How to build secure AI based 6G solutions for adversarial machine learning attacks — The design of security schemes for deep learning algorithms is critical for protecting wireless networks from adversarial attacks. However, there is no consensus about the best way to do this. In this blog, I present a security vulnerability in deep learning for beamforming prediction using deep neural networks (DNNs), and…

Deep learning based Sequential model for malware analysis using Windows Exe API Calls — Malware development has seen diversity in terms of architecture and features. This advancement in the competencies of malware poses a severe threat and opens new research dimensions in malware detection. This study is focused on metamorphic malware that is the most advanced member of the malware family. It is quite…

A simple approach to protect your machine learning model for the adversarial attacks — There are several attacks against deep learning models in the literature, including fast-gradient sign method (FGSM), basic iterative method (BIM) or momentum iterative method (MIM) attacks. These attacks are the purest form of the gradient-based evading technique that is used by attackers to evade the classification model.

Introduction Even though artificial intelligence originated in decades, it has achieved its most significant progress in the last ten years. Now it is not only used in research centres and universities, but it has also started to be used in many commercial areas such as logistics, health and autonomous vehicles…

Our public malware dataset generated by Cuckoo Sandbox based on Windows OS API calls analysis for cybersecurity researchers for malware analysis in CSV file format for machine learning applications. Github: https://github.com/ocatak/malware_api_class If you find those results useful please cite them : @misc{ mal-api-2019, author = "Catak, FÖ. and…