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WebShell Attack Detection Based on a Deep Super Learner
2020
Symmetry
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However, conventional WebShell detection methods can no longer cope with complex and flexible variations of WebShell attacks. Therefore, this paper proposes a deep super learner for attack detection. ...
Finally, we use a deep super learner to detect WebShell. The experimental results show that this algorithm can effectively detect WebShell, and its accuracy and recall are greatly improved. ...
In Toward a Deep Learning Approach for Detecting PHP Webshell [7] , Yara rule technology is used to convert the PHP source code into opcodes to determine whether a file is malicious code. ...
doi:10.3390/sym12091406
fatcat:kfrwmxvhonci7g7lzm2yg6pfja
Automatic and Accurate Detection of Webshell Based on Convolutional Neural Network
[chapter]
2019
Communications in Computer and Information Science
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Based on the variability of Webshells and the vulnerability of detection methods, this paper proposed a model that used deep learning to detect and implements the automatic identification of Webshells. ...
The deep learning model does not require complicated artificial feature engineering, and the modeled features trained through model learning can also allow the attacker to avoid targeted bypassing in Webshell ...
Literature [5] proposed a Webshell detection technology based on semantic analysis. ...
doi:10.1007/978-981-13-6621-5_6
fatcat:km2m73q3jnbllpolcu7uwimlva
A New Method for WebShell Detection Based on Bidirectional GRU and Attention Mechanism
2022
Security and Communication Networks
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WebShell escape technology is changing with each passing day, and the traditional method based on feature matching is difficult to accurately detect. ...
The model not only detects the PHP-type WebShell but also has a good performance on the WebShell written in JSP, ASPX, or ASP languages. ...
In view of this, this study proposes a WebShell detection method based on the deep learning algorithm, which can accurately identify multiple types of WebShell. ...
doi:10.1155/2022/3434920
fatcat:q5wrfdn2kbeg7g3caewzylswyq
Research on WebShell Detection Method Based on Regularized Neighborhood Component Analysis (RNCA)
2021
Symmetry
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In order to solve this problem, a method of WebShell detection based on regularized neighborhood component analysis (RNCA) is proposed. ...
The variant, encryption, and confusion of WebShell results in problems in the detection method based on feature selection, such as poor detection effect and weak generalization ability. ...
detection method based on contrast deep learning. ...
doi:10.3390/sym13071202
fatcat:bq7vfleaybbnhatxjpjd2bbvtq
BERT-Embedding-Based JSP Webshell Detection on Bytecode Level Using XGBoost
2022
Security and Communication Networks
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on webshell detection. ...
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 ...
Machine learning and deep learning methods [25] [26] [27] are used in webshell detection. Among the deep learning methods used, CNN [28] [29] [30] is often used for webshell detection. Tian et al. ...
doi:10.1155/2022/4315829
fatcat:bk75fhsyyffgvinha75zc4mlfm
Webshell Detection Based on Executable Data Characteristics of PHP Code
2021
Wireless Communications and Mobile Computing
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The wide use of obfuscation and encryption technologies has greatly increased the difficulty of webshell detection. ...
To this end, we propose a novel webshell detection model leveraging the grammatical features extracted from the PHP code. ...
Moreover, since webshell code obfuscation and encryption technology continues to mature, detection methods based on regular expressions can be easily bypassed. ...
doi:10.1155/2021/5533963
fatcat:p7f3acivajbqjp4hdcqza4v3ma
A Novel Machine Learning based Hybrid Model for Webshell Detection
2021
International Journal for Research in Applied Science and Engineering Technology
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These attacks strongly escalate the requisite for Machine Learning based detection. ...
Behavioral based Detection methods will be discussed to take advantage from ML algorithms so as to frame social-based web shell recognition and classification model. ...
Different detection methods are widely employed to mitigate webshell attacks [3] [4] . This model detects based on behaviors related to network using TCP dump data. ...
doi:10.22214/ijraset.2021.35644
fatcat:zoeax7ebpbc7fcq3rb2qpfn7lq
WTA: A Static Taint Analysis Framework for PHP Webshell
2021
Applied Sciences
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In this paper, we propose a static webshell detection method based on taint analysis, which realizes accurate taint analysis based on ZendVM. ...
Based on this method, we constructed a taint analysis prototype system named WTA and evaluated it with a benchmark dataset by comparing its performance with popular webshell detection tools. ...
