Methods to Identify the Family of Advanced Persistent Threats Based on Deep Neural Network and n-gram of API calls
Résumé
Advanced Persistent Threat attacks (APTs) have a set of special features that make them different from other attacks. They are stealth, target-based, and designed by expert teams. There are various methods to detect malwares, but since the APT attacks are complex and well-designed to evade detection with a minimum fingerprint on the target systems, they are difficult to detect. Research on APTs has two challenges: the complexity of the attack and the small number of identified samples. These challenges have some drawbacks to the accuracy of malware classifiers. We address these two challenges in this study by presenting two methods. We used static and dynamic malware analysis as an input to the deep neural network to address the complexity of the attack challenge and n-gram of API calls as an input to four machine learning algorithms to address the small number of available samples. The feature selection phase of these two methods employs the TF-IDF measure to identify valuable terms in the malware analysis report. The proposed hybrid approach shows better results for the dataset we used for this research compared to the other baseline methods. In this research, we collected an APT dataset with a clear validation method which will benefit our future research.
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