Abstract
Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detecting obfuscated and packed malware. Considering that the cause of a problem is often best understood by studying the structural aspects of a program like the mnemonics, instruction opcode, API Call, etc. In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable. Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). Experiments were conducted on four datasets using machine learning and deep learning approaches such as Support Vector Machine (SVM), Naïve Bayes, J48, Random Forest (RF), and XGBoost. In addition, we also evaluate the performance of the collection of deep neural networks like Deep Dense network, One-Dimensional Convolutional Neural Network (1D-CNN), and CNN-LSTM in classifying unknown samples, and we observed promising results using APIs and system calls. On combining APIs/system calls with static features, a marginal performance improvement was attained comparing models trained only on dynamic features. Moreover, to improve accuracy, we implemented our solution using distinct deep learning methods and demonstrated a fine-tuned deep neural network that resulted in an F1-score of 99.1% and 98.48% on Dataset-2 and Dataset-3, respectively.
Highlights
Malware or malicious code is harmful code injected into legitimate programs to perpetrate illicit intentions
Dataset-1 (VX-Dataset): A total of 2000 Portable Executables were collected which consists of 1000 malware samples gathered from sources VxHeaven (650) [35], User Agency (250), and Offensive Computing (100), and benign samples were collected from Windows XP System32 Folder (450), Windows7 System32 Folder (100), MikTex/Matlab Library (400), and Games (50);
We address the detection of malicious files using diverse datasets comprising of real and synthetic malware samples
Summary
Malware or malicious code is harmful code injected into legitimate programs to perpetrate illicit intentions. With the rapid growth of the Internet and heterogeneous devices connected over the network, the attack landscape has increased and has become a concern, affecting the privacy of users [1]. The primary source of infection, causing malicious programs to enter the systems without users’ knowledge. Freely downloadable software’s are a primary source of malware, which include freeware comprising of games, web browsers, free antivirus, etc. Financial transactions are performed using the Internet, these have caused huge financial losses for organizations and individuals. Malware writing has transformed into profit-making industries, attracting a large number of hackers. Current malware is broadly classified as polymorphic or metamorphic, and they remain undetected by a signature-based detector [2]
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