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  • 1.
    Bhuyan, Monowar H.
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Umea University.
    Elmroth, Erik
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Umea University.
    Multi-Scale Low-Rate DDoS Attack Detection Using the Generalized Total Variation Metric2018In: 17th IEEE International Conference on Machine Learning and Applications, IEEE, 2018, p. 1040-1047Conference paper (Refereed)
    Abstract [en]

    We propose a mechanism to detect multi-scale low-rate DDoS attacks which uses a generalized total variation metric. The proposed metric is highly sensitive towards detecting different variations in the network traffic and evoke more distance between legitimate and attack traffic as compared to the other detection mechanisms. Most low-rate attackers invade the security system by scale-in-and-out of periodic packet burst towards the bottleneck router which severely degrades the Quality of Service (QoS) of TCP applications. Our proposed mechanism can effectively identify attack traffic of this natures, despite its similarity to legitimate traffic, based on the spacing value of our metric. We evaluated our mechanism using datasets from CAIDA DDoS, MIT Lincoln Lab, and real-time testbed traffic. Our results demonstrate that our mechanism exhibits good accuracy and scalability in the detection of multi-scale low-rate DDoS attacks.

  • 2. Sarmah, Roshmi
    et al.
    Bhuyan, Manasjyoti
    Bhuyan, Monowar H.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    SURE-H: A Secure IoT Enabled Smart Home System2019In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), IEEE, 2019, p. 59-63Conference paper (Refereed)
    Abstract [en]

    With the growing technology, the demand for smart things is drastically increased in daily-life. The IoT (Internet of Things) is one of the major components that provides facility to interact with IoT enabled devices. In this work, we propose a secure and efficient smart home system that enable to protect homes from theft or unusual activities and parallelly saves power. Our system is developed by exploiting the features of IoT that facilitates us to monitor an IoT enabled home from anywhere anytime over the Internet when data are stored in the cloud. This system uses a motion detector to detect a moving object from the environment where the system is deployed. The proposed system is evaluated using real-time deployment at KU campus considering 30 rooms for 60 days and found really useful in terms of safeness from any theft and saving power in comparison to existing systems.

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