| Title | A Survey on Machine Learning Based Detection on DDoS Attacks for IoT Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Wehbi, Khadijeh, Hong, Liang, Al-salah, Tulha, Bhutta, Adeel A |
| Conference Name | 2019 SoutheastCon |
| Date Published | apr |
| Keywords | common malicious attacks, composability, comprehensive detection method, Computer crime, computer network security, DDoS attack detection, DDoS Attacks, DDoS Defense, denial of service (dos), Distributed Denial of Service (DDoS) attack, distributed denial of service attacks, feature extraction, Human Behavior, Internet of Things, Internet of Things (IoT), IoT networks, learning (artificial intelligence), machine learning, machine learning (ML), machine learning based detection, Metrics, Object recognition, performance evaluation, pubcrawl, Resiliency, resource IoT devices, Support vector machines |
| Abstract | Internet of Things (IoT) is transforming the way we live today, improving the quality of living standard and growing the world economy by having smart devices around us making decisions and performing our daily tasks and chores. However, securing the IoT system from malicious attacks is a very challenging task. Some of the most common malicious attacks are Denial of service (DoS), and Distributed Denial of service (DDoS) attacks, which have been causing major security threats to all networks and specifically to limited resource IoT devices. As security will always be a primary factor for enabling most IoT applications, developing a comprehensive detection method that effectively defends against DDoS attacks and can provide 100% detection for DDoS attacks in IoT is a primary goal for the future of IoT. The development of such a method requires a deep understanding of the methods that have been used thus far in the detection of DDoS attacks in the IoT environment. In our survey, we try to emphasize some of the most recent Machine Learning (ML) approaches developed for the detection of DDoS attacks in IoT networks along with their advantage and disadvantages. Comparison between the performances of selected approaches is also provided. |
| DOI | 10.1109/SoutheastCon42311.2019.9020468 |
| Citation Key | wehbi_survey_2019 |
A Survey on Machine Learning Based Detection on DDoS Attacks for IoT Systems