Open AccessAnomaly based Intrusion Detection by Heuristics to Predict Intrusion Scope of IOT Network Transactions
Author(s) -
Ravinder Korani,
P. Chandra Sekhar Reddy
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.7.10982
Subject(s) - intrusion detection system , computer science , heuristics , heuristic , scope (computer science) , anomaly detection , software deployment , context (archaeology) , network security , computer security , database transaction , data mining , distributed computing , artificial intelligence , software engineering , database , paleontology , biology , programming language , operating system
Conventional intrusion detection mechanisms face serious limitations in identifying heterogeneous and distributed type of intrusions over the IoT environment. This is due to inadequate resources and open deployment environment of IoT. Accordingly, ensuring data security and privacy are tough challenges in the practical context. This manuscript discusses various aspects of networking security and related challenges along with the concepts of system architecture. Further, endeavored to define a machine learning model that outlines two heuristics called Intrusion Scope Heuristic ( ), and benign scope heuristic ( ), which further uses in predictive analysis to identify the IOT network transaction is prone to intrusion or benign. The experimental study revealed the significance of the proposal with maximal detection accuracy, and minimal miss rate.