Implementation and Embellishment of Prevention of Keylogger Spyware Attacks

Wazid, Mohammad; Sharma, Robin; Katal, Avita; Goudar, R. H.; Bhakuni, P.; Tyagi, A.K.

doi:10.1007/978-3-642-40576-1_26

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…Furthermore, Detection methodology is also among the aspect of classification which Regarding the detection method of [1,3], IDS uses the misuse detection (signature) and anomaly (behavior) detection. Misuse detection uses the pre-definition of known attacks like a knowledge base of signatures to define the possible suspicious events such as [78]. Therefore, anomaly detection has been used a statistical and machine learning pattern to build a classifier model which is used to discover the normal and abnormal behavior.…”

Section: Typementioning

confidence: 99%

Feature Selection Algorithms in Intrusion Detection System: A Survey

2018

KSII TIIS

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Regarding to the huge number of connections and the large flow of data on the Internet, Intrusion Detection System (IDS) has a difficulty to detect attacks. Moreover, irrelevant and redundant features influence on the quality of IDS precisely on the detection rate and processing cost. Feature Selection (FS) is the important technique, which gives the issue for enhancing the performance of detection. There are different works have been proposed, but a map for understanding and constructing a state of the FS in IDS is still need more investigation. In this paper, we introduce a survey of feature selection algorithms for intrusion detection system. We describe the well-known approaches that have been proposed in FS for IDS. Furthermore, we provide a classification with a comparative study between different contribution according to their techniques and results. We identify a new taxonomy for future trends and existing challenges.

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Section: Typementioning

confidence: 99%