{"id":126041,"date":"2021-08-12T17:50:48","date_gmt":"2021-08-13T00:50:48","guid":{"rendered":"\/blogs\/?p=126041"},"modified":"2025-06-06T01:36:46","modified_gmt":"2025-06-06T08:36:46","slug":"the-rise-of-deep-learning-for-detection-and-classification-of-malware","status":"publish","type":"post","link":"https:\/\/www.mcafee.com\/blogs\/other-blogs\/mcafee-labs\/the-rise-of-deep-learning-for-detection-and-classification-of-malware\/","title":{"rendered":"The Rise of Deep Learning for Detection and Classification of Malware"},"content":{"rendered":"

C<\/span>o-written by\u00a0Catherine Huang, Ph.D.\u00a0and\u00a0Abhishek\u00a0Karnik<\/span>\u00a0<\/span><\/p>\n

Artificial Intelligence (AI)\u00a0continues to evolve\u00a0and\u00a0has made huge\u00a0progress\u00a0over the last\u00a0decade.\u00a0AI\u00a0shapes our daily lives. Deep learning is\u00a0a\u00a0subset\u00a0of\u00a0techniques\u00a0in\u00a0AI that extract patterns from data using neural networks. Deep learning has been applied to image segmentation, protein structure, machine translation, speech recognition and robotics. It has outperformed human champions in\u00a0the game of\u00a0<\/span>Go<\/span><\/i>. In recent years, deep learning has been applied to malware analysis. Different types of deep learning algorithms, such as convolutional neural networks (CNN), recurrent neural networks\u00a0and\u00a0Feed-Forward networks, have been applied to\u00a0a\u00a0variety of use cases in malware analysis using bytes sequence, gray-scale image, structural entropy, API call sequence, HTTP traffic and network behavior.\u00a0<\/span>\u00a0<\/span><\/p>\n

Most traditional machine learning malware classification and detection approaches rely on handcrafted features.\u00a0These features are selected based on experts\u00a0with domain knowledge.\u00a0Feature engineering\u00a0can be a\u00a0very time-consuming process,\u00a0and handcrafted features may not generalize well to novel malware.\u00a0In this blog, we\u00a0briefly\u00a0describe how we apply CNN on raw bytes for malware detection and classification\u00a0in real-world data.<\/span>\u00a0<\/span><\/p>\n

    \n
  1. \n

    CNN on Raw Bytes\u00a0<\/strong><\/h2>\n<\/li>\n<\/ol>\n

    The motivation for applying deep learning is to identify new patterns in raw bytes. The novelty of this work is threefold. First, there is no domain-specific feature extraction and pre-processing. Second, it is an end-to-end deep learning approach. It can also perform end-to-end classification. And it can be a feature extractor for feature augmentation. Third, the explainable AI (XAI) provides insights on the CNN decisions and help human identify interesting patterns across malware families. As shown in Figure 1, the input is only raw bytes and labels. CNN performs representation learning to automatically learn features and classify malware. <\/span>\u00a0<\/span><\/p>\n

    2. Experimental Results\u00a0<\/strong><\/h2>\n

    For the purposes of our experiments with malware detection, we first gathered 833,000\u00a0distinct binary samples (Dirty and Clean)\u00a0across multiple families, compilers\u00a0and varying \u201cfirst-seen\u201d time periods.\u00a0There were\u00a0large groups of samples from common families although they did utilize varying packers, obfuscators.\u00a0Sanity checks were performed to discard samples that were corrupt, too large or too small, based on our experiment.\u00a0From\u00a0samples\u00a0that\u00a0met our sanity check criteria, we extracted raw bytes from these samples and utilized them for\u00a0conducting\u00a0multiple\u00a0experiments.\u00a0The data\u00a0was\u00a0randomly\u00a0divided\u00a0into\u00a0a\u00a0training and\u00a0a\u00a0test set\u00a0with\u00a0an\u00a080%\u00a0\/\u00a020% split.\u00a0We utilized this data set to run\u00a0the three\u00a0experiments.\u00a0<\/span>\u00a0<\/span><\/p>\n

    In our first experiment,\u00a0raw bytes from\u00a0the\u00a0833,000\u00a0samples\u00a0were fed to\u00a0the\u00a0CNN\u00a0and\u00a0the\u00a0performance accuracy\u00a0in terms of\u00a0area under\u00a0receiver\u00a0operating\u00a0curve (ROC)\u00a0was 0.9953.\u00a0<\/span>\u00a0<\/span><\/p>\n

    One observation with the initial run was that, after raw byte extraction from the 833,000 unique samples, we did find duplicate raw byte entries. This was primarily due to malware families that utilized hash-busting as an approach to polymorphism. Therefore, in our second experiment, we deduplicated the extracted raw byte entries. This reduced the raw byte input vector count to 262,000 samples. The test area under ROC was 0.9920.<\/span>\u00a0<\/span><\/p>\n

    In our third experiment, we attempted multi-family malware classification. We took a subset of 130,000 samples from the original set and labeled 11 categories \u2013 the 0<\/span>th<\/span>\u00a0were bucketed as Clean, 1-9\u00a0of which were malware families,<\/span>\u00a0<\/span>and\u00a0the\u00a010<\/span>th<\/span> were bucketed as Others.\u00a0Again, these\u00a011 buckets contain samples\u00a0with varying packers and compilers.\u00a0We\u00a0performed\u00a0another\u00a080\u00a0\/\u00a020% random split for the training set and test set.\u00a0For this\u00a0experiment, we\u00a0achieved\u00a0a\u00a0test accuracy\u00a0of\u00a00.9700. The training and test time on one GPU\u00a0was 26 minutes.\u00a0<\/span>\u00a0<\/span><\/p>\n

