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However, some can use system processes to hide their existence, such as thread insertion backdoor (Section 2.1), and this method does not identify well. Some studies use the dynamic acquisition of logs and other information to capture backdoor behaviors for detection.
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The attacker will use the backdoor to perform a series of subsequent operations, which must leave traces on the victim’s host. We think that these are essential when judging the maliciousness. At the same time, the previous research did not involve a more detailed analysis and summary of function behavior of the backdoor. These features should have corresponding changes to the Python language. There are feature selections for programming language functions or programming features in the research methods, such as PHP language tags “”, “shell_exec ()” functions, etc. Simultaneously, due to the differences in programming languages, existing methods are not fully applicable to Python text. Most of the existing backdoor-related research is aimed at PHP or general-purpose webshells and has not considered other backdoor types. Nevertheless, there is little research on malicious Python code. Sufficient identification of backdoors to take further measures has become the current research’s focus in cyber security.Īs a concise, easy-to-read, and extensible programming language, Python has been widely used in large-scale project development while initially only writing automated scripts. The 2020 State of Malware Report released by Malwarebytes Labs shows that backdoors have continuously become the top ten common security threat’s categories among users and commercial products, and the proportion is increasing. Among them, the backdoor is an effective means for attackers to achieve the purpose of intrusion. However, once criminals utilize it, it will cause data leakage and property damage. With the rapid development of network technology in recent years, various applications have become the primary way to provide information services, significantly improving the convenience of people’s life. Covering most types of backdoors, some samples are obfuscated, the model achieves an accuracy of 97.70%, and the TNR index is as high as 98.66%, showing a good classification performance in Python backdoor detection. Finally, we introduce the Random Forest algorithm to build a classifier.
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Besides, the opcode sequence is used to represent code characteristics, such as TF-IDF vector and FastText classifier, to eliminate the influence of interference items. What is more, we consider the text’s statistical characteristics, including the information entropy, the longest string, etc., to identify the obfuscated Python code. The model summarizes the common functional modules and functions in the backdoor files and extracts the number of calls in the text to form sample features. This paper proposes a Python backdoor detection model named PBDT based on combined features. Language differences make the method not entirely applicable. Previous research work was mainly focused on numerous PHP webshells, with less research on Python backdoor files. Thus, it is urgent to take adequate measures to defend such attacks. It has posed a serious threat to network security. Backdoor is a means by which attackers can invade the system to achieve illegal purposes and damage users’ rights. Application security is essential in today’s highly development period.