Exploring Scope of Computational Intelligence in IoT Security Paradigm
As the expansion of nano-devices, smartphones, 5G, tiny sensors, and distributed networks evolve, the IoT is combining the “factual and virtual” anywhere and anytime. Subsequently, it is attracting the attention of both “maker and hacker.” However, interconnecting many “things” also means the possibility of interconnecting many diversified threats and attacks. For example, a malware virus can easily propagate through the IoT at an unprecedented rate. In the four design aspects of the smart IOT, there may be various threats and attacks; they are:
Data perception and collection: In this aspect, typical attacks include data leakage, sovereignty, breach, and authentication.
Data storage: The following attacks may occur – denial-of-service attacks (attacks on availability), access control attacks, integrity attacks, impersonation, modification of sensitive data, and so on.
Data processing: In this aspect, there may exist computational attacks that aim to generate...
- Ayala I, Pinilla MA, Fuentes L (2012) Exploiting dynamic weaving for self-managed agents in the IOT. In: German conference on multiagent system technologies. Springer, pp 5–14Google Scholar
- Bao F, Chen IR (2012) Dynamic trust management for internet of things applications. In: Proceedings of the 2012 international workshop on Self-aware internet of things. ACM, pp 1–6Google Scholar
- Javaid A, Niyaz Q, Sun W, Alam M (2016) A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI international conference on bio-inspired information and communications technologies (formerly BIONETICS), ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp 21–26Google Scholar
- Lever KE, Kifayat K, Merabti M (2015) Identifying interdependencies using attack graph generation methods. In: 2015 11th international conference on innovations in information technology (IIT). IEEE, pp 80–85Google Scholar
- Li Y, Ma R, Jiao R (2015) A hybrid malicious code detection method based on deep learning. Methods 9(5):205–216Google Scholar
- Lin KC, Chen SY, Hung JC (2014) Botnet detection using support vector machines with artificial fish swarm algorithm. J Appl Math 2014:1–9Google Scholar
- Mahalle PN, Thakre PA, Prasad NR, Prasad R (2013) A fuzzy approach to trust based access control in internet of things. In: 2013 3rd international conference on wireless communications, vehicular technology, information theory and aerospace & electronic systems (VITAE). IEEE, pp 1–5Google Scholar
- Muldoon C, O’Hare GM, Collier R, O’Grady MJ (2006) Agent factory micro edition: a framework for ambient applications. In: International conference on computational science. Springer, pp 727–734Google Scholar
- Ould-Yahia Y, Banerjee S, Bouzefrane S, Boucheneb H (2017) Exploring formal strategy framework for the security in iot towards e-health context using computational intelligence. In: Internet of things and big data technologies for next generation healthcare. Springer, pp 63–90Google Scholar
- Urien P (2016) Three innovative directions based on secure elements for trusted and secured iot platforms. In: 2016 8th IFIP international conference on new technologies, mobility and security (NTMS). IEEE, pp 1–2Google Scholar
- Viroli M, Audrito G, Damiani F, Pianini D, Beal J (2016) A higher-order calculus of computational fields. arXiv preprint. arXiv:161008116Google Scholar
- WhitePaper (2017) Iot 2020: smart and secure. International Electrotechnical Commission, GenevaGoogle Scholar
- Zhao YL (2013) Research on data security technology in internet of things. Appl Mech Mater Trans Tech Publ 433:1752–1755Google Scholar