Traditional commercial antivirus products usually rely on signature-based method, which needs a local signature database to store patterns extracted from malware by experts. The number of samples is too large, requiring a highly effective way to detect malwares.Ī large number of researches have studied methods for analyzing and detecting malware. The total number of malware samples increased 22% in the past four quarters to 670 million samples detected by McAfee Labs in 2017. For instance, 69,277,289 kinds of malicious objects (scripts, exploits, executable files, etc.) are detected by Kaspersky Lab in 2016. According to the recent study, the number of malicious samples is rapidly increasing.
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Nowadays, various kinds of software provide wealth resources for users but also bring a certain potential danger thus malware detection is always a highly concerned issue in computer security field. In addition, we also take malware family classification experiment on 9 malware families to compare MalNet with other related works, in which MalNet outperforms most of related works with 99.36% detection accuracy and achieves a considerable speed-up on detecting efficiency comparing with two state-of-the-art results on Microsoft malware dataset. The evaluation result shows that MalNet achieves 99.88% validation accuracy for malware detection. We perform experiments on more than 40,000 samples including 20,650 benign files collected from online software providers and 21,736 malwares provided by Microsoft.
Then MalNet uses CNN and LSTM networks to learn from grayscale image and opcode sequence, respectively, and takes a stacking ensemble for malware classification. Concretely, we first generate a grayscale image from malware file, meanwhile extracting its opcode sequences with the decompilation tool IDA. In this paper, we propose MalNet, a novel malware detection method that learns features automatically from the raw data. Recent researches mainly use machine learning based methods heavily relying on domain knowledge for manually extracting malicious features. Malware detection plays a crucial role in computer security.
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