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

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.

[1]  Vishwa Teja Alaparthy,et al.  A Multi-Level Intrusion Detection System for Wireless Sensor Networks Based on Immune Theory , 2018, IEEE Access.

[2]  Salvatore D. Morgera,et al.  Modeling an Intrusion Detection System Based on Adaptive Immunology , 2019, Int. J. Interdiscip. Telecommun. Netw..

[3]  Wei-Yin Loh,et al.  A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms , 2000, Machine Learning.

[4]  Salvatore D. Morgera,et al.  Cross layer-based intrusion detection based on network behavior for IoT , 2018, 2018 IEEE 19th Wireless and Microwave Technology Conference (WAMICON).

[5]  Ashish Mishra,et al.  Detecting border intrusion using wireless sensor network and artificial neural network , 2010, 2010 6th IEEE International Conference on Distributed Computing in Sensor Systems Workshops (DCOSSW).

[6]  Nick Feamster,et al.  Machine Learning DDoS Detection for Consumer Internet of Things Devices , 2018, 2018 IEEE Security and Privacy Workshops (SPW).

[7]  Aikaterini Mitrokotsa,et al.  Intrusion Detection with Neural Networks and Watermarking Techniques for MANET , 2007, IEEE International Conference on Pervasive Services.

[8]  Ahmed Helmy,et al.  A SURVEY OF MOBILITY MODELS in Wireless Adhoc Networks , 2004 .

[9]  Ali Dehghantanha,et al.  Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning , 2019, IEEE Transactions on Sustainable Computing.

[10]  A. Kannan,et al.  Multi-Layer Integrated Anomaly Intrusion Detection System for Mobile Adhoc Networks , 2007, 2007 International Conference on Signal Processing, Communications and Networking.

[11]  Christos Dimitrakakis,et al.  Intrusion detection in MANET using classification algorithms: The effects of cost and model selection , 2013, Ad Hoc Networks.

[12]  J.B.D. Cabrera,et al.  Infrastructures and algorithms for distributed anomaly-based intrusion detection in mobile ad-hoc networks , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[13]  Norman R. Draper,et al.  Fitting a Straight Line by Least Squares , 2014 .

[14]  Naveen K. Chilamkurti,et al.  Distributed attack detection scheme using deep learning approach for Internet of Things , 2017, Future Gener. Comput. Syst..

[15]  Marimuthu Palaniswami,et al.  Anomaly detection in wireless sensor networks , 2008, IEEE Wireless Communications.

[16]  Prasenjit Choudhury,et al.  A simulation analysis of flooding attack in MANET using NS-3 , 2011, 2011 2nd International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE).

[17]  Geethapriya Thamilarasu,et al.  Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things , 2019, Sensors.

[18]  Salvatore D. Morgera,et al.  A Cross-Layer, Anomaly-Based IDS for WSN and MANET , 2018, Sensors.

[19]  Beizhan Wang,et al.  Survey on HMM based anomaly intrusion detection using system calls , 2010, 2010 5th International Conference on Computer Science & Education.

[20]  Farrukh Aslam Khan,et al.  Malicious AODV: Implementation and Analysis of Routing Attacks in MANETs , 2012, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.

[21]  Raman K. Mehra,et al.  Ensemble methods for anomaly detection and distributed intrusion detection in Mobile Ad-Hoc Networks , 2008, Inf. Fusion.

[22]  Dharma P. Agrawal,et al.  SVM-based intrusion detection system for wireless ad hoc networks , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[23]  Thiemo Voigt,et al.  SVELTE: Real-time intrusion detection in the Internet of Things , 2013, Ad Hoc Networks.

[24]  Salvatore D. Morgera,et al.  A Study on the Adaptability of Immune models for Wireless Sensor Network Security , 2018, BICA.

[25]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[26]  Georgios Kambourakis,et al.  Swarm intelligence in intrusion detection: A survey , 2011, Comput. Secur..

[27]  Zied Elouedi,et al.  Naive Bayes vs decision trees in intrusion detection systems , 2004, SAC '04.

[28]  Amar Amouri.  Cross Layer-based Intrusion Detection System Using Machine Learning for MANETs , 2019 .

[29]  Nei Kato,et al.  Detecting Blackhole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic Learning Method , 2007, Int. J. Netw. Secur..

[30]  Ioanis Nikolaidis,et al.  Node density and connectivity properties of the random waypoint model , 2004, Comput. Commun..

[31]  Y.A. Sekercioglu,et al.  Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[32]  Karl N. Levitt,et al.  A general cooperative intrusion detection architecture for MANETs , 2005, Third IEEE International Workshop on Information Assurance (IWIA'05).

[33]  Miao Xie,et al.  Anomaly Detection in Wireless Sensor Networks , 2013 .

[34]  Salvatore D. Morgera,et al.  A simple scheme for pseudo clustering algorithm for cross layer intrusion detection in MANET , 2015, 2015 7th IEEE Latin-American Conference on Communications (LATINCOM).