WAF-A-MoLE A guided mutation-based fuzzer for ML-based Web Application Firewalls, inspired by AFL and based on the FuzzingBook by Andreas Zeller et al. Given an input SQL injection query, it tries to produce a semantic invariant query that is able...
AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning AutoPentest-DRL is an automated penetration testing framework based on Deep Reinforcement Learning (DRL) techniques. The framework determines the most appropriate attack path for a given network...
Detection of Sensitive Data Exposure in Images In the current age of smart devices and smartphones, any image taken using these devices is immediately auto uploaded to the cloud (Google Photos, iCloud, etc) or...
The idea of artificial intelligence (AI) has captured human imagination ever since it was first conceived. Nowadays AI is more than just an idea. The influence of computers on our livelihood has been growing...
Automated Security Risk Identification Using AutomationML-based Engineering Data This prototype identifies security risk sources (i.e., threats and vulnerabilities) and types of attack consequences based on AutomationML (AML) artifacts. The results of the risk identification...
Pesidious – Malware Mutation using Deep Reinforcement Learning and GANs Pesidious is an open-source tool that uses Generative Adversarial Networks (GAN) and Reinforcement Learning (RL) to generate mutative malware that can evade nextgen AI-powered...
SharpML SharpML is a proof of concept file share data mining tool using Machine Learning in Python and C#. The tool is discussed in more detail on our blog here, but is summarised below also:...
Deep Exploit Fully automatic penetration test tool using Machine Learning. Deep Exploit is fully automated penetration tool linked with Metasploit. Deep Exploit has two exploitation modes. Intelligence mode Deep Exploit identifies the status of all opened...
pcfg_cracker This project uses machine learning to identify password creation habits of users. A PCFG model is generated by training on a list of disclosed plaintext/cracked passwords. In the context of this project, the...
Deep Learning models for network traffic classification The development of an anomaly-based intrusion detection system (IDS) is a primary research direction in the field of intrusion detection. An IDS learns normal and anomalous behavior...
