The field of cybersecurity is rapidly advancing with the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques. Recent research has focused on developing innovative methods for detecting and mitigating cyber threats, such as Distributed Denial of Service (DDoS) attacks and Advanced Persistent Threats (APTs). Notably, the application of Large Language Models (LLMs) has shown promising results in various cybersecurity tasks, including network attack detection and malware analysis. Additionally, researchers have explored the use of hybrid architectures, such as CNN-BiLSTM, for efficient IoT intrusion detection. These developments highlight the potential of AI-driven approaches in enhancing cybersecurity measures. Noteworthy papers include 'Knowledge Transfer from LLMs to Provenance Analysis' and 'Payload-Aware Intrusion Detection with CMAE and Large Language Models', which demonstrate the effectiveness of LLMs in cybersecurity applications.
Advances in AI-Driven Cybersecurity
Sources
Detecting and Mitigating DDoS Attacks with AI: A Survey
BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection
Literature Review: Cyber Security Monitoring in Maritime
Knowledge Transfer from LLMs to Provenance Analysis: A Semantic-Augmented Method for APT Detection
J&H: Evaluating the Robustness of Large Language Models Under Knowledge-Injection Attacks in Legal Domain
Large Language Models powered Network Attack Detection: Architecture, Opportunities and Case Study
Adaptive Machine Learning for Resource-Constrained Environments
Secure Edge Computing Reference Architecture for Data-driven Structural Health Monitoring: Lessons Learned from Implementation and Benchmarking
Process or Result? Manipulated Ending Tokens Can Mislead Reasoning LLMs to Ignore the Correct Reasoning Steps
Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture
A Benign Activity Extraction Method for Malignant Activity Identification using Data Provenance
CNN+Transformer Based Anomaly Traffic Detection in UAV Networks for Emergency Rescue
Payload-Aware Intrusion Detection with CMAE and Large Language Models
Leveraging VAE-Derived Latent Spaces for Enhanced Malware Detection with Machine Learning Classifiers
AUTOBargeSim: MATLAB(R) toolbox for the design and analysis of the guidance and control system for autonomous inland vessels
Intelligent IoT Attack Detection Design via ODLLM with Feature Ranking-based Knowledge Base
Training Large Language Models for Advanced Typosquatting Detection