@Article{iasc.2021.016933,
AUTHOR = {Zhiguo Chen, Xiaorui Zhang, Sungryul Kim},
TITLE = {A Learning-based Static Malware Detection System with Integrated Feature},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {27},
YEAR = {2021},
NUMBER = {3},
PAGES = {891--908},
URL = {http://www.techscience.com/iasc/v27n3/41666},
ISSN = {2326-005X},
ABSTRACT = {The rapid growth of malware poses a significant threat to the security of computer systems. Analysts now need to examine thousands of malware samples daily. It has become a challenging task to determine whether a program is a benign program or malware. Making accurate decisions about the program is crucial for anti-malware products. Precise malware detection techniques have become a popular issue in computer security. Traditional malware detection uses signature-based strategies, which are the most widespread method used in commercial anti-malware software. This method works well against known malware but cannot detect new malware. To overcome the deficiency of the signature-based approach, we proposed a static malware detection system using data mining techniques to identify known and unknown malware by comparing the malware and benign programs’ profiles with real-time response with low false-positive ratio. The proposed system includes a sample labeling module, a feature extraction module, a pre-processing module, and a decision module. The sample labeling module used the VirusTotal to correctly label the collected samples. The feature extraction module statically extracts a set of header information, section entropy, APIs, and section opcode <i>n</i>-grams. The pre-processing module is primarily based on the PCA algorithm used to reduce the dimensionality of the features, thus reducing the overhead costs of computation. The decision module uses various machine-learning algorithms such as K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) to build the detection model for judging whether the program is a benign program or malware. The experimental results indicate our proposed system can achieve 99.56% detection accuracy and 99.55% f1-score on the extracted 79 features using the XGBoost algorithm, and it has the potential for real-time large-scale malware detection tasks.},
DOI = {10.32604/iasc.2021.016933}
}