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Critical ops hack android 0.6.05/9/2023 Thus, how to reduce the cost of artificial feature engineering and how to extract useful information from the raw data and let the model achieve features of self-learning to improve the accuracy and efficiency for malware detection are our main motivations. To deal with this, a great deal of expert knowledge is required to catch up the changing malware environment, and the original well-designed features may not be applicable to a new malware family (a malware family refers to a malware variants group with homogeneous attack behaviors), resulting in heavy and inefficient feature engineering work. But a serious problem is that malware is constantly being created, updated, and changed. These features are used to train a classification machine learning model and finally make the classification for a new file sample. However, many of them heavily rely on the relevant domain knowledge for malware analysis and artificial features extraction. ![]() Meanwhile, malware uses packing technologies to prevent reverse engineering which leads to high costs for static analysis.Īt present, several machine learning methods are paid the most attention for solving the above problems and have been applied to malware detection in the industry. Attackers can deliberately make various changes on malwares, hence static analysis is difficult to capture the characteristics of malware. However, various encryption and obfuscation techniques are the major issue for static analysis. For static analysis, a great strength is that it can achieve rapid detection for massive malwares. At the same time, it is time-consuming for malware behavior monitoring since some malicious behaviors hide for a long time before attack. Though dynamic analysis does not require complex reverse engineering, it needs to simulate the operation environment for malwares, which is difficult to arouse all malware behaviors. Hence, many different malware detection approaches with machine learning technology have been proposed in recent years, such as static analysis which learns statistical characteristics like API calls, -grams, and so on or dynamic behavior analysis. However, this approach has great limitations since specific minor changes to malware can change the signature, so more and more malware could easily evade signature-based detection by encrypting, obfuscating, or packing. 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. 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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