An Ensemble Intrusion Detection Model For Internet of Things Network

Choudhary, Sarika; Kesswani, Nishtha; Majhi, Sudhan

doi:10.21203/rs.3.rs-479157/v1

Cited by 6 publications

(6 citation statements)

References 29 publications

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“…Choudhary et al [ 33 ] propose an IDS based on deep learning using SVM and DNN. Since SVMs are binary classifiers, only two routes were classified at a time for the appropriate route to be shown.…”

Section: Existing Workmentioning

confidence: 99%

“…Based on the studies reviewed in the previous sections, we found the following: Most papers targeted the IoT network environment when designing their systems (e.g., [ 6 , 8 , 13 , 18 , 22 , 23 , 28 , 33 ]). Few papers targeted other environments, such as wireless medical sensor networks (e.g., [ 20 ]).…”

Section: Existing Workmentioning

confidence: 99%

“… As for the datasets used to train the system, most papers (e.g., [ 3 , 8 , 24 ]) used the UNSW-NB15, NLS-KDD, and KDD99, which are considered good but outdated datasets, while other papers built their own datasets ([ 14 , 17 , 18 ]). In some papers, however, the IoTID20 and Bot-IoT datasets, which collect data in real time, were used (e.g., [ 15 , 32 , 33 ]). In most of the studies, accuracy, recall, F1 score, and precision were used as metrics for evaluating system performance (e.g., [ 15 , 16 , 19 , 23 ]).…”

Section: Existing Workmentioning

confidence: 99%

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Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Altulaihan,

Almaiah,

Aljughaiman

2024

Sensors

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Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users’ security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

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Section: Existing Workmentioning

confidence: 99%

Section: Existing Workmentioning

confidence: 99%

Section: Existing Workmentioning

confidence: 99%

See 1 more Smart Citation

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Altulaihan,

Almaiah,

Aljughaiman

2024

Sensors

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“…Then, the paper discussed the result that was defined by using the CCNx 1.0 protocol. Sarika et al [45] presented a network intrusion detection system (IDS) to be applied to the IoT. This paper focused on some attacks such as DoS and DDoS attacks using ML algorithms to detect all possible intrusions, such as deep neural network (DNN) and SVM.…”

Section: Related Work On Icn-based Iot With Machine Learning Technolo...mentioning

confidence: 99%

Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

Bukhowah,

Aljughaiman,

Rahman

2024

Electronics

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The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy access and straightforward security to IoT content. However, the integration of IoT devices into the ICN introduces new security challenges, particularly in the form of DoS attacks. These attacks aim to disrupt or disable the normal operation of the ICN, potentially leading to severe consequences for IoT applications. Machine learning (ML) is a powerful technology. This paper proposes a new approach for developing a robust and efficient solution for detecting DoS attacks in ICN-IoT networks using ML technology. ML is a subset of artificial intelligence (AI) that focuses on the development of algorithms. While several ML algorithms have been explored in the literature, including neural networks, decision trees (DTs), clustering algorithms, XGBoost, J48, multilayer perceptron (MLP) with backpropagation (BP), deep neural networks (DNNs), MLP-BP, RBF-PSO, RBF-JAYA, and RBF-TLBO, researchers compare these detection approaches using classification metrics such as accuracy. This classification metric indicates that SVM, RF, and KNN demonstrate superior performance compared to other alternatives. The proposed approach was carried out on the NDN architecture because, based on our findings, it is the most used one and has a high percentage of various types of cyberattacks. The proposed approach can be evaluated using an ndnSIM simulation and a synthetic dataset for detecting DoS attacks in ICN-IoT networks using ML algorithms.

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“…A multi-agent model was proposed using deep learning in [16]. Many researchers have extensively studied the vulnerabilities of RPL, including various internal attacks, for instance, Sybil, rank, sinkhole, version number, blackhole, neighbor, DIS, DAO, and selective-forwarding attacks [14,[17][18][19]. (DIS and DAO are explained in Section 3.1.2).…”

Section: Related Workmentioning

confidence: 99%

Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security

Asim

Tariq

Baker

et al. 2022

Sensors

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Recently, the Internet of Things (IoT) has emerged as an important way to connect diverse physical devices to the internet. The IoT paves the way for a slew of new cutting-edge applications. Despite the prospective benefits and many security solutions offered in the literature, the security of IoT networks remains a critical concern, considering the massive amount of data generated and transmitted. The resource-constrained, mobile, and heterogeneous nature of the IoT makes it increasingly challenging to preserve security in routing protocols, such as the routing protocol for low-power and lossy networks (RPL). RPL does not offer good protection against routing attacks, such as rank, Sybil, and sinkhole attacks. Therefore, to augment the security of RPL, this article proposes the energy-efficient multi-mobile agent-based trust framework for RPL (MMTM-RPL). The goal of MMTM-RPL is to mitigate internal attacks in IoT-based wireless sensor networks using fog layer capabilities. MMTM-RPL mitigates rank, Sybil, and sinkhole attacks while minimizing energy and message overheads by 25–30% due to the use of mobile agents and dynamic itineraries. MMTM-RPL enhances the security of RPL and improves network lifetime (by 25–30% or more) and the detection rate (by 10% or more) compared to state-of-the-art approaches, namely, DCTM-RPL, RBAM-IoT, RPL-MRC, and DSH-RPL.

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An Ensemble Intrusion Detection Model For Internet of Things Network

Cited by 6 publications

References 29 publications

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Detection of DoS Attacks for IoT in Information-Centric Networks Using Machine Learning: Opportunities, Challenges, and Future Research Directions

Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security

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