Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense

This paper presents CADL Cognitive-Adaptive Deception Layer, an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning Random Forest, XGBoost, Neural Networks combined with behavioral...

Can LLMs Handle WebShell Detection? Overcoming Detection Challenges with Behavioral Function-Aware Framework

WebShell attacks, in which malicious scripts are injected into web servers, are a major cybersecurity threat. Traditional machine learning and deep learning methods are hampered by issues such as the need for extensive training data, catastrophic forgetting, and poor generalization. Recently, Lar...

Amazon’s Plan to Track Worker Keystrokes: A Sign of Controls to Come?

Data theft, insider threats and imposters accessing sensitive customer data have apparently gotten so bad inside Amazon, the company is considering rolling out keyboard-stroke monitoring for its customer-service reps. A confidential memo from inside Amazon explained that customer service credenti...

Trojan watch

We continue to research how proliferation of IoT devices affects the daily lives of users and their information security. In our previous study, we touched upon ways of intercepting authentication data using single-board microcomputers. This time, we turned out attention to wearable devices:...