Wildfires pose increasing environmental and economic threats, making early and accurate detection essential to minimize their impact. This paper presents an AI driven UAV surveillance system designed to balance wildfire detection accuracy with energy efficiency. The system operates in two stages: UAVs conduct continuous low resolution monitoring and switch to high resolution imaging only when potential fire signals are detected. A custom Convolutional Neural Network (CNN) was trained on a curated dataset of fire and non fire images with diverse environmental conditions. Performance was evaluated using accuracy, precision, recall, and F1 score, demonstrating that the dual resolution approach improves fire precision to 0.96 while reducing false positives, achieving an overall accuracy of 83 percent. An energy analysis shows that the selective two stage model achieves 64.4 percent lower energy consumption compared to continuous high resolution imaging, extending UAV endurance. This work introduces a scalable and cost effective approach to UAV based wildfire surveillance, highlighting tradeoffs between accuracy, latency, and energy, and setting the stage for real world deployments in wildfire prone regions.

HTML and JavaScript have become the go-to languages for many developers making apps for multiple smartphone operating systems. HTML provides developers with a way to make a universal framework to build cross-platform apps. This convenience does not come without risk, however. Being HTML-based, these apps are susceptible to Cross-Site Scripting (XSS). In this paper, we break down what a cross-site scripting attack is, how it works, and the security risks that come with it. We also investigate several different methods used to detect and path code with vulnerability.

Every day, web credibility is becoming an increasingly important. It affects how we interact with information on the internet as well as the quality of those interactions. Web credibility also