A Review on the Security of IoT Networks: From Network Layer’s Perspective

Jahangeer, Asma; Bazai, Sibghat Ullah; Aslam, Saad; Marjan, Shah; Anas, Muhammad; Hashemi, Sayed Habibullah

doi:10.1109/access.2023.3246180

Cited by 24 publications

(15 citation statements)

References 80 publications

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“…While rule-based detection engines are a strong foundation for security businesses, cyber threat hunting is a crucial skill to have to detect undisclosed advanced threats. In contrast to rule-based detection approaches, hunting identifies and investigates risks proactively [11]. Cyber threat hunting is a proactive eye into the entire network to keep focused on the networks and endpoint to hunt malicious, suspicious, or any risky activity that has detection by existing tools.…”

Section: B Problem Evolutionmentioning

confidence: 99%

Machine Learning-Based Security Mechanism to Detect and Prevent Cyber-Attack in IoT Networks

Alomiri,

Mishra,

AlShehri

2024

IJCDS

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The increasing prevalence of Internet of Things (IoT) systems has brought about significant security concerns. Cyber-attacks, including denial-of-service attacks, malware infections, and phishing attempts, pose serious threats to the integrity and functionality of IoT networks. To ensure comprehensive protection, it is essential to develop machine learning-based security measures that employ robust models and integrate multiple security mechanisms. In this study, a Ridge Classifier is utilized as a powerful model to detect anomalies in IoT systems. By leveraging this approach, the proposed security system can accurately identify and predict cyber-attacks in real-time, utilizing secure and up-to-date information from the network. The integration of machine learning techniques enhances the system's ability to detect and mitigate threats effectively. Experimental results demonstrate the high accuracy of the proposed system in detecting and mitigating network threats in IoT systems, achieving a remarkable accuracy rate of 97 percent. This level of accuracy not only improves the security and resilience of government and business networks but also ensures the protection of valuable data from malicious threats. The development of machine learning-based security measures, such as the system presented in this study, is crucial for addressing the security challenges faced by IoT systems. By accurately detecting and predicting cyber-attacks, these measures play a pivotal role in safeguarding the integrity, confidentiality, and availability of IoT networks. Furthermore, the integration of robust models and the incorporation of multiple security measures provide a comprehensive defense against a wide range of threats. In conclusion, the implementation of machine learning-based security measures, particularly utilizing the Ridge Classifier model, offers significant benefits in protecting IoT systems. By effectively detecting and mitigating network threats with high accuracy, these measures contribute to improving the security and resilience of government and business networks. Moreover, the protection of data from malicious threats ensures the integrity and confidentiality of IoT systems, fostering trust and reliability in the rapidly expanding IoT landscape.

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Section: B Problem Evolutionmentioning

confidence: 99%

Machine Learning-Based Security Mechanism to Detect and Prevent Cyber-Attack in IoT Networks

Alomiri,

Mishra,

AlShehri

2024

IJCDS

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“…The RPL as a widely deployed routing protocol in IoT is susceptible to routing attacks such as DIS-flooding, sinkhole, rank, and worst parent attacks to name a few. Routing attacks pose a great threat to IoT running RPL as a routing protocol; and may affect its performance and functionalities [11].…”

Section: Figure 1 Applications Of the Internet Of Thingsmentioning

confidence: 99%

High-Level Defence Model against Routing Attacks on the Internet-of-Things

Sejaphala,

Malele,

Lugayizi

2024

ijcs

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This paper is part of the doctoral work that aims to answer the following research question: “To what extent can an intelligent security model effectively defend against routing attacks in RPL-based Internet of Things (IoT) with a demonstration of less network resource consumption, high detection rate, and minimal false negatives?” To answer this question, this paper proposes a high-level conceptual framework to defend the IoT against routing attacks. In recent works, mitigation techniques have been proposed to act against routing attacks, however conceptual defence or mitigation framework is not presented as a set of steps to follow to develop an effective and robust intelligent security model. This paper aims to present a high-level conceptual defence framework against routing attacks; specifically, sinkhole, rank, DIS-Flooding, and worst parent. The four mentioned routing attacks are capable of disturbing IoT network functions and operations, and consuming network resources such as memory and power.

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“…Security is one of the most critical challenges of IoT and especially of IoMT, which deals with patients' and individuals' vital, sensitive and private information. The problems and solutions in this region can be classified as confidentiality and key management, authentication and access control, anonymity and privacy-preserving, and intrusion detection systems [10], [11]. Considering the resource constraint of IoT devices, the efficiency of using resources in these solutions becomes a necessary condition.…”

Section: A Main Motivationmentioning

confidence: 99%

“…• By the above information, the attacker can perform all computations of Algorithm 2, at steps ( 6) and ( 7) and calculate N s1 . Finally, by eavesdropping on M 1.3 at step (9) of Algorithm 2 and performing the computations of step (10), the shared key K ws is disclosed.…”

Section: ) Eavesdropping Attackmentioning

confidence: 99%

A New PUF-Based Protocol for Mutual Authentication and Key Agreement Between Three Layers of Entities in Cloud-Based IoMT Networks

Aminian Modarres,

Anzabi-Nezhad,

Zare

2024

IEEE Access

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The Internet of Medical Things (IoMT) is a promising framework for expanding and improving telemedicine services. A common cloud-based IoMT architecture consists of three layers of entities, the first layer (such as smart sensors and devices), the second layer (such as gateways), and the third layer (such as cloud servers). Obviously, in these networks, the protection of sensitive information against security threats as well as authentication between the entities is a key issue. On the other hand, the devices involved in the first and second layers usually suffer from poor computational capabilities as well as a lack of physical protection, which should be considered in the design of security protocols. Recently, Alladi et al. have proposed a lightweight authentication protocol for the cloud-based IoMT that addresses these challenges, using Physically Unclonable Function (PUF). In this paper, we first provide thorough cryptanalysis of their scheme and clarify its important vulnerabilities that lead to protocol collapse. Then, we propose a new lightweight protocol based on PUF to perform strong mutual authentication and key agreement between parties in the IoMT networks. The formal (using BAN logic) and informal security analysis demonstrate that our scheme is resistant to several well-known attacks, including physical attacks. Also, our evaluation of computational cost and security features clearly shows that the proposed scheme outperforms similar schemes in security and efficiency. Another important advantage of our protocol is that it performs the authentication and key agreement process separately for each pair of layers in the three-layer cloud-based IoMT architecture.

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A Review on the Security of IoT Networks: From Network Layer’s Perspective

Cited by 24 publications

References 80 publications

Machine Learning-Based Security Mechanism to Detect and Prevent Cyber-Attack in IoT Networks

Machine Learning-Based Security Mechanism to Detect and Prevent Cyber-Attack in IoT Networks

High-Level Defence Model against Routing Attacks on the Internet-of-Things

A New PUF-Based Protocol for Mutual Authentication and Key Agreement Between Three Layers of Entities in Cloud-Based IoMT Networks

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