Expert Knowledge Correlated Evaluation of Intrusion Detection System in Heterogeneous IoT

<div>The dynamic heterogeneous IoT contexts adversely affect the performance of learning-based network intrusion detection and prevention systems resulting in increased misclassification rates—necessitating an expert knowledge correlated evaluation framework. The proposed framework includes intrusion root cause analysis and a correlation model that can be generalized over any network intrusion dataset, corresponding expert knowledge, detection technique, and learning-based algorithm. The experimentations prove the robustness of the propounded</div><div>framework on imbalanced datasets.</div>

Expert Knowledge Correlated Evaluation of Intrusion Detection System in Heterogeneous IoT

<div>The dynamic heterogeneous IoT contexts adversely affect the performance of learning-based network intrusion detection and prevention systems resulting in increased misclassification rates—necessitating an expert knowledge correlated evaluation framework. The proposed framework includes intrusion root cause analysis and a correlation model that can be generalized over any network intrusion dataset, corresponding expert knowledge, detection technique, and learning-based algorithm. The experimentations prove the robustness of the propounded</div><div>framework on imbalanced datasets.</div>

Behavioral Host-Based Intrusion Detection and Prevention System for Android

Intrusion Detection System (IDS) is vital to protect smartphones from about to happen security breach and make sure user privacy. Android is the most popular mobile Operating System (OS), holding many markets share. Android malware detection has received important concentration, existing solutions typically rely on performing resource intensive analysis on a server, assuming an uninterrupted link between the device and the server. In this paper, we propose a behavior Host-based IDS (HIDS) by using permissions incorporating arithmetical and ML algorithms. The benefit of our proposed IDS is two folds. First, it is completely independent and runs on the smartphone device, without need any link to a server. Second, it requires only training dataset consisting of some of examples from both benign and malicious datasets for tuning. though, in put into practice, collecting malicious examples is exciting since its important infecting the device and collecting many of samples in order to characterize the malware’s behavior and the labelling has to be done. The evaluation outcome show that the proposed IDS gives a very hopeful accuracy.