Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT.

The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection. In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. The proposed AMLSDM framework develops an SDN-enabled security mechanism for IoT devices with the support of an adaptive machine learning classification model to achieve the successful detection and mitigation of DDoS attacks. The proposed framework utilizes machine learning algorithms in an adaptive multilayered feed-forwarding scheme to successfully detect the DDoS attacks by examining the static features of the inspected network traffic. In the proposed adaptive multilayered feed-forwarding framework, the first layer utilizes Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers to build a model for detecting DDoS attacks from the training and testing environment-specific datasets. The output of the first layer passes to an Ensemble Voting (EV) algorithm, which accumulates the performance of the first layer classifiers. In the third layer, the adaptive frameworks measures the real-time live network traffic to detect the DDoS attacks in the network traffic. The proposed framework utilizes a remote SDN controller to mitigate the detected DDoS attacks over Open Flow (OF) switches and reconfigures the network resources for legitimate network hosts. The experimental results show the better performance of the proposed framework as compared to existing state-of-the art solutions in terms of higher accuracy of DDoS detection and low false alarm rate.

Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT).

The central intelligence offered by Software Defined Networking (SDN) promise the smart and reliable reconfiguration which enables the scalability of dynamic enterprise networks. The decoupled forwarding plane and control plane of SDN infrastructure is a key feature that supports the SDN controller to extract the physical network topology information at runtime to formulate network reconfigurations. This SDN-based network reconfiguration enables application-aware routing capability for Internet of Thing (IoT). However, these IoT enabled SDN-based routing protocols face some performance limitations in iterative reconfiguration process due to complete centralized path selection mechanism To this end, in this paper, we propose SDN-Based Application-aware Distributed adaptive Flow Iterative Reconfiguring (SADFIR) routing protocol. The proposed routing protocol enables the distributed SDN iterative solver controller to maintain the load-balancing between flow reconfiguration and flow allocation cost. In particular, the proposed routing protocol of SADFIR implements multiple SDN controllers that collaborate with network devices at forwarding plane to develop appropriate clustering strategy for routing the sensed information. This distributed SDN controllers are assisted to clustering topology that successfully map the residual network resources and also enable unique multi-hop application-aware data transmission. In addition, the proposed SADFIR monitor the iterative reconfiguration settings according to the network traffic of heterogeneity-aware network devices. The simulation experiments are conducted in comparison with the state-of-the-art routing protocols which demonstrates that SADFIR is heterogeneity-aware which is able to adopt the different scales of network with maximum network lifetime.