malware Classifiers via Minor Perturbation Techniques
4. Detecting Botnets Using Deep Learning and Network Flow Analysis
5. Interpretability Analysis of Machine Learning-Based Malware Detection Results Using Rule Sets
6. Consortium Blockchain-Based Mobile Malware Detection
7. Application of the BERT Model in Malware Classification
8. Machine Learning Methods for Malware Evolution Detection

Document Introduction

This collection of research series focuses on the cutting-edge applications and challenges of artificial intelligence technology in cybersecurity, particularly in the field of malware detection and adversarial confrontation. As machine learning increasingly becomes a core component of cybersecurity defense systems, the inherent vulnerability of its models, the "black-box" nature of their decision-making processes, and their robustness issues in adversarial environments have become key bottlenecks restricting their effectiveness and trustworthiness. Through multiple in-depth technical papers, this report systematically explores how to utilize and defend against AI technology to build safer, more reliable, and interpretable malware detection systems.

The core themes revolve around two main lines: first, the attack perspective, i.e., how to use advanced AI technologies (such as reinforcement learning and generative adversarial networks) to generate adversarial malware samples capable of evading existing detection mechanisms; second, the defense and interpretability perspective, i.e., how to enhance the resistance of detection models to such attacks and improve the transparency and understandability of their decision-making processes. The specific research covers various malware carriers, from binary executable files (PE format) to Android applications (APK/DEX), and employs multiple analytical methods including static feature analysis, image-based representation, and network traffic analysis.

In terms of attack methodology, the report elaborates on a black-box attack framework based on reinforcement learning. This framework successfully deceives mainstream machine learning classifiers such as LightGBM and MalConv by making functionality-preserving modifications to executable files, achieving considerable evasion rates against some real-world antivirus software. Concurrently, the research also explores using Generative Adversarial Networks (GANs) to generate realistic "fake" malware images to test classifiers' discrimination capabilities and potential weaknesses. On the other hand, for image-based classifiers targeting the Android platform, the report demonstrates that simple code-level perturbations (such as inserting no-operation instructions or redundant strings) can significantly reduce model classification accuracy, revealing the fragility of such methods.

In terms of defense, detection, and interpretability, the report proposes a botnet detection method based on network flow pattern analysis and deep learning, achieving effective identification of command-and-control servers by extracting statistical features such as communication periodicity. To demystify the "black box" of machine learning models, the research extensively applies rule-based classification methods (such as the I-REP and RIPPER algorithms), attempting to transform complex model prediction results into human-understandable decision rule lists. It also proposes quantitative evaluation metrics like "interpretability entropy" to measure the difficulty of interpreting the output results of different machine learning models.

This collection integrates multiple independent yet complementary in-depth studies, providing cybersecurity researchers, AI practitioners, and anti-malware engineers with a comprehensive technical reference on the latest advancements, methods, challenges, and solutions in the intersecting field of AI and security. Its content not only includes specific algorithm implementations, experimental designs, and result analyses but also offers profound insights into the reliability, interpretability, and future development paths of AI models in real adversarial environments.