Moving towards a more digital and intelligent world equipped with internet-of-thing (IoT) devices creates many security issues. A distributed denial of service (DDoS) attack is one of the most formidable and challenging security threats that has taken hold with the emergence of the heterogeneous IoT (HetIoT). The massive DDoS attacks have exhibited their impact by continuously destroying a variety of infrastructures, resulting in huge losses, and endangering the overall availability of the digital world. The emphasis of this research is to identify and mitigate various DDoS attacks for HetIoT. The research proposes an intelligent intrusion detection system (IDS) using a convolutional neural network (CNN), i.e., HetIoT-CNN IDS, a novel deep learning-based convolutional neural network for the HetIoT environment. The proposed intelligent IDS successfully identifies and mitigates various DDoS attacks in the HetIoT infrastructure. The feasibility of the new proposed HetIoT-CNN IDS is assessed by considering binary and multi-class (8- and 13-classes) classification. The performance of the proposed intelligent IDS is compared with two state-of-the-art deep learning approaches for HetIoT, and the results reveal that the proposed HetIoT-CNN IDS outperforms it. The proposed HetIoT-CNN IDS successfully identifies various DDoS attacks with an accuracy rate of 99.75% for binary classes, 99.95% for 8-classes, and 99.99% for 13-classes. The work also compares the individual accuracy of binary classes, 8-classes, and 13-classes with state-of-the-art work.
A network comprises of a plurality of nodes working together to perform similar or different tasks. The network has a huge room for attacks so as to make the network inefficient. The major attacks that are causing huge turbulences to the network and its equivalent resources include Denial of Service (DoS) attacks and Distributed Denial of Service (DDoS) attacks. In prior-art there are many techniques that can sense and avoid these attacks. Packet Marking (PM) techniques are the most widely used successful techniques towards avoiding these attacks. However, there are some critical issues with the PM techniques, as these attacks are becoming more complicated due to growing talent in the market. PM technique can be used further to traceback the origin of attacks. In this traceback the major contest is to minimize the amount of packets in successful traceback of these attacks. The packets that are originating from the sources are not enough to traceback the origin. The paper discusses various known PM techniques available for fighting back against the mentioned attacks. Further, the paper also discloses the implementation of these techniques, their advantages, disadvantages, complexity analysis, and the results measured. Future trends including the need for effective and efficient defense mechanism are also discussed.
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