Project Details
Description
The rapid advancement in the field of Internet of Things (IoT) has scaled the level of connectivity among devices and it has also increased
security risks. The study explores the critical role of Intrusion Detection Systems (IDS) in protecting the IoT ecosystem against diverse cyber
threats. The study evaluates the existing trends, methodologies, techniques as well as challenges in IDS for IoT. We have followed a
systematic literature review (SLR) approach to gather and analyze existing review papers on IDS for IoT in order to find gaps in scalability,
resource efficiency and heterogeneity. In order to address these gaps a lightweight IDS using ML algorithms and techniques has been
proposed. The proposed framework will use comprehensive datasets such as benchmark datasets as well as IoT specific datasets in order
to obtain comprehensive findings.
security risks. The study explores the critical role of Intrusion Detection Systems (IDS) in protecting the IoT ecosystem against diverse cyber
threats. The study evaluates the existing trends, methodologies, techniques as well as challenges in IDS for IoT. We have followed a
systematic literature review (SLR) approach to gather and analyze existing review papers on IDS for IoT in order to find gaps in scalability,
resource efficiency and heterogeneity. In order to address these gaps a lightweight IDS using ML algorithms and techniques has been
proposed. The proposed framework will use comprehensive datasets such as benchmark datasets as well as IoT specific datasets in order
to obtain comprehensive findings.
| Status | Not started |
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