A Machine Learning Based Intrusion Detection System for Mobile Internet of Things

Abstract

Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios.

Application of linear regression algorithm and stochastic gradient descent in a machine‐learning environment for predicting biomass higher heating value

“…This method can be defined using Eqn. ((2), where the dependency between the dependent variable Y and independent variable X is shown 39 . In the proposed model, the linear regression method is developed without regulation.…”

“…((2), where the dependency between the dependent variable Y and independent variable X is shown. 39 In the proposed model, the linear regression method is developed without regulation. Linear regression supervised machine learning has been used previously in energy modeling investigations such as power (1) systems reliability analysis, 40 battery energy storage systems, 41 and solar photovoltaic predictions.…”

An Optimal Decision Method for Intrusion Detection System in Wireless Sensor Networks With Enhanced Cooperation Mechanism

“…Amar et al [12] proposed a cross-layer-based IDS with two layers of detection aiming at the limited resources and distributed characteristics of the IoT. It uses a heuristic approach in which the IDS composes of two stages.…”

Error-Robust Distributed Denial of Service Attack Detection Based on an Average Common Feature Extraction Technique

“…Distributed Denial of Service attacks are one of the most important security threats nowadays. In a DDoS attack, a large volume of traffic is sent through the network, exhausting the network resources, as well as the overall bandwidth and individual node resources [ 15 ]. Consequently, the victim is forced to slow down, crash or shut down due to multiple connection requests during a period of time [ 16 ].…”