Research on Network Attack and Defense Based on Artificial Intelligence Technology

Li, Mingxuan; Yang, Zhushi; Zhong, Jinsong; Ling, He; Teng, Yangxin

doi:10.1109/itnec48623.2020.9085100

Cited by 2 publications

(2 citation statements)

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“…It is one of the most efficient security testing approaches. Since Barton Miller presented the idea of fuzzing testing in 1988, after more than two decades of study and development, and with the introduction of new technological approaches such as feedback-based grey-box, taint analysis, and artificial intelligence [11,12], testing technology has been enhanced. Numerous automated testing tools, such as PROTOS, SPIKE, Sulley, Peach3, AFL, etc., have evolved as a result of constant development and promotion [13]; they include PROTOS, SPIKE, Sulley, Peach3, AFL, etc.…”

Section: Grey-box Fuzzingmentioning

confidence: 99%

Optimization Research of Directed Fuzzing Based on AFL

Feng

Liu

2022

Electronics

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Fuzz testing is the process of testing programs by continually producing unique inputs in order to detect and identify security flaws. It is often used in vulnerability mining. The most prevalent fuzzing approach is grey-box fuzzing, which combines lightweight code instrumentation with data-feedback-driven generation of fresh program input seeds. AFL (American Fuzzy Lop) is an outstanding grey-box fuzzing tool that is well known for its quick fork server execution, dependable genetic algorithm, and numerous mutation techniques. AFLGO proposes and executes power scheduling based on a simulated annealing process for a more appropriate energy allocation to seeds, however it is neither reliable nor successful. To tackle this issue, we offer an energy-dynamic scheduling strategy based on the algorithm of the fruit fly. Adjusting the energy of the seeds dynamically controls the production of test cases. The findings demonstrate that the approach suggested in this research can test the target region more rapidly and thoroughly and has a high application value for patch testing and vulnerability replication.

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Section: Grey-box Fuzzingmentioning

confidence: 99%

Optimization Research of Directed Fuzzing Based on AFL

Feng

Liu

2022

Electronics

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“…These are used to normalize the 􀅫low of data/information. On the other hand, LSTM networks also exist, which are used to process, classify and predict depending on the data of time sequence [55].…”

Section: A Lstmmentioning

confidence: 99%

A review on the impact of AI as big data in computer network environment

Ayub¹,

Mumtaz²,

Ahmad³

et al. 2021

J. adv. tec. eng. res.

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The rapid growth of Arti􀅫icial Intelligence (AI) with the evolution of network technology has improved and progressed to meet the needs of people. Big data place an important role in the 􀅫ield of AI due to enhanced utilization in computer networks. Many applications are used for big data-based AI in a computer network environment. The problems and the attacks (such as Ransomware, Phishing attacks, and AI attacks) occurring in Network Technology are widely associated with AI as big data along with the solutions such as detection and identi􀅫ication of given attacks and malicious activities. The computer network technology environment is used on a large scale and varies widely with the use of AI in the context of Big Data. However, more advanced technologies are applied with AI to ensure the security of Computer networks. Due to the vast amount of traf􀅫ic occurring in networks, the result leads to the occurrence of damage in the network, and intrusion detection is a process that proved to be the most essential to ensure the security provided to the system. The de􀅫ined attacks are speci􀅫ied as the main issues and will prevent vulnerable and malicious activities. The network requires a system to detect intrusions, which is further used to observe and identify the system and networks for anomalies and malicious intrusions as Intrusion Detection System (IDS), Firewall, Encryption, and Anti-spam technologies are used to identify any malicious activity and prevent it. For these attacks, Long Short Term Memory (LSTM) is used in deep learning, and many other technologies such as Machine learning and Data mining are associated with LSTM to detect intrusions. The computer networks with the application of big data as AI were made highly secure and ef􀅫icient systems by implementing the proposed LSTM method.

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Research on Network Attack and Defense Based on Artificial Intelligence Technology

Cited by 2 publications

References 0 publications

Optimization Research of Directed Fuzzing Based on AFL

Optimization Research of Directed Fuzzing Based on AFL

A review on the impact of AI as big data in computer network environment

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