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Intrusion Detection System: Challenges in Network Security and Machine Learning

EasyChair Preprint no. 8578

7 pagesDate: August 3, 2022


Intrusion Detection Systems (IDSs) are a type of security management system that can detect and detect attacks aimed at compromising system security elements like accessibility, integrity, and privacy. There is 76% of DDoS attacks were conducted with mixed attacks. In 2020, about 24.02% of attacks employed a single type of technique, with an average of 1.66 attack strategies used per event among those that used multiple methodologies. In this paper, the details of intrusion detection system that include the techniques, methods, and machine learning algorithms are being discussed. There are also advantages and disadvantages that being explain in this paper. Moreover, it leverages its huge attack signature database to monitor the operation of routers, firewalls, important servers, and files, raising an alarm and sending relevant notifications when a breach is detected. Attackers gain access to other people's files by cracking their passwords, resulting in a significant breach in network security. Therefore, developers need to work on more precise, faster, and scalable techniques in order to prevent it from happen.

Keyphrases: Challenges, disadvantages, Intrusion Detection System, machine learning, Security, Techniques

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Nursyafiqah Abdul Samat},
  title = {Intrusion Detection System: Challenges in Network Security and Machine Learning},
  howpublished = {EasyChair Preprint no. 8578},

  year = {EasyChair, 2022}}
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