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The Internet of things (IoT) has opened new dimensions of novel services and computing power for modern living standards by introducing innovative and smart solutions. Due to the extensive usage of these services, IoT has spanned numerous devices and communication entities, which makes the management of the network a complex challenge. Hence it is urgently needed to redefine the management of the IoT network. Software-defined networking (SDN) intrinsic programmability and centralization features simplify network management, facilitate network abstraction, ease network evolution, has the potential to manage the IoT network. SDN’s centralized control plane promotes efficient network resource management by separating the control and data plane and providing a global picture of the underlying network topology. Apart from the inherent benefits, the centralized SDN architecture also brings serious security threats such as spoofing, sniffing, brute force, API exploitation, and denial of service, and requires significant attention to guarantee a secured network. Among these security threats, Distributed Denial of Service (DDoS) and its variant Low-Rate DDoS (LR-DDoS), is one of the most challenging as the fraudulent user generates malicious traffic at a low rate which is extremely difficult to detect and defend. Machine Learning (ML), especially Federated Learning (FL), has shown remarkable success in detecting and defending against such attacks. In this paper, we adopted Weighted Federated Learning (WFL) to detect Low-Rate DDoS (LR-DDoS) attacks. The extensive MATLAB experimentation and evaluation revealed that the proposed work ignites the LR-DDoS detection accuracy compared with the individual Neural Networks (ANN) training algorithms, existing packet analysis-based, and machine learning approaches.

With rapid population growth in cities, to allow full use of modern technology, transportation networks need to be developed efficiently and sustainability. A significant problem in the traffic motion barrier is dynamic traffic flow. To manage traffic congestion problems, this paper provides a method for forecasting traffic congestion with the aid of a Deep neural network that minimizes blockage and plays a vital role in traffic smoothing. In the proposed model, data is collected and received by using smart Internet of things enabled devices. With the help of this model, data of the previous junction of signals will send to another junction and update after that next layer named as intelligence prediction for the congestion layer will receive data from sensors and the cloud which is used to find out the congestion point. The proposed TC2S-DNN model achieved the accuracy of 98.03 percent and miss rate of 1.97 percent which is better then previous published approaches.

Cloud health has consistently been a major issue in information technology. In the CC environment, it becomes particularly serious because the data is located in different places even in the entire globe. Associations are moving their information on to cloud as they feel their information is more secure and effectively evaluated. However, as a few associations are moving to the cloud, they feel shaky. As the present day world pushes ahead with innovation, one must know about the dangers that come along with cloud health. Cloud benefit institutionalization is important for cloud security administrations. There are a few confinements seeing cloud security as it is never a 100% secure. Instabilities will dependably exist in a cloud with regards to security. Cloud security administrations institutionalization will assume a noteworthy part in securing the cloud benefits and to assemble a trust to precede onward cloud. In the event that security is tight and the specialist organizations can guarantee that any interruption endeavor to their information can be observed, followed and confirmed. In this paper, we proposed ranking system using Mamdani fuzzifier. After performing different ranking conditions, like, if compliance is 14.3, Data Protection 28.2, Availability 19.7 and recovery is 14.7 then cloud health is 85% and system will respond in result of best cloud health services.

Suicide is the understudied subject in Pakistan that is a cause of death all over the world. Seventy-fivepercent of suicide occurs in LMIC.In Pakistan information about suicide is limited. The study is about tofind the number of suicide from major cities of Pakistan and then predict the number of suicides by usingNeural Networks Algorithm. About 24639 cases were found in our research from 2001-18 in majorcities of Pakistan. Hanging and poisoning were the most common methods of suicide. The peak age ofsuicide committers was 20-35 included males and females. The lowest number of suicide was inBahawalpur (130 from 2001 to 2018) and the Highest was in Lahore (5925 from 2001 to 2018).