scholarly journals ELNIDS: Ensemble Learning based Network Intrusion Detection System for RPL based Internet of Things

2020 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Ensemble Learning ◽  
Intrusion Detection System ◽  
Detection System ◽  
Smart Devices ◽  
Network Intrusion Detection ◽  
The Internet ◽  
Routing Attacks ◽  
Network Intrusion

Internet of Things is realized by a large number of heterogeneous smart devices which sense, collect and share data with each other over the internet in order to control the physical world. Due to open nature, global connectivity and resource constrained nature of smart devices and wireless networks the Internet of Things is susceptible to various routing attacks. In this paper, we purpose an architecture of Ensemble Learning based Network Intrusion Detection System named ELNIDS for detecting routing attacks against IPv6 Routing Protocol for Low-Power and Lossy Networks. We implement four different ensemble based machine learning classifiers including Boosted Trees, Bagged Trees, Subspace Discriminant and RUSBoosted Trees. To evaluate proposed intrusion detection model we have used RPL-NIDDS17 dataset which contains packet traces of Sinkhole, Blackhole, Sybil, Clone ID, Selective Forwarding, Hello Flooding and Local Repair attacks. Simulation results show the effectiveness of the proposed architecture. We observe that ensemble of Boosted Trees achieve the highest Accuracy of 94.5% while Subspace Discriminant method achieves the lowest Accuracy of 77.8% among classifier validation methods. Similarly, an ensemble of RUSBoosted Trees achieves the highest Area under ROC value of 0.98 while lowest Area under ROC value of 0.87 is achieved by an ensemble of Subspace Discriminant among all classifier validation methods. All the implemented classifiers show acceptable performance results.

ELNIDS: Ensemble Learning based Network Intrusion Detection System for RPL based Internet of Things

2020 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Ensemble Learning ◽  
Intrusion Detection System ◽  
Detection System ◽  
Smart Devices ◽  
Network Intrusion Detection ◽  
The Internet ◽  
Routing Attacks ◽  
Network Intrusion

Internet of Things is realized by a large number of heterogeneous smart devices which sense, collect and share data with each other over the internet in order to control the physical world. Due to open nature, global connectivity and resource constrained nature of smart devices and wireless networks the Internet of Things is susceptible to various routing attacks. In this paper, we purpose an architecture of Ensemble Learning based Network Intrusion Detection System named ELNIDS for detecting routing attacks against IPv6 Routing Protocol for Low-Power and Lossy Networks. We implement four different ensemble based machine learning classifiers including Boosted Trees, Bagged Trees, Subspace Discriminant and RUSBoosted Trees. To evaluate proposed intrusion detection model we have used RPL-NIDDS17 dataset which contains packet traces of Sinkhole, Blackhole, Sybil, Clone ID, Selective Forwarding, Hello Flooding and Local Repair attacks. Simulation results show the effectiveness of the proposed architecture. We observe that ensemble of Boosted Trees achieve the highest Accuracy of 94.5% while Subspace Discriminant method achieves the lowest Accuracy of 77.8% among classifier validation methods. Similarly, an ensemble of RUSBoosted Trees achieves the highest Area under ROC value of 0.98 while lowest Area under ROC value of 0.87 is achieved by an ensemble of Subspace Discriminant among all classifier validation methods. All the implemented classifiers show acceptable performance results.

scholarly journals Internet of Things: Evolution, Concerns and Security Challenges

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1809
Author(s):  
Parushi Malhotra ◽  
Yashwant Singh ◽  
Pooja Anand ◽  
Deep Kumar Bangotra ◽  
Pradeep Kumar Singh ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Security And Privacy ◽  
Research Assessment ◽  
The Internet ◽  
Digital World ◽  
Network Intrusion ◽  
Depth Analysis

The escalated growth of the Internet of Things (IoT) has started to reform and reshape our lives. The deployment of a large number of objects adhered to the internet has unlocked the vision of the smart world around us, thereby paving a road towards automation and humongous data generation and collection. This automation and continuous explosion of personal and professional information to the digital world provides a potent ground to the adversaries to perform numerous cyber-attacks, thus making security in IoT a sizeable concern. Hence, timely detection and prevention of such threats are pre-requisites to prevent serious consequences. The survey conducted provides a brief insight into the technology with prime attention towards the various attacks and anomalies and their detection based on the intelligent intrusion detection system (IDS). The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS). Additionally, a case study of healthcare in IoT is presented. The study depicts the architecture, security, and privacy issues and application of learning paradigms in this sector. The research assessment is finally concluded by listing the results derived from the literature. Additionally, the paper discusses numerous research challenges to allow further rectifications in the approaches to deal with unusual complications.

scholarly journals Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 432
Author(s):  
Xuan-Ha Nguyen ◽  
Xuan-Duong Nguyen ◽  
Hoang-Hai Huynh ◽  
Kim-Hung Le
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Cyber Attacks ◽  
Network Intrusion Detection ◽  
The Internet ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Cyber security has become increasingly challenging due to the proliferation of the Internet of things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the Internet. However, these devices possess various security flaws resulting from the lack of defense mechanisms and hardware security support, therefore making them vulnerable to cyber attacks. In addition, IoT gateways provide very limited security features to detect such threats, especially the absence of intrusion detection methods powered by deep learning. Indeed, deep learning models require high computational power that exceeds the capacity of these gateways. In this paper, we introduce Realguard, an DNN-based network intrusion detection system (NIDS) directly operated on local gateways to protect IoT devices within the network. The superiority of our proposal is that it can accurately detect multiple cyber attacks in real time with a small computational footprint. This is achieved by a lightweight feature extraction mechanism and an efficient attack detection model powered by deep neural networks. Our evaluations on practical datasets indicate that Realguard could detect ten types of attacks (e.g., port scan, Botnet, and FTP-Patator) in real time with an average accuracy of 99.57%, whereas the best of our competitors is 98.85%. Furthermore, our proposal effectively operates on resource-constraint gateways (Raspberry PI) at a high packet processing rate reported about 10.600 packets per second.

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