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.
In WBAN, energy efficiency is a major concern. The sensor nodes attached to the human body are battery-powered devices with a finite lifespan. These sensor nodes assist in gathering biological data from the human body and transmitting it to a control device. In WBAN, the MAC protocol is critical in evaluating a protocol's energy efficiency. Traditional MAC protocols aim to boost throughput and bandwidth efficiency. The most critical aspect is that they lack in energy conserving mechanism. By employing correct control techniques that aid in the efficient use of energy resources, the useful network life time can be extended. Several MAC protocols for WBAN have been devised to reduce energy consumption, and packet collision, idle listening, overhearing, and control packet overhead are the main causes of energy waste in wireless networks. Idle listening, packet overhead, overhearing, and collision rate are all addressed by the energy-saving technique. In WBAN, we introduced a novel energy-efficient MAC protocol called Scheduled Access MAC (SAMAC) to extend the network life time without sacrificing QoS. Using the Castalia simulator, we analyze and compare the performance of our proposed SAMAC to that of the Baseline MAC (IEEE 802.15.6) and ZigBee MAC (IEEE 802.15.4) in terms of energy consumption, packet delivery ratio, and endto-end delay. In terms of both energy conversion and WBAN Quality of Service, our simulation results suggest that our proposed SAMAC is more efficient than Baseline MAC and ZigBee MAC.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.