In Recent years, Network security becomes essential due to increase in usage of Smart phones and Internet of Things (IoT) devices. An IoT device plays a vital role in day to day life of human being. Such IoT devices are less secured and mostly used under abandoned environment. Recently these devices are widely affected by Distributed Denial of Service (DDoS) attack. DDos is one of the risky threats that destroy the critical network services. The extreme flow of packets in a network results in attack. Single source attack raises the Denial of Service (DoS); on the other hand attack rises from multiple servers referred to as DDoS. Researchers have developed software-defined networks (SDN) to effectively handle IoT equipment. To overcome above issue, we use an updated firefly algorithm to optimize the convolutional neural network (CNN) for detection of DDoS attacks in software-defined Internet of Things (SD-IoT) environment. Experimental result shows that our proposed model achieves 98% accuracy over detection of DDoS attack.

This paper proposes an Intrusion Detection System (IDS) against Sinkhole attacks in Mobile Adhoc Networks (MANET) with mobile sinks. A sinkhole attack is where a hacked node advertises a false routing update to draw network traffic. One effect of a sinkhole attack is that it may be used to launch further attacks, such as drops or changed routing information. Sinkhole nodes attempt to forge the source-destination routes to attract the surrounding network traffic. For this purpose, they modify routing control packets to publish fake routing information that makes sinkhole nodes appear as the best path to some destinations. Several machine learning techniques, including Decision Tree (DT), K-Nearest Neighbor (KNN), Convolution neural network (CNN), and Support Vector Machine (SVM), are used to do the categorization. Furthermore, the MANET's node's characteristics, particularly speed, are used for feature extraction. Totally 3997 unique samples, including 256 malicious samples and 3604 normal samples are collected. The categorization results demonstrate the accuracy of DT, KNN, CNN, and SVM at 98.4%, 96.7%, 98.6%, and 97.8%, respectively. The CNN approach is more accurate than other methods, at 98.6%, based on the data. After that, Priority, SVM, KNN, and CNN, in that order, each denotes excellent accuracy.