In the current scenario most of the business enterprises are running through web applications. But the major drawback is that they fail to provide a secure environment. To overcome this security issue in web applications, there are many vulnerability detection tools are available at present. But these tools are not proactive and consistent as it does not adapt to all kinds of recent updates and is unable to track new emerging vulnerabilities. For the longterm functioning of a business enterprise, statistical data with efficient analytics on vulnerabilities is required to enhance its security impacts. Predictive Analytics is a powerful solution to effectively arm the recent incident response to modern-day threats. Predictive Analytics provides a proactive and decision-making approach and insights into how well security programs are working. It can also help to identify problem areas and can warn about imminent or active attacks in heterogeneous web applications to enhance the former features and analyze the origin and pattern of the attack in a more effective manner. The pattern analyzed through research is given as an input to the Machine Learning techniques such as Deterministic Arithmetic Automata (DAA), Probabilistic Arithmetic Automata (PAA) to predict the probabilistic value as an output. From the obtained probabilistic values, we can detect the cause of an attack, prevent the heterogeneous web application of business enterprises from further impacts and find the penetration level of an attack from web application to web service.INDEX TERMS Security Analytics, Deterministic Arithmetic Automata (DAA), Probabilistic Arithmetic Automata (PAA), heterogeneous web application.

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Real-time traffic monitoring and controlling are one of the biggest problems in this present living world. So many researchers have dealt with and put their effort into this problem, as a result, several types of approaches have developed. Currently using traffic monitoring and alert systems are not up to the needs of smart city applications and more expensive. This paper proposes an Internet of Things (IoT) based Smart Real-Time Traffic Monitoring System to provide better service with low cost for Smart city applications using semantic annotations. The proposed framework contains two phases namely-traffic monitoring unit and traffic reduction unit. The first phase analyses semantic traffic to detect an emergency, the latter phase removes redundant semantic information for traffic reduction. Simulation results suggest that the framework is capable of accurate and early detection of an emergency as well as traffic reduction while keeping sufficient information to report the emergency.

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