Research and Implementation of Security Vulnerability Detection in Application System of WEB Static Source Code Analysis Based on JAVA

Yuan, Hui; Zheng, Lei; Liang, Dong; Peng, Xiangli; Zhuang, Yan; Deng, Guoru

doi:10.1007/978-3-030-15235-2_66

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…But the random token detection mechanism should be revised due to errors in random token detection. Yuan et al [14] investigate vulnerabilities and analyze the common vulnerabilities in web applications. Anbiya et al [15] in the paper will present an approach using static analysis with lexical analysis techniques for reducing an expert's knowledge.…”

Section: Related Workmentioning

confidence: 99%

Capable of Classifying the Tuples with Wireless Attacks Detection Using Machine Learning

Islam

Allayear

2022

Intelligent Computing Systems

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A wireless attack is a malicious action against wireless systems and wireless networks. In the last decade of years, wireless attacks are increasing day by day and it is now a very big problem for modern wireless communication systems. In this paper, the author's used the Aegean Wi-Fi Intrusion Dataset (AWID3). There are two versions of this dataset, one is over 200 million tuples of full data and one is 1.8 million tuples of reduced data. As our dataset has millions of tuples and over 100 columns, it is easy to become overwhelmed because of its size. The authors used the reduced version and predicted if an attack was one of four types using the k-nearest-neighbors classifier. All of the attack types we used had a distribution that highly favored the non-attack class. Our best results were for the attack "arp'' type where we attained the best accuracy with recall. The author's primary goal of the paper was to attain the highest accuracy possible when creating a model that is capable of classifying the 4 attack types and detecting and classifying wireless attacks using Machine Learning models on the AWID3 dataset. One of the goals that supported this main objective was determining a way to avoid the curse of dimensionality.

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Section: Related Workmentioning

confidence: 99%

Capable of Classifying the Tuples with Wireless Attacks Detection Using Machine Learning

Islam

Allayear

2022

Intelligent Computing Systems

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“…That can improve the recognition rate of the detection system. Yuan et al [33] investigate vulnerabilities analyze common web application vulnerabilities, research the basic characteristics of these vulnerabilities in depth, and comprehend the principles and remedies to these weaknesses. Moustafa et al [34] suggest a method to automatically extract relevant features from web data and network traffic data to improve the effectiveness of threat detection techniques.…”

Section: Related Workmentioning

confidence: 99%

DDoS Attack Preventing and Detection with the Artificial Intelligence Approach

Islam

Jabiullah

Abid

2022

Intelligent Computing Systems

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DDoS attacks are a major Internet security concern with this large number of customers. Each attack sends a service request to a certain server, which limits the server's capacity to provide normal services. Since the attackers use legitimate packages and alter their package information, traditional methods are not very effective. The assault on DDoS is one of the most potent Internet hacking techniques. The hacker's basic weapon to take down and crash websites during these sorts of assaults is network trafficking. There are different sub-categories, each category explains how a hacker attempts to enter the network. In this paper, we define the DDoS attacks detection method based on artificial intelligence and explored with more than 96-percent accuracy a technique to detect a DDoS attacks assault danger using artificial intelligence (A.I). In addition to a secure or healthy network, authors have identified 7 separate sub-categories of DDoS attacks.

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Research and Implementation of Security Vulnerability Detection in Application System of WEB Static Source Code Analysis Based on JAVA

Cited by 2 publications

References 10 publications

Capable of Classifying the Tuples with Wireless Attacks Detection Using Machine Learning

Capable of Classifying the Tuples with Wireless Attacks Detection Using Machine Learning

DDoS Attack Preventing and Detection with the Artificial Intelligence Approach

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