Open AccessSecure automated threat detection and prevention (SATDP)
Author(s) -
CH Ramaiah,
D Adithya Charan,
R Akhil
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.20.11760
Subject(s) - computer science , workload , intrusion detection system , artificial intelligence , categorization , computer security , permission , unsupervised learning , machine learning , network packet , supervised learning , evasion (ethics) , action (physics) , data mining , artificial neural network , physics , immune system , quantum mechanics , political science , law , immunology , biology , operating system
Secure automated threat detection and prevention is the more effective procedure to reduce the workload of analyst by scanning the network, server functions& then informs the analyst if any suspicious activity is detected in the network. It monitors the system continuously and responds according to the threat environment. This response action varies from phase to phase. Here suspicious activities are detected by the help of an artificial intelligence which acts as a virtual analyst concurrently with network intrusion detection system to defend from the threat environment and taking appropriate measures with the permission of the analyst. In its final phase where packet analysis is carried out to surf for attack vectors and then categorize supervised and unsupervised data. Where the unsupervised data will be decoded or converted to supervised data with help of analyst feedback and then auto-update the algorithm (virtual analyst). So that it evolves the algorithm (with active learning mechanism) itself by time and become more efficient, strong. So, it can able to defend form similar or same kind of attacks.