2015
DOI: 10.20533/ijisr.2042.4639.2015.0070
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Internet of Things (IoT) Security: Current Status, Challenges and Countermeasures

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Cited by 41 publications
(28 citation statements)
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“…In this section, we discuss the security properties that are supported by our solution: a. Authentication: Everything on the Internet of Things must be identifiable and authenticated with the other objects connected, so it's a challenging task because of the nature of IoT. Our proposed solution provides mutual authentication between the broker and their clients (publishers and subscribers) [24]. 1) Publisher (subscriber) authentication: the publisher (the subscriber) is authenticated by using the session key Skp (Sks) approved by the usage of the AugPAKE Algorithm.…”
Section: Security Discussionmentioning
confidence: 99%
“…In this section, we discuss the security properties that are supported by our solution: a. Authentication: Everything on the Internet of Things must be identifiable and authenticated with the other objects connected, so it's a challenging task because of the nature of IoT. Our proposed solution provides mutual authentication between the broker and their clients (publishers and subscribers) [24]. 1) Publisher (subscriber) authentication: the publisher (the subscriber) is authenticated by using the session key Skp (Sks) approved by the usage of the AugPAKE Algorithm.…”
Section: Security Discussionmentioning
confidence: 99%
“…A lightweight solution should be considered through designing and executing protocols in the encryption and authentication of IoT devices. Meanwhile, these algorithms can run with limited capabilities on IoT devices [ 17 , 59 ].…”
Section: Connectivity and System Integrity Approachesmentioning
confidence: 99%
“…Internet [93,94,100,107,110,[114][115][116][117]120] Lastly, regarding agricultural cyber-security, researchers intensify their efforts in designing Machine Learning (ML) and Deep Learning (DL) methods for anomaly behavior detection and network analysis of intrusion detection systems (IDS) [124]. Traditional Machine and Deep Learning applications applied for cybersecurity in agriculture include [125,126]: The emergence of such methods has enabled efficient handling and analysis of large amounts of data (e.g., files extracted from server logs, communication equipment, security solutions and blogs related to information security, in different types of structured and unstructured formats), so as to support a variety of conceptual models, security activities and knowledge generation mechanisms.…”
Section: Privacy Confidentially Availabilitymentioning
confidence: 99%