Review of Specific Features and Challenges in the Current Internet of Things Systems Impacting Their Security and Reliability

Bureš, Miroslav; Klima, Matej; Rechtberger, Vaclav; Ahmed, Bestoun S.; Hindy, Hanan; Bellekens, Xavier

doi:10.1007/978-3-030-72660-7_52

Cited by 17 publications

(16 citation statements)

References 15 publications

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“…The DDoS attack is a type of DoS attack whereby multiple systems or machines or devices operate together to carry out an attack on a single target, which makes it difficult to track and turn off the attack machines. DDoS attackers often use a botnet to disrupt network services [27,28]. On 21 October 2016, the biggest DDoS attack was performed using Mirai IoT malware.…”

Section: Overview Of Denial-of-service (Dos) Attackmentioning

confidence: 99%

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Alimi

Ouahada

Abu‐Mahfouz

et al. 2022

JSAN

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The Internet of Things (IoT) is a promising technology that allows numerous devices to be connected for ease of communication. The heterogeneity and ubiquity of the various connected devices, openness to devices in the network, and, importantly, the increasing number of connected smart objects (or devices) have exposed the IoT network to various security challenges and vulnerabilities which include manipulative data injection and cyberattacks such as a denial of service (DoS) attack. Any form of intrusive data injection or attacks on the IoT networks can create devastating consequences on the individual connected device or the entire network. Hence, there is a crucial need to employ modern security measures that can protect the network from various forms of attacks and other security challenges. Intrusion detection systems (IDS) and intrusion prevention systems have been identified globally as viable security solutions. Several traditional machine learning methods have been deployed as IoT IDS. However, the methods have been heavily criticized for poor performances in handling voluminous datasets, as they rely on domain expertise for feature extraction among other reasons. Thus, there is a need to devise better IDS models that can handle the IoT voluminous datasets efficiently, cater to feature extraction, and perform reasonably well in terms of overall performance. In this paper, an IDS based on redefined long short-term memory deep learning approach is proposed for detecting DoS attacks in IoT networks. The model was tested on benchmark datasets; CICIDS-2017 and NSL-KDS datasets. Three pre-processing procedures, which include encoding, dimensionality reduction, and normalization were deployed for the datasets. Using key classification metrics, experimental results obtained show that the proposed model can effectively detect DoS attacks in IoT networks as it performs better compared to other methods including models from related works.

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Section: Overview Of Denial-of-service (Dos) Attackmentioning

confidence: 99%

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Alimi

Ouahada

Abu‐Mahfouz

et al. 2022

JSAN

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Section: Security Challenges and Vulnerabilities Of The Iotmentioning

confidence: 99%

“…The wireless technology, scalability, energy and distributed nature of the IoT are several of the main causes of security challenges [ 14 ]. According to [ 15 ], there are several underlying factors of the security challenges in the IoT: The weakest parts of a system. As the number of IoT devices is rapidly growing, the resource limitations of IoT devices lead to the use of lightweight security algorithms and the security of certain devices is likely neglected.…”

Section: Security Challenges and Vulnerabilities Of The Iotmentioning

confidence: 99%

Biometrics for Internet-of-Things Security: A Review

Yang

Wang

Sahri

et al. 2021

Sensors

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The large number of Internet-of-Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric-based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric-cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking issues and future research directions.

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“…Bures et al [38] provide a comprehensive review of the various features of IoT and the security challenges specifically related to IoT. The paper covered a vast number of security features and challenges that must be addressed to secure IoT devices and emphasized that security and privacy are the major security challenges that must be addressed to achieve a secure IoT system.…”

Section: Related Workmentioning

confidence: 99%

Securing Environmental IoT Data Using Masked Authentication Messaging Protocol in a DAG-Based Blockchain: IOTA Tangle

et al. 2021

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The demand for the digital monitoring of environmental ecosystems is high and growing rapidly as a means of protecting the public and managing the environment. However, before data, algorithms, and models can be mobilized at scale, there are considerable concerns associated with privacy and security that can negatively affect the adoption of technology within this domain. In this paper, we propose the advancement of electronic environmental monitoring through the capability provided by the blockchain. The blockchain’s use of a distributed ledger as its underlying infrastructure is an attractive approach to counter these privacy and security issues, although its performance and ability to manage sensor data must be assessed. We focus on a new distributed ledger technology for the IoT, called IOTA, that is based on a directed acyclic graph. IOTA overcomes the current limitations of the blockchain and offers a data communication protocol called masked authenticated messaging for secure data sharing among Internet of Things (IoT) devices. We show how the application layer employing the data communication protocol, MAM, can support the secure transmission, storage, and retrieval of encrypted environmental sensor data by using an immutable distributed ledger such as that shown in IOTA. Finally, we evaluate, compare, and analyze the performance of the MAM protocol against a non-protocol approach.

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Review of Specific Features and Challenges in the Current Internet of Things Systems Impacting Their Security and Reliability

Cited by 17 publications

References 15 publications

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Refined LSTM Based Intrusion Detection for Denial-of-Service Attack in Internet of Things

Biometrics for Internet-of-Things Security: A Review

Securing Environmental IoT Data Using Masked Authentication Messaging Protocol in a DAG-Based Blockchain: IOTA Tangle

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