Enhanced network intrusion detection system protocol for internet of things

Mbarek, Bacem; Ge, Mouzhi; Pitner, Tomáš

doi:10.1145/3341105.3373867

Cited by 16 publications

(10 citation statements)

References 21 publications

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“…NIDS is set up at a selected point as a sub-net within the network to examine and match the passing traffic. Then it analyzes the pacts and raises an alert if violated [21]. These sensors activate the interfaces for managing, controlling, and receiving alerts and then forward the same to the central server.…”

Section: Ids For Iotsmentioning

confidence: 99%

“…These sensors activate the interfaces for managing, controlling, and receiving alerts and then forward the same to the central server. NIDS applications are attached to the network with two interfaces, one monitors the network conversation, and the other control and generate a report of the activity [21]. Table 2.…”

Section: Ids For Iotsmentioning

confidence: 99%

“…Lightweight, self adapting nature, multiple IoT device compatibility Not suitable for constrained objects. Bacem Mbarek et al [30] Distributed-Signature IDS Good in testing clone attacks Lack of real-time testing V. Subbarayalu et al [31] Distributed Anomaly detection is based on the observation and deviation of behavior or activity from the normal baseline [35]. An anomaly detection system in NIDS detects the intrusion at the physical network after passing the firewall, and in HIDS it is the last layer of the protection that exists in the endpoint that allow fine-tuned protection at the application level [36].…”

Section: D: Signature Intrusion Detection System (Sids)mentioning

confidence: 99%

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Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

et al. 2022

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The increasing number of connected devices in the era of Internet of Thing (IoT) has also increased the number intrusions. Intrusion Detection System (IDS) is a secondary intelligent system to monitor, detect, and alert about malicious activities; an Intrusion Prevention System (IPS) is an extension of a detection system that triggers relevant action when an attack is suspected in a futuristic aspect. Both IDS and IPS systems are significant and useful for developing a security model. Several studies exist to review the detection and prevention models; however, the coherence in the opportunistic or advancements in the models is missing. Besides, the existing models also have some limitations, which need to be surveyed to develop new security models. Our survey is the first one to present a study of risk factor analysis using mapping technique, and provide a proposal for hybrid framework for an efficient security model for intrusion detection and/or prevention. We explore the importance of various Artificial Intelligence (AI)-based techniques, tools, and methods used for the detection and/or prevention systems in IoTs. More specifically, we emphasize on Machine Learning (ML) and Deep Learning (DL) techniques for intrusion detection-prevention systems and provide a comparative analysis focusing on the feasibility, compatibility, challenges, and real-time issues. This present survey is beneficial for industry and academia to categorize the challenges and issues in the current security models and generate the new dimensions of developments of security frameworks with efficient ML or DL methods.

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Section: Ids For Iotsmentioning

confidence: 99%

Section: Ids For Iotsmentioning

confidence: 99%

Section: D: Signature Intrusion Detection System (Sids)mentioning

confidence: 99%

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Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

et al. 2022

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“…A witness or guarantor mediates between a sender and receiver to monitor and validate exchanged communication between the pair. The validation is performed in a non-involved manner [ 29 ]. In other words, no processing or communication is delegated to the witnesses to be performed on behalf of the communicating entities.…”

Section: A Review Of Resource-aiding Modesmentioning

confidence: 99%

Secure Communications for Resource-Constrained IoT Devices

Taha

Rashwan

Hassanein

2020

Sensors

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The importance of securing communications on the Internet of Things (IoT) cannot be overstated. This is especially the case in light of the increasing proliferation of IoT devices and instances, as well as the growing dependence on their usage. Meanwhile, there have recently been mounting concerns over a wide array of vulnerabilities in IoT communications. The objective of this work is to address constraints in IoT devices that are “resource-constrained”, which are devices that are limited in terms of computing, energy, communication, or range capabilities, whether in terms of nominal or temporal limitations. Specifically, we propose a framework for resource-aiding constrained devices to facilitate secure communication. Without loss of generalization, the framework’s viability is illustrated by focusing on a group of security functions that utilize message authentication codes, which is a strongly representative example of resource-intensive security functions. Aspects of the framework are further demonstrated in processing cores commonly used in commercial IoT devices.

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“…As a result, variety of malware variants and threats are newly emerging at a faster pace. [2][3][4] Attacks against IoT include theft of sensitive data or disrupting the functions of the network which include, Brute Force, Port Scanning, Denial of Service (DoS), Remote to Local (R2L), Probing (Probe), User to Root (U2R) attacks, etc., These attacks are affect the real-time applications such as transport and network protocols such as HTTP, TCP, SMTP, UDP, FTP, ICMP, etc [5][6][7]. In contrast, anomaly-based detection is used to detect either known or unknown attacks [8,9].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Particle Swarm and Gray Wolf Optimization Algorithm for IoT Intrusion Detection System

Krishna¹,

Thangavelu²

2021

IJIES

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Internet of Things (IoT) is a network that provides security for physical objects such as smart home appliance, smart machines and many more. The physical objects are assigned to a unique Internet address known as Internet Protocol (IP) that is used for data communication with the external entities of the network through the internet. The IoT devices are facing security issues due to the rapid increase in attacks that are launched by the intruders during data sharing through the internet. The detection of attacks is essential to provide a strong security mechanism for such threatening attacks. The proposed hybrid optimization algorithm utilizes the combination of Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in this research. The PSO is known for its fast computation speed and has found extensive utility in data training as well as data estimation. The GWO is developed as an intrusion detection approach to classify data and to efficiently detect several of intrusions. The proposed hybrid GWO-PSO uses NSL-KDD data set with binary and multi class problem respectively for showing the effectiveness of the present work. The results obtained better accuracy value of 99.97 % when compared to the existing LSTM-RNN that achieved 97.72% of accuracy, whereas the multi class SVM obtained 98 % and modified rank-based information gain feature selection method showed 99.8 % of accuracy.

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Enhanced network intrusion detection system protocol for internet of things

Cited by 16 publications

References 21 publications

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

Secure Communications for Resource-Constrained IoT Devices

Hybrid Particle Swarm and Gray Wolf Optimization Algorithm for IoT Intrusion Detection System

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