Intrusion Detection System- Via Fuzzy Artmap in Addition with Advance Semi Supervised Feature Selection
نویسندگان
چکیده
منابع مشابه
Intrusion Detection System-via Fuzzy Artmap in Addition with Advance Semi Supervised Feature Selection
Outstanding to the promotion of the Internet and local networks, interruption occasions to computer systems are emerging. Intrusion detection systems are becoming progressively vital in retaining appropriate network safety. IDS is a software or hardware device that deals with attacks by gathering information from a numerous system and network sources, then evaluating signs of security complexit...
متن کاملApplication of Fuzzy Association Rules-Based Feature Selection and Fuzzy ARTMAP to Intrusion Detection
Intrusion Detection System (IDS) deals with a very large amount of data that includes redundant and irrelevant features. Therefore, feature selection is a necessary data pre-processing step to design IDSs that are lightweight. In this paper, a novel feature selection method based on data mining techniques is proposed, which uses fuzzy association rules to obtain the optimum feature subset. In t...
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Considerable research work have been conducted towards Intrusion Detection Systems (IDSs) as well as feature selection. IDS guard a system from attack, misuse, and compromise. It can also screen network activity. Network traffic observing and extent is increasingly regarded as an vital role for understanding and improving the performance and security of our cyber infrastructure. In this researc...
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With the continued and relentless growth in dataset sizes in recent times, feature or attribute selection has become a necessary step in tackling the resultant intractability. Indeed, as the number of dimensions increases, the number of corresponding data instances required in order to generate accurate models increases exponentially. Fuzzy-rough set-based feature selection techniques offer gre...
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Feature selection is an important task in effective data mining. A new challenge to feature selection is the socalled “small labeled-sample problem” in which labeled data is small and unlabeled data is large. The paucity of labeled instances provides insufficient information about the structure of the target concept, and can cause supervised feature selection algorithms to fail. Unsupervised fe...
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ژورنال
عنوان ژورنال: International Journal of Data Mining & Knowledge Management Process
سال: 2015
ISSN: 2231-007X,2230-9608
DOI: 10.5121/ijdkp.2015.5303