Machine Learning Algorithms with Feature Reduction Mechanism for Intrusion Detection System
Language: English
Published by Mohd Abdul Hafi, 2024
- Softcover
- Used
Condition: Used
£ 27.75
Quantity: 2 available
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Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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- Title
- Machine Learning Algorithms with Feature Reduction Mechanism for Intrusion Detection System
- Author
- Sah, Gulab
- Publisher
- Mohd Abdul Hafi
- Publication year
- 2024
- Condition
- Hervorragend
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798224227990
- Seller catalogs
- Bücher
In modern society, the internet-based services are getting too much popularity from last few decads. In today's world, people typically use smartphones, laptops, tablets and other gadgets to access these services at any time, from anywhere. Therefore, data that streams via these networks may contain essential or sensitive information. Moreover, due to advancement of internet technology, the sensitive data is always transmitted between devices and data-warehouse for storing and retrieval purpose. These results open trapdoor for the attackers to launch the widespread of attacks that may be threatend to the organisation and/or the individuals. Attackers use a variety of cutting-edge approaches to take advantage of system security flaws. This might result in the misuse of confidential or delicate information, unauthorised access to the system, or a breach of client accounts. Security professionals and system administrators must employ the advanced security techniques to defend against these threats. Additionally, as new technologies developed for instance big data, IoTs, etc., the vast volumes of traffic or data are also growing, gradually. Therefore, updating the attack signature is getting harder, slower, and more tedious because of the network data traffic expanding significantly. Moreover, searching through such large amounts of data in hunt of relevant or useful evidence is a critical task for data scientists, commercial corporations, and marketers. Hence, network security is presently a rising field of study for scientists and academics because of increased traffic and extensive internet usage. Researchers in these fields (network security) attempt to prevent intrusions or attackers from exploiting system or network vulnerabilities to gain unauthorised entry (s). Although many prevention technologies such as Antivirus, firewalls, etc., for securing a network or system have been developed since last two decades to detect and eliminate potential attacks such as DoS, user to root (U2R), remote to local (R2L), Probe, etc. As a result, fundamental security procedures are needed to identify new kinds of attacks as well as unwanted traffic or data that could harm or disrupt a particular host or network.
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