Security Challenges and Solutions in theInternet of Things

Porras, Jari; Khakurel, Jayden; Knutas, Antti; Pänkäläinen, Jouni

doi:10.13052/nbjict1902-097x.2018.010

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…Entrepreneurial or commercial off-the-shelf IoT devices are usually supported with the solutions of software that are insufficient to protect every IoT device or system [287,288].…”

Section: New Insights In Machine and Deep Learning For Iot Securitymentioning

confidence: 99%

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Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions

Bharati

Podder

2022

Security and Communication Networks

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The integration of the Internet of Things (IoT) connects a number of intelligent devices with minimum human interference that can interact with one another. IoT is rapidly emerging in the areas of computer science. However, new security problems are posed by the cross-cutting design of the multidisciplinary elements and IoT systems involved in deploying such schemes. Ineffective is the implementation of security protocols, i.e., authentication, encryption, application security, and access network for IoT systems and their essential weaknesses in security. Current security approaches can also be improved to protect the IoT environment effectively. In recent years, deep learning (DL)/machine learning (ML) has progressed significantly in various critical implementations. Therefore, DL/ML methods are essential to turn IoT system protection from simply enabling safe contact between IoT systems to intelligence systems in security. This review aims to include an extensive analysis of ML systems and state-of-the-art developments in DL methods to improve enhanced IoT device protection methods. On the other hand, various new insights in machine and deep learning for IoT securities illustrate how it could help future research. IoT protection risks relating to emerging or essential threats are identified, as well as future IoT device attacks and possible threats associated with each surface. We then carefully analyze DL and ML IoT protection approaches and present each approach’s benefits, possibilities, and weaknesses. This review discusses a number of potential challenges and limitations. The future works, recommendations, and suggestions of DL/ML in IoT security are also included.

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“…Entrepreneurial or commercial off-the-shelf IoT devices are usually supported with the solutions of software that are insufficient to protect every IoT device or system [287,288].…”

Section: New Insights In Machine and Deep Learning For Iot Securitymentioning

confidence: 99%

“…Entrepreneurial or commercial off-the-shelf IoT devices are usually supported with the solutions of software that are insufficient to protect every IoT device or system [275,276]. Since the IoT has many different use cases, the software-level security is poor.…”

Section: Iot Securitymentioning

confidence: 99%