# Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning
###### Abstract  
Cyber security is used to protect and safeguard computers and various networks from ill-intended digital threats and attacks. It is getting more difficult in the information age due to the explosion of data and technology. There is a drastic rise in the new types of attacks where the conventional signature-based systems cannot keep up with these attacks. Machine learning seems to be a solution to solve many problems, including problems in cyber security. It is proven to be a very useful tool in the evolution of malware detection systems. However, the security of AI-based malware detection models is fragile. With advancements in machine learning, attackers have found a way to work around such detection systems using an adversarial attack technique. Such attacks are targeted at the data level, at classifier models, and during the testing phase. These attacks tend to cause the classifier to misclassify the given input, which can be very harmful in real-time AI-based malware detection. This paper proposes a framework for generating the adversarial malware images and retraining the classification models to improve malware detection robustness. Different classification models were implemented for malware detection, and attacks were established using adversarial images to analyze the model's behavior. The robustness of the models was improved by means of adversarial training, and better attack resistance is observed.  
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the modern-day artillery used by attackers to hurt unsuspecting consumers and steal their data by violating their privacy [11; 12]. Malware detection systems have progressed from detecting binary files to detecting executables. However, such attacks can also target the detection system [13].  
Machine learning models learn to classify malware by a learning process in which the model is provided with a training data set and testing data set. The model learns with the help of the training dataset [14; 15]. There is a huge drawback to this approach. The attackers can affect the learning process and circumvent the classifiers, helping them to bypass the detection systems. It also affects the privacy preservation of the data in such systems [9; 16]. Along with machine learning, deep learning techniques have also been applied to improve malware detection's accuracy [17]. DL models such as LSTM, RNN, CNN have been implemented in the literature [18; 19].  
Adversarial attacks are these types of attacks, and adversarial modeling (AML) is the process of training a model to withstand them without compromising its overall effectiveness (adversarial machine learning) [20; 21]. It has been extensively tested and applied in areas such as spam detection and image classification. In domains such as malware detection, its exploration is currently insufficient [22; 23]. Robust malware detection is not only needed for standalone systems, but also IoT-based systems [24; 25] and Android systems, which also require an aggressive malware management framework. Lots of attacks happen at the sensor level, and also in Android-based devices [26].  
In this work, the authors examined various state-of-the-art ML and DL architectures and their optimization for malware detection in this research work, and how those algorithms may be made more robust by attacking and training them using adversarial data.