Detection of Malicious Code Variants Based on Deep Learning

By Zhihua Cui, Fei Xue, Xingjuan Cai, Yang Cao, Gai-ge Wang, Jinjun Chen. IEEE Transactions on Industrial Informatics, 2018

514 citations recorded by the source corpus.

With the development of the Internet, malicious code attacks have increased exponentially, with malicious code variants ranking as a key threat to Internet security. The ability to detect variants of malicious code is critical for protection against security breaches, data theft, and other dangers. Current methods for recognizing malicious code have demonstrated poor detection accuracy and low detection speeds. This paper proposed a novel method that used deep learning to improve the detection of malware variants. In prior research, deep learning demonstrated excellent performance in image recognition. To implement our proposed detection method, we converted the malicious code into grayscale images. Then, the images were identified and classified using a convolutional neural network (CNN) that could extract the features of the malware images automatically. In addition, we utilized a bat algorithm to address the data imbalance among different malware families. To test our approach, we conducted a series of experiments on malware image data from Vision Research Lab. The experimental results demonstrated that our model achieved good accuracy and speed as compared with other malware detection models.

Subfields: Computer Networks and Communications, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/47de0a40bff225908a19247d2e28503c1a74e6e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Shin Yoo
2. Mubashir Husain Rehmani
3. Alexandre Mouradian

In Software

1. Shin Yoo
2. Robert Feldt
3. Mark Harman
4. Phil McMinn

In Signal Processing

1. Shin Yoo
2. Robert Feldt
3. Muneeb Ul Hassan
4. Jinjun Chen
5. Jinhan Kim
6. Mubashir Husain Rehmani

Citations in the corpus

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• Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios (2020, methodology): “In this section, an improved K-means clustering with optimized centroids by Cuckoo search [44], [45], [46] and timeaware is designed to improve the classic CF models.”
• Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, background): “However, preserving individual privacy along with extracting data is one of the most challenging task of future machine learning algorithms [341].”
• Guiding Deep Learning System Testing Using Surprise Adequacy (2018, background): “Based on their capability to match or even surpass human performance, DL systems are increasingly being adopted as part of larger systems in both safety and security critical domains such as autonomous driving [6], [10], and malware detection [12].”