Recent Papers

In this project, we propose an online and unsupervised anomaly detection algorithm for streaming data using an array of sliding windows and the probability density-based descriptors (based on these windows). The experimental results and performances are presented based on the intrusion detection. Compared with the anomaly detection algorithm using the hierarchical temporal memory proposed by intrusion detection (which outperforms a wide range of other anomaly detection algorithms), our algorithm can perform better in many cases, that is, with higher detection rates and earlier detection for contextual anomalies and concept drifts. Remote sensor networks are progressively utilized in a wide scope of possible applications, including security and observation, control, incitation and support of intricate frameworks and fine-grain checking of indoor and open air conditions. The idea of remote sensor networks makes them entirely helpless against assault. The portable hubs are haphazardly conveyed, there are no actual snags for the foe, hence, they can be effectively caught, and assaults can emerge out of all headings and focus on any hub. Therefore, security of remote sensor organizations (WSN) is the most trying for this sort of organization. Intrusion Detection Systems (IDSs) can assume a significant part in identifying and forestalling security assaults. An interruption discovery component is viewed as a main wellspring of security for data and correspondences innovation. In any case, traditional interruption location strategies should be changed and improved for application to the Internet of Things attributable to specific constraints, similar to asset obliged gadgets, the restricted memory and battery limit of hubs, and explicit convention stacks.

Copyright © 2023 Ramana S, Abinaya S, Dharani M, Sathiya K, Vigneshwari A. This is an open access article distributed under the Creative Commons Attribution License.