An Advanced Computing Approach for IoT-Botnet Detection in Industrial Internet of Things

Nguyen, Tu N.; Ngo, Quoc-Dung; Nguyen, Huy-Trung; Giang, Nguyễn Long

doi:10.1109/tii.2022.3152814

Cited by 23 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Nguyen et al (2022) , a supervised machine learning classification method was proposed. A graph-based hybrid analysis method, which contains static and dynamic methods, was used in IoT botnet detection.…”

Section: Preliminariesmentioning

confidence: 99%

Modified graph-based algorithm to analyze security threats in IoT

Arat,

Akleylek

2023

PeerJ Computer Science

View full text Add to dashboard Cite

In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions via the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained.

show abstract

Section: Preliminariesmentioning

confidence: 99%

Modified graph-based algorithm to analyze security threats in IoT

Arat,

Akleylek

2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…A number of solutions are used in the current literature for detecting abnormal traffic generated by IoT devices [12][13][14][15]. These methods are mainly based on the idea of using machine and deep learning techniques to check the network traffic on the home edge router and classify them as normal and abnormal.…”

Section: Related Workmentioning

confidence: 99%

A DDoS Detection and Prevention System for IoT Devices and Its Application to Smart Home Environment

et al. 2022

View full text Add to dashboard Cite

The Internet of Things (IoT) has become an integral part of our daily life as it is growing in many fields, such as engineering, e-health, smart homes, smart buildings, agriculture, weather forecasting, etc. However, the growing number of IoT devices and their weak configuration raise many security challenges such as designing protocols to protect these devices from various types of attacks such as using them as bots for DDoS attacks on target servers. In order to protect IoT devices from enslavement as bots in a home environment, we develop a lightweight security model consisting of various security countermeasures. The working mechanism of the proposed security model is presented in a two-part experimental scenario. Firstly, we describe the working mechanism of how an attacker infects an IoT device and then spreads the infection to the entire network. Secondly, we propose a set of mechanisms consisting of filtration, detection of abnormal traffic generated from IoT devices, screening, and publishing the abnormal traffic patterns to the rest of the home routers on the network. We tested the proposed scheme by infecting an IoT device with malicious code. The infected device then infects the rest of the IoT devices in its network and launches a DDoS attack by receiving attack-triggering commands from the botmaster. Finally, the proposed detection mechanism is used to detect the abnormal traffic and block the connection of infected devices in the network. The results reveal that the proposed system blocks abnormal traffic if the packets from an IoT device exceeded a threshold of 50 packets. Similarly, the network packet statistics show that, in the event of an unwanted situation, the detection mechanism runs smoothly and avoids any possible delays in the network.

show abstract

“…The Internet of Things (IoT) has witnessed substantial growth in recent years, enabling various physical devices and objects to connect to the internet and exchange data. This technology has found applications in diverse fields, including education, healthcare, energy, military, manufacturing, agriculture and transportation [15,39]. It is projected that the global number of interconnected IoT devices will surpass 30 billion by 2025, as reported by statista.com [22].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Deep Learningbased Intrusion Detection System for Internet of Things Networks with Hybrid Feature Reduction and Data Balancing Techniques

Karamollaoğlu,

Doğru,

Yücedağ

2024

ITC

View full text Add to dashboard Cite

With the increasing use of Internet of Things (IoT) technologies, cyber-attacks on IoT devices are also increasing day by day. Detecting attacks on IoT networks before they cause any damage is crucial for ensuring the security of the devices on these networks. In this study, a novel Intrusion Detection System (IDS) was developed for IoT networks. The IoTID20 and BoT-IoT datasets were utilized during the training phase and performance testing of the proposed IDS. A hybrid method combining the Principal Component Analysis (PCA) and the Bat Optimization (BAT) algorithm was proposed for dimensionality reduction on the datasets. The Synthetic Minority Over-SamplingTechnique (SMOTE) was used to address the problem of data imbalance in the classes of the datasets. The Convolutional Neural Networks (CNN) model, a deep learning method, was employed for attack classification. The proposed IDS achieved an accuracy rate of 99.97% for the IoTID20 dataset and 99.98% for the BoT-IoT dataset in attack classification. Furthermore, detailed analyses were conducted to determine the effects of the dimensionality reduction and data balancing models on the classification performance of the proposed IDS.

show abstract

An Advanced Computing Approach for IoT-Botnet Detection in Industrial Internet of Things

Cited by 23 publications

References 17 publications

Modified graph-based algorithm to analyze security threats in IoT

Modified graph-based algorithm to analyze security threats in IoT

A DDoS Detection and Prevention System for IoT Devices and Its Application to Smart Home Environment

An Efficient Deep Learningbased Intrusion Detection System for Internet of Things Networks with Hybrid Feature Reduction and Data Balancing Techniques

Contact Info

Product

Resources

About