TL-RL-FusionNet: An Adaptive and Efficient Reinforcement Learning-Driven Transfer Learning Framework for Detecting Evolving Ransomware Threats

Modern ransomware exhibits polymorphic and evasive behaviors by frequently modifying execution patterns to evade detection. This dynamic nature disrupts feature spaces and limits the effectiveness of static or predefined models. To address this challenge, we propose TL-RL-FusionNet, a reinforceme...

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...

New Snake Keylogger Variant Leverages AutoIt Scripting to Evade Detection

A new variant of the Snake Keylogger malware is being used to actively target Windows users located in China, Turkey, Indonesia, Taiwan, and Spain. Fortinet FortiGuard Labs said the new version of the malware has been behind over 280 million blocked infection attempts worldwide since the start of...

DrSemu - Malware Detection And Classification Tool Based On Dynamic Behavior

Dr.Semu runs executables in an isolated environment, monitors the behavior of a process, and based on Dr.Semu rules created by you or the community, detects if the process is malicious or not. whoami:@qazqaz With Dr.Semu you can create rules to detect malware based on dynamic behavior of a proces...