Edge-Based Intrusion Detection for IoT devices

Mudgerikar, Anand; Sharma, Puneet; Bertino, Elisa

doi:10.1145/3382159

Cited by 24 publications

(17 citation statements)

References 22 publications

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“…erefore, the single host-based intrusion detection and the network-based intrusion detection technologies have been increasingly unable to meet the security requirements of the current complex and diverse attack behavior recognition. In addition, the IoT devices based on 5G have a primary function for which computation of massive data is required [4]. e high capacity and complexity of safety audit data on large-scale and high-speed networks are overwhelming to the traditional IDS.…”

Section: Introductionmentioning

confidence: 99%

IoT-Oriented Distributed Intrusion Detection Methods Using Intelligent Classification Algorithms in Spark

Jia

et al. 2022

Security and Communication Networks

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With the rapid development and wide application of the 5G mobile communication and the explosive security threats of the Internet of things (IoT), distributed intrusion detection is one of the hot topics in the intrusion detection field of network security. The classification algorithm is a kind of the most representative and classical algorithms of artificial intelligence (AI), and it is an important technique for intrusion detection in order to distinguish the attack traffic from massive network data. In order to solve the problem to detect massive and complex network attack traffic in IoT, in this study, we propose the distributed intrusion detection framework and method using intelligent classification algorithms in Spark. We first introduce several mainstream classification algorithms provided by Spark. Second, the distributed intrusion detection procedure using intelligent classification algorithms is given. Next, the overall framework of the proposed model is built. Finally, a series of comparison experiments by the binary classification and quintuple classification in six evaluation indicators (i.e., recall, precision, F1-score, FNR, FPR, and ROC curve) indicate that the naive Bayes has a worse classification performance than that of other classification algorithms, and the classification effect in a cluster environment is almost the same as that in a stand-alone environment.

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Section: Introductionmentioning

confidence: 99%

IoT-Oriented Distributed Intrusion Detection Methods Using Intelligent Classification Algorithms in Spark

Jia

et al. 2022

Security and Communication Networks

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“…Second, all attacks were gathered into one group, and significance weights for this group were determined [12]. Create successful strategies for IoT security and detection of denial of service (DoS) attacks using deep machine learning algorithm integrating evaluation of RF, CNN, and MLP algorithms [13]. Hash chains are used to provide a realistic threat model for IoT devices and a secure mechanism for storing and relocating device records; E-Spion uses system information to create 3-layer core profiles with varied overheads for IoT devices and detects intrusions based on anomalous behavior [14][15][16][17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

Computer Vision-Based Intrusion Detection System for Internet of Things

Alosaimi

Almutairi

Chamato

2023

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) contributes to improving and automating the quality of our lives via devices and applications that progressively become more interconnected without user intervention in many areas such as smart homes, smart cities, smart transportation, and smart environment. However, IoT devices are vulnerable to cyberattacks. We cannot prevent all attacks, but they can be detected and resolved with the least damage. Moreover, they are connected for long periods of time without user intervention. Additionally, since they remain connected for long periods of time without user intervention, creative solutions must be devised to keep them safe, such as machine learning. The reach goal is to evaluate different machine learning algorithms to detect IoT network attacks quickly and effectively. The Bot-IoT dataset, which is derived from the original dataset, is used to evaluate various detection algorithms. Five different machine learning algorithms were tested on the two databases, and the results of the tests revealed high and accurate performance at all levels of the dataset.

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“…It is mainly composed of an agent, an analysis engine, and a response module. Several IDS-based security solutions were developed in the previous year [15][16][17][18][19][20][21][22][23][24][25]. But, these IDS-based security solutions cannot cope with the MEC architecture due to the changing behavior of users and devices.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The system's efficiency is high, but combining three different approaches increases the system complexity and the response time. For the IoT infrastructure, device-edge-based IDS has been proposed in [19]. The IDS is made with the help of behavioral profiles and system-level information.…”

Section: Edge Based Intrusion Detecɵon Frameworkmentioning

confidence: 99%

An edge based hybrid intrusion detection framework for mobile edge computing

Singh

Chatterjee

Satapathy

2021

Complex Intell. Syst.

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The Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

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Edge-Based Intrusion Detection for IoT devices

Cited by 24 publications

References 22 publications

IoT-Oriented Distributed Intrusion Detection Methods Using Intelligent Classification Algorithms in Spark

IoT-Oriented Distributed Intrusion Detection Methods Using Intelligent Classification Algorithms in Spark

Computer Vision-Based Intrusion Detection System for Internet of Things

An edge based hybrid intrusion detection framework for mobile edge computing

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