An Adversarial Learning Model for Intrusion Detection in Real Complex Network Environments

Author(s):  
Ying Zhong ◽  
Yiran Zhu ◽  
Zhiliang Wang ◽  
Xia Yin ◽  
Xingang Shi ◽  
...  
2021 ◽  
pp. 102177
Author(s):  
ZHENDONG WANG ◽  
YAODI LIU ◽  
DAOJING HE ◽  
SAMMY CHAN

2020 ◽  
Vol 513 ◽  
pp. 386-396 ◽  
Author(s):  
Mohammad Mehedi Hassan ◽  
Abdu Gumaei ◽  
Ahmed Alsanad ◽  
Majed Alrubaian ◽  
Giancarlo Fortino

2020 ◽  
Vol 29 (6) ◽  
pp. 267-283
Author(s):  
Femi Emmanuel Ayo ◽  
Sakinat Oluwabukonla Folorunso ◽  
Adebayo A. Abayomi-Alli ◽  
Adebola Olayinka Adekunle ◽  
Joseph Bamidele Awotunde

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 776
Author(s):  
Marcin Niemiec ◽  
Rafał Kościej ◽  
Bartłomiej Gdowski

The Internet is an inseparable part of our contemporary lives. This means that protection against threats and attacks is crucial for major companies and for individual users. There is a demand for the ongoing development of methods for ensuring security in cyberspace. A crucial cybersecurity solution is intrusion detection systems, which detect attacks in network environments and responds appropriately. This article presents a new multivariable heuristic intrusion detection algorithm based on different types of flags and values of entropy. The data is shared by organisations to help increase the effectiveness of intrusion detection. The authors also propose default values for parameters of a heuristic algorithm and values regarding detection thresholds. This solution has been implemented in a well-known, open-source system and verified with a series of tests. Additionally, the authors investigated how updating the variables affects the intrusion detection process. The results confirmed the effectiveness of the proposed approach and heuristic algorithm.


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