With the increasing rates of interconnected Internet of Things (IoT) devices within Software-Defined Networking (SDN) environments, distributed denial of service (DDoS) attacks have become increasingly common. As a result of this challenge, novel detection and classification methods must be developed based on the unique characteristics of SDN-supported IoT networks. This paper proposes a novel approach to detecting and categorizing DDoS attacks that has been optimized specifically for such environments. As part of our methodology, we integrate convolutional neural networks (CNN) and long-short-term memory (LSTM) models into a multilevel deep neural network architecture. With this hybrid architecture, complex spatial and temporal patterns can be automatically extracted from raw network traffic data to facilitate comprehensive analysis and accurate identification of DDoS attacks. We validate the efficacy and superiority of our proposed approach over traditional machine learning algorithms by conducting rigorous experiments on real-world datasets. Our findings underscore the potential of the multi-level deep neural network approach as a robust and scalable solution for mitigating DDoS attacks in SDN-supported IoT networks. By improving network security and resilience to evolving threats, our methodology contributes to safeguarding critical infrastructures in the era of interconnected IoT ecosystems.

In multiple-instance learning (MIL), the existing bag encoding and attention-based pooling approaches assume that the instances in the bag have no relationship among them. This assumption is unsuited, as the instances in the bags are rarely independent in diverse MIL applications. In contrast, the instance relationship assumption-based techniques incorporate the instance relationship information in the classification process. However, in MIL, the bag composition process is complicated, and it may be possible that instances in one bag are related and instances in another bag are not. In present MIL algorithms, this relationship assumption is not explicitly modeled. The learning algorithm is trained based on one of two relationship assumptions (whether instances in all bags have a relationship or not). Hence, it is essential to model the assumption of instance relationships in the bag classification process. This paper proposes a robust approach that generates vector representation for the bag for both assumptions and the representation selection process to determine whether to consider the instances related or unrelated in the bag classification process. This process helps to determine the essential bag representation vector for every individual bag. The proposed method utilizes attention pooling and vision transformer approaches to generate bag representation vectors. Later, the representation selection subnetwork determines the vector representation essential for bag classification in an end-to-end trainable manner. The generalization abilities of the proposed framework are demonstrated through extensive experiments on several benchmark datasets. The experiments demonstrate that the proposed approach outperforms other state-of-the-art MIL approaches in bag classification.

Cloud Computing (CC) concept is an emerging field of technology. It provides shared resources through its own Data Centers (DC’s), Virtual Machines (VM’s) and servers. People now shift their data on cloud for permanent storage and online easily approachable. Fog is the extended version of cloud. It gives more features than cloud and it is a temporary storage, easily accessible and secure for consumers. Smart Grid (SG) is the way which fulfills the demand of electricity of consumers according to their requirements. Micro Grid (MG) is a part of SG. So there is a need to balance load of requests on fog using VM’s. Response Time (RT), Processing Time (PT) and delay are three main factors which, discussed in this paper with Hill Climbing Load Balancing (HCLB) technique with Optimize best RT service broker policy. Keywords Cloud Computing Fog Computing Virtual Machines Hill Climbing Load Balancing Technique Smart Grid