Collaborating Authors

DOOM: A Novel Adversarial-DRL-Based Op-Code Level Metamorphic Malware Obfuscator for the Enhancement of IDS Artificial Intelligence

We designed and developed DOOM (Adversarial-DRL based Opcode level Obfuscator to generate Metamorphic malware), a novel system that uses adversarial deep reinforcement learning to obfuscate malware at the op-code level for the enhancement of IDS. The ultimate goal of DOOM is not to give a potent weapon in the hands of cyber-attackers, but to create defensive-mechanisms against advanced zero-day attacks. Experimental results indicate that the obfuscated malware created by DOOM could effectively mimic multiple-simultaneous zero-day attacks. To the best of our knowledge, DOOM is the first system that could generate obfuscated malware detailed to individual op-code level. DOOM is also the first-ever system to use efficient continuous action control based deep reinforcement learning in the area of malware generation and defense. Experimental results indicate that over 67% of the metamorphic malware generated by DOOM could easily evade detection from even the most potent IDS. This achievement gains significance, as with this, even IDS augment with advanced routing sub-system can be easily evaded by the malware generated by DOOM.

DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware Artificial Intelligence

In this paper, we propose a novel mechanism to normalize metamorphic and obfuscated malware down at the opcode level and hence create an advanced metamorphic malware de-obfuscation and defense system. We name this system DRLDO, for Deep Reinforcement Learning based De-Obfuscator. With the inclusion of the DRLDO as a sub-component, an existing Intrusion Detection System could be augmented with defensive capabilities against 'zero-day' attacks from obfuscated and metamorphic variants of existing malware. This gains importance, not only because there exists no system to date that uses advanced DRL to intelligently and automatically normalize obfuscation down even to the opcode level, but also because the DRLDO system does not mandate any changes to the existing IDS. The DRLDO system does not even mandate the IDS' classifier to be retrained with any new dataset containing obfuscated samples. Hence DRLDO could be easily retrofitted into any existing IDS deployment. We designed, developed, and conducted experiments on the system to evaluate the same against multiple-simultaneous attacks from obfuscations generated from malware samples from a standardized dataset that contains multiple generations of malware. Experimental results prove that DRLDO was able to successfully make the otherwise un-detectable obfuscated variants of the malware detectable by an existing pre-trained malware classifier. The detection probability was raised well above the cut-off mark to 0.6 for the classifier to detect the obfuscated malware unambiguously. Further, the de-obfuscated variants generated by DRLDO achieved a very high correlation (of 0.99) with the base malware. This observation validates that the DRLDO system is actually learning to de-obfuscate and not exploiting a trivial trick.

Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection Artificial Intelligence

Recently, Deep Learning has been showing promising results in various Artificial Intelligence applications like image recognition, natural language processing, language modeling, neural machine translation, etc. Although, in general, it is computationally more expensive as compared to classical machine learning techniques, their results are found to be more effective in some cases. Therefore, in this paper, we investigated and compared one of the Deep Learning Architecture called Deep Neural Network (DNN) with the classical Random Forest (RF) machine learning algorithm for the malware classification. We studied the performance of the classical RF and DNN with 2, 4 & 7 layers architectures with the four different feature sets, and found that irrespective of the features inputs, the classical RF accuracy outperforms the DNN.

An investigation of a deep learning based malware detection system Artificial Intelligence

We investigate a Deep Learning based system for malware detection. In the investigation, we experiment with different combination of Deep Learning architectures including Auto-Encoders, and Deep Neural Networks with varying layers over Malicia malware dataset on which earlier studies have obtained an accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these results were done using extensive man-made custom domain features and investing corresponding feature engineering and design efforts. In our proposed approach, besides improving the previous best results (99.21% accuracy and a False Positive Rate of 0.19%) indicates that Deep Learning based systems could deliver an effective defense against malware. Since it is good in automatically extracting higher conceptual features from the data, Deep Learning based systems could provide an effective, general and scalable mechanism for detection of existing and unknown malware.

Protecting from Malware Obfuscation Attacks through Adversarial Risk Analysis Machine Learning

Standard algorithms in detection systems perform insufficiently when dealing with malware passed through obfuscation tools. We illustrate this studying in detail an open source metamorphic software, making use of a hybrid framework to obtain the relevant features from binaries. We then provide an improved alternative solution based on adversarial risk analysis which we illustrate describe with an example. KEYWORDS: Adversarial Risk Analysis, Malware Obfuscation, Cybersecurity 1 INTRODUCTION The digital era is bringing along new global threats among which cybersecurity related ones emerge as truly worrisome, see for example the evolution of the Global Risks Map from the World Economic Forum (2017, 2018, 2019). Indeed, the operation of critical cyber infrastructures relies on components which could be cyber attacked, both incidentally and intentionally, suffering major performance degradation, Rao et al. (2016).