In addition, there are many research works on webshell detection methods based on machine learning and neural networks, such as cnn-webshell [9] and Yong e al.' ...
doi:10.3390/app11167763
fatcat:vl7foguzcvfpfkcvii6unmcpji
Applications of Machine Learning in Cyber Security
2019
International Journal of Advanced Research in Computer and Information Security
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Machine learning is one of the latest trends models and methods that can be implemented and the users can get good results. ...
Hence, people need to think of these problems in a different manner and also to solve these technical problems by using these advanced technologies. ...
But today it had developed further to the extent that the machines are learning based on the data and acting based on the suggestions or the decisions that were already taken in the previous models. ...
doi:10.21742/ijacis.2019.3.1.01
fatcat:5ft3ihygpbh6himpzuae6slume
PBDT: Python Backdoor Detection Model Based on Combined Features
2021
Security and Communication Networks
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This paper proposes a Python backdoor detection model named PBDT based on combined features. ...
Previous research work was mainly focused on numerous PHP webshells, with less research on Python backdoor files. Language differences make the method not entirely applicable. ...
Compared with the dynamic detection which requires high detection environment and the deep learning that consumes a lot of resources, the static detection scheme based on machine learning proposed in this ...
doi:10.1155/2021/9923234
fatcat:rqctdyewdbhjlhwknpuukrcxj4
Large Language Models are Few-shot Generators: Proposing Hybrid Prompt Algorithm To Generate Webshell Escape Samples
[article]
2024
arXiv
pre-print
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2024 pre-print arXiv:2402.07408v1 fatcat:2jbkluopdndf3hljwgugnwekvu
escape sample generation and artificial intelligence (AI)-based webshell detection. ...
GPT-4 model on VirusTotal detection engine) and (Survival Rate 54.98% with GPT-4 model). ...
This idea is also in line with the logical process of human learning and cognition, e.g., "from shallow to deep," to help LLM better learn the features of the methods. ...
arXiv:2402.07408v2
fatcat:46j2kpdj7jdvph7g7mlio4jwdq
A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks
2022
Sensors
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Today's advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. ...
The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. ...
Tests carried out on machine learning models for webshell detection on PHP scripts only. ...
doi:10.3390/s22052017
pmid:35271163
pmcid:PMC8915055
fatcat:6khxq7pkyzgifdos7ifcyqsmgi
Classification of Abnormal Traffic in Smart Grids Based on GACNN and Data Statistical Analysis
2021
Security and Communication Networks
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For example, some malicious software usually uses encryption technology or tunnel technology to bypass firewalls, intrusion detection systems, etc., thereby posing a serious threat to the information security ...
In order to solve the problem of accurate classification of power network traffic, this paper proposes a method of convolutional neural network based on genetic algorithm optimization (GACNN) and data ...
strong versatility. ere are three main methods for detecting network traffic anomalies based on deep learning: deep Boltzmann machine [33, 34] , stacked autoencoder [35, 36] , and CNN [16, 37] . ...
doi:10.1155/2021/9927325
fatcat:snrzsqg2hrdndd2mlrt6q34om4
Table of contents
2018
2018 IEEE Third International Conference on Data Science in Cyberspace (DSC)
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Sentiment Analysis Based on Deep Learning and Its Application in Screening for Perinatal Depression
451
Yong Chen (National University of Defense Technology, China), Bin Zhou
(National University of ...
Detection Based on Random Forest-Gradient Boosting Decision Tree AlgorithmPrivacy-Aware Personal Information Propagation Management in Social Networks169 Yu Wu (Shanghai Jiaotong University, China) ...
doi:10.1109/dsc.2018.00004
fatcat:24ndnf5myfabznfcwvvsdozjne
Comparative Study of Datasets used in Cyber Security Intrusion Detection
2020
International Journal of Scientific Research in Computer Science Engineering and Information Technology
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, adversarial attacks became one crucial security threat to several deep learning applications in today's world Deep learning techniques became the core part for several cyber security applications like ...
In recent years, deep learning frameworks are applied in various domains and achieved shows potential performance that includes malware detection software, self-driving cars, identity recognition cameras ...
Khoshgoftaar, "Survey on deep learning with class imbalance,'' J. Big Data, vol. 6, no. 1, p. 27, 2019. [5]. A. Ali, S. M. Shamsuddin, and A. L. ...
doi:10.32628/cseit2063103
fatcat:xz7imau25bhpvmg2jdmhz7e4py
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