    3. Visual Explanation\u00a0<\/strong><\/h2>\n

    To understand the CNN training process, we performed a visual analysis for the CNN training. Figure 2 shows the t-Distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA) for before and after CNN training. We can see that after training, CNN is able to extract useful representations to capture characteristics of different types of malware as shown in different clusters. There was a good separation for most categories, lending us to believe that the algorithm was useful as a multi-class classifier.<\/span>\u00a0<\/span><\/p>\n

    We then performed XAI to understand CNN\u2019s decisions. Figure 3 shows XAI heatmaps for one sample of Fareit and one sample of Emotet. The brighter the color is the more important the bytes contributing to the gradient activation in neural networks. Thus, those bytes are important to CNN\u2019s decisions. We were interested in understanding the bytes that weighed in heavily on the decision-making and reviewed some samples manually.<\/span>\u00a0<\/span><\/p>\n

    4. Human analysis<\/span>\u00a0to understand the ML decision and XAI\u00a0<\/span><\/span>\u00a0<\/span><\/strong><\/h2>\n

    To verify if the CNN can learn new patterns,\u00a0<\/span>we fed a<\/span>\u00a0few\u00a0<\/span>never before seen<\/span>\u00a0samples to the CNN<\/span>,<\/span>\u00a0and requested a\u00a0<\/span>human expert to\u00a0<\/span>verify the\u00a0<\/span>CNN\u2019s decision<\/span>\u00a0<\/span>on<\/span>\u00a0<\/span>some<\/span>\u00a0random\u00a0<\/span>samples<\/span>. The human\u00a0<\/span>analysis\u00a0<\/span>verified<\/span>\u00a0<\/span>that\u00a0<\/span>the\u00a0<\/span>CNN\u00a0<\/span>was able to\u00a0<\/span>correctly\u00a0<\/span>identify\u00a0<\/span>many\u00a0<\/span>malware families<\/span>.\u00a0<\/span>In some cases, it identified samples accurately before the to<\/span>p 15\u00a0<\/span>AV\u00a0<\/span>vendors based on our internal tests<\/span>. Figure 4 shows a subset of samples that belong to the\u00a0<\/span>Nabucur<\/span>\u00a0family that\u00a0<\/span>were correctly ca<\/span>tegorized by the CNN despite having no\u00a0<\/span>vendor detection<\/span>\u00a0at that point in time<\/span>.\u00a0<\/span>It<\/span>\u2019s<\/span>\u00a0<\/span>also\u00a0<\/span>interesting to note that o<\/span>ur results\u00a0<\/span>showed that the CNN was able to\u00a0<\/span>currently\u00a0<\/span>categorize\u00a0<\/span>malware\u00a0<\/span>samples\u00a0<\/span>across families utilizing common packers<\/span>\u00a0into an accurate family\u00a0<\/span>bucket<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n

    We ran domain analysis on\u00a0the same\u00a0sample complier VB files. As shown in Figure 5, CNN\u00a0was able to\u00a0identify\u00a0two samples\u00a0of a\u00a0threat family before other vendors. CNN agreed with MSMP\/other vendors on two samples.\u00a0In this experiment, the\u00a0CNN\u00a0incorrectly\u00a0identified one sample as Clean.\u00a0<\/span>\u00a0<\/span><\/p>\n

    We asked a human expert to inspect an XAI heatmap and verify if those bytes in bright color are associated with the malware family classification. Figure 6 shows one sample which belongs to the Sodinokibi family. The bytes identified by the XAI (c3 8b 4d 08 03 d1 66 c1) are interesting because the byte sequence belongs to part of the Tea decryption algorithm. This indicates these bytes are associated with the malware classification, which confirms the CNN can learn and help identify useful patterns which humans or other automation may have overlooked. Although these experiments were rudimentary, they were indicative of the effectiveness of the CNN in identifying unknown patterns of interest. <\/span>\u00a0<\/span><\/p>\n

    In summary, the experimental results and visual explanations demonstrate that CNN can automatically learn PE raw byte representations. CNN raw byte model can perform end-to-end malware classification. CNN can be a feature extractor for feature augmentation. The CNN raw byte model has the potential to identify threat families before other vendors and identify novel threats. These initial results indicate that CNN\u2019s can be a very useful tool to assist automation and human researcher in analysis and classification. Although we still need to conduct a broader range of experiments, it is encouraging to know that our findings can already be applied for early threat triage, identification, and categorization which can be very useful for threat prioritization. <\/span>\u00a0<\/span><\/p>\n

    We believe that McAfee\u2019s ongoing AI research, such as deep learning-based approaches, leads the security industry to tackle the evolving threat landscape, and we look forward to continuing to share our findings in this space with the security community.<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

    Co-written by\u00a0Catherine Huang, Ph.D.\u00a0and\u00a0Abhishek\u00a0Karnik\u00a0 Artificial Intelligence (AI)\u00a0continues to evolve\u00a0and\u00a0has made huge\u00a0progress\u00a0over the last\u00a0decade.\u00a0AI\u00a0shapes our daily lives. Deep learning is\u00a0a\u00a0subset\u00a0of\u00a0techniques\u00a0in\u00a0AI that…<\/p>\n","protected":false},"author":695,"featured_media":126083,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[442],"tags":[],"coauthors":[4136],"class_list":["post-126041","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-mcafee-labs"],"acf":[],"yoast_head":"\nThe Rise of Deep Learning for Detection and Classification of Malware | McAfee Blog<\/title>\n<meta name=\"description\" content=\"Co-written by\u00a0Catherine Huang, Ph.D.\u00a0and\u00a0Abhishek\u00a0Karnik\u00a0 Artificial Intelligence (AI)\u00a0continues to evolve\u00a0and\u00a0has made huge\u00a0progress\u00a0over the\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" 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