A Machine Learning Based Intrusion Detection System for Mobile Internet of Things
| Autor: | Vishwa Teja Alaparthy, Amar Amouri, Salvatore D. Morgera |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
iot
Mobility model Computer science Denial-of-service attack 02 engineering and technology Intrusion detection system wsn lcsh:Chemical technology Biochemistry Article Analytical Chemistry 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Markov chain business.industry Wireless network ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS 020206 networking & telecommunications Atomic and Molecular Physics and Optics Random forest amof intrusion detection systems linear regression 020201 artificial intelligence & image processing business Wireless sensor network random forest Computer network |
| Zdroj: | Sensors, Vol 20, Iss 2, p 461 (2020) Sensors (Basel, Switzerland) Sensors Volume 20 Issue 2 |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|