DEDA: An algorithm for early detection of topology attacks in the internet of things

Karande, Jalindar Baban; Joshi, Sarang

doi:10.11591/ijece.v11i2.pp1761-1770

Cited by 5 publications

(3 citation statements)

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“…Similarly, in smart universities, real-time anomaly detection will secure student and administrative data and campus facilities against unauthorized access and cyber breaches [32]. By exploring scalability and resilience, future research will contribute to robust cybersecurity frameworks tailored to IIoT environments, effectively addressing emerging challenges posed by cyber threats and attacks [33], [34].…”

Section: Experiments Resultsmentioning

confidence: 99%

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

Idouglid,

Tkatek,

Elfayq

et al. 2024

IJECE

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In the dynamic landscape of Industry 4.0, characterized by the integration of smart technologies and the industrial internet of things (IIoT), ensuring robust security measures is imperative. This paper explores advanced security solutions tailored for the IIoT, focusing on the integration of intrusion detection systems (IDS) with advanced machine learning (ML) and deep learning (DL) techniques. In this paper, we present a novel intrusion detection model to fortify to fortify Industry 4.0 systems against evolving cyber threats by leveraging ML an DL algorithms for dynamic adaptation. To evaluate the performances and effectiveness of our proposed model, we use the improved Coburg intrusion detection data sets (CIDDS) and BoT-IoT datasets, showcasing notable performance attributes with an exceptional 99.99% accuracy, high recall, and precision scores. The model demonstrates computational efficiency, with rapid learning and detection phases. This research contributes to advancing next-gen security solutions for Industry 4.0, offering a promising approach to tackle contemporary cyber.

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Section: Experiments Resultsmentioning

confidence: 99%

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

Idouglid,

Tkatek,

Elfayq

et al. 2024

IJECE

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“…Secondly, personal information and sensitive patient data could be altered, used, or compromised without real-time control. This poses a risk to the infrastructure and a disastrous effect on people's lives, as the transmission of false data can lead to misdiagnosis and severe health problems and even put patients' lives at Bulletin of Electr Eng & Inf ISSN: 2302-9285  risk [7], [8]. If these data can be verified (data authentication), this prevents users from accessing erroneous or falsified data.…”

Section: Introductionmentioning

confidence: 99%

Data access control for named data of health things

EL-Bakkouchi,

EL Ghazi,

Bouayad

et al. 2024

Bulletin EEI

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The internet of health things (IoHT) represents an innovative network concept that significantly improving healthcare. However, security and privacy are the main concerns of IoHT because the transmitted health data is often sensitive data about patients’ health status, which needs to be secured and protected from unauthorized users and any leakage. Named data networking (NDN) is considered the most promising architecture for the future internet that perfectly fits with the requirements of IoHT systems, especially regarding security and privacy. In this paper, we exploit the fundamental features of NDN to design a robust system for IoHT to ensure secure communication and access to health data. This system presents a content access control model, which prevents attackers and unauthorized users from accessing health data, allows only authorized users to access these data, and prevents users from accessing “corrupted” or “fake” content. The simulation results show that the proposed mechanism slightly delays the secure retrieval of health data. However, this delay is tolerable since the mechanism protects the health data from unauthorized persons and those who try to inject untrusted data into the network.

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“…However, RPL-based networks are vulnerable to a massive security vulnerabilities due to their limited nature [11], [12]. The most dangerous attack in IoT implementation is the blackhole attack, which is regarded as one of the most dangerous and a point of entry for all other attacks [13].…”

Section: Introductionmentioning

confidence: 99%

Blackhole attacks in internet of things networks: a review

Kamis

Yassin

Abdollah

et al. 2023

IJEECS

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The internet of things (IoT) is one of data revolution area and is the following extraordinary mechanical jump after the internet. In terms of IoT, it is expected that electronic gadgets that are used on a regular basis would be connected to the current of the internet. IPv6 over low-power wireless personal area networks (6LoWPAN) is a one of IPv6 header pressure technology, and accordingly, it is vulnerable to attack. The IoT is a combination of devices with restricted resource assets like memory, battery power, and computational capability. To solve this, RPL or routing protocol for low power Lossy network is deploy by utilizing a distance vector scheme. One of denial of service (Dos) attack to RPL network is blackhole attack in which the assailant endeavors to become a parent by drawing in a critical volume of traffic to it and drop all packets. In this paper, we discuss research on numerous attacks and current protection methods, focusing on the blackhole attack. There is also discussion of challenge, open research issues and future perspectives in RPL security. Furthermore, research on blackhole attacks and specific detection technique proposed in the literature is also been presented.

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DEDA: An algorithm for early detection of topology attacks in the internet of things

Cited by 5 publications

References 0 publications

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

Next-gen security in IIoT: integrating intrusion detection systems with machine learning for industry 4.0 resilience

Data access control for named data of health things

Blackhole attacks in internet of things networks: a review

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