PBDT: Python Backdoor Detection Model Based on Combined Features

Fang, Yong; Xie, Mingyu; Huang, Cheng

doi:10.1155/2021/9923234

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…are included in static features. Due of the inherent limits of static features, their detection methods are frequently integrated with text information [12][13][14]. In addition, some research approaches for PHP language [15][16][17] focus on using machine learning methods to classify the opcode obtained after processing the PHP language directly.…”

Section: Methods Of Webshell Detectionmentioning

confidence: 99%

BERT-Embedding-Based JSP Webshell Detection on Bytecode Level Using XGBoost

Xing

Zhang

et al. 2022

Security and Communication Networks

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Webshell is a malicious program that might result in data theft, file modification, or other damaging behaviors once uploaded to a server. Detecting webshells is a key security concern for website administrators. In recent years, techniques such as obfuscation and encryption have been deployed on webshell technology, and classic detection approaches such as static feature matching are gradually underperforming on webshell detection. Meanwhile, there are variations between languages such as JSP and PHP, and researchers have proposed webshell detection methods primarily for languages such as PHP. At the same time, there are fewer detection techniques for JSP webshells. In this case, a detection approach for the JSP webshells is needed. This paper provides a novel webshell detection model for the JSP language. The model’s fundamental premise is that it introduces the BERT-based word vector extraction method, which has been shown in experiments to be more effective at detecting obfuscation, encryption, and other means of evading detection than the traditional Word2vec word vector extraction method. Meanwhile, we introduce the XGBoost algorithm as the model classifier. The experimental results reveal that present model has achieved 99.14% accuracy, 98.68% precision, 98.03% recall, and 98.35% f1 score, and the overall effect is better than the already existing JSP webshell detection approaches.

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Section: Methods Of Webshell Detectionmentioning

confidence: 99%

BERT-Embedding-Based JSP Webshell Detection on Bytecode Level Using XGBoost

Xing

Zhang

et al. 2022

Security and Communication Networks

Self Cite

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“…e emergence of advanced information technology [30,31] makes the design and development of network public opinion communication management system possible. Based on Python tools [32][33][34][35], the overall framework of the network public opinion communication management system is designed as shown in Figure 12.…”

Section: Network Public Opinion Communication Management System Designmentioning

confidence: 99%

Simulation Model on Network Public Opinion Communication Model of Major Public Health Emergency and Management System Design

Wan

Plewczyński

et al. 2022

Scientific Programming

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In order to identify the communication law of network public opinion in major public health emergency, a multifactor communication model based on multiagent modeling is proposed. Based on the SEIR model and NetLogo simulation analysis, the model integrates a variety of network public opinion communication characteristics and extends the existing single network public opinion communication model. Based on these studies, a network public opinion communication management system is designed and developed with Python tools. Case analysis and simulation results show that this model can better simulate the evolution trend of network public opinion communication in major public health emergency; the key communication channels of public opinion play an important role in the evolution of network public opinion, which can control and guide the network public opinion of major public health emergency from the three communication influencing factors of total number, initial number, and communication cycle. Communication cycle refers to the number of days in the process of network public opinion dissemination of major public health emergencies.

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“…A previous study proposed a Python backdoor detection model that could detect obfuscated malware samples represented by its statistical text features and opcode sequence. The result provides 97.7% detection accuracy using the Random Forest classifier [11]. The security challenges in the Android ecosystem are also growing despite the introduction of advanced anti-malware tools [12].…”

Section: Introductionmentioning

confidence: 99%

Evading Antivirus Software Detection Using Python and PowerShell Obfuscation Framework

Aditiawarman,

Dody,

Mantoro

et al. 2023

matrik

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Avoiding antivirus detection in penetration testing activities is tricky. The simplest, most effective, and most efficient way is to disguise malicious code. However, the obfuscation process will also be very complex and time-consuming if done manually. To solve this problem, many tools or frameworks on the internet can automate the obfuscation process, but how effective are obfuscation tools to avoid antivirus detection are. This study aimed to provide an overview of the effectiveness of the obfus- cation framework in avoiding antivirus detection. This study used experimental design to test and determine the effectiveness of the payload obfuscation process. The first step was generating Python and PowerShell payloads, followed by the obfuscation process. The results showed that by using the right method of obfuscation, malware could become completely undetectable. The automatic obfus- cation process also did not deteriorate the malware’s function. It was proven that the malware could run and open a connection on the server. These findings required more Python obfuscator techniques to determine the effectiveness of the obfuscated payload on the target machines using both static and dynamic analysis

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PBDT: Python Backdoor Detection Model Based on Combined Features

Cited by 7 publications

References 24 publications

BERT-Embedding-Based JSP Webshell Detection on Bytecode Level Using XGBoost

BERT-Embedding-Based JSP Webshell Detection on Bytecode Level Using XGBoost

Simulation Model on Network Public Opinion Communication Model of Major Public Health Emergency and Management System Design

Evading Antivirus Software Detection Using Python and PowerShell Obfuscation Framework

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