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- W4386014799 abstract "The traditional security systems are by no means enough to create a secured IT infrastructure, intrusion detection systems (IDSs), which observe the flow of system works and detect intrusions, which are usually used to complement other defense techniques. However, threats are becoming more powerful and strong, with attackers using new attack methods or modifying existing ones. Furthermore, developing an effective and robust intrusion detection systems is a challenging research platform due to the environment resource restrictions and its constant evolution. This work has a core objective of designing a model for protecting wireless local area network through data mining techniques. Protection from unauthorized access is thus another line of defense for network technologies. The work is to use data mining techniques to assist the IDS construction effort to help mitigate these issues. It has been found that intrusion detection technologies can be implemented jointly with data mining algorithms to spot attacks. An intrusion detection system is used to control network operations, mainly involved to detect and separate unwanted users with classification techniques. As mobile crowd sourcing technologies are applied to smart environments, planners can focus on revolutionizing the world by integrating and coordinating all of the technology resources. An effective IDS must still bring in the technical specifications of the systems being used. Additionally, these algorithms are evaluated for their use in discovering unknown attacks." @default.
- W4386014799 created "2023-08-21" @default.
- W4386014799 creator A5006952703 @default.
- W4386014799 date "2023-01-01" @default.
- W4386014799 modified "2023-10-02" @default.
- W4386014799 title "Intrusion Detection and Classification in Wireless LAN Using Data Mining Techniques" @default.
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- W4386014799 doi "https://doi.org/10.1007/978-981-99-3691-5_29" @default.
- W4386014799 hasPublicationYear "2023" @default.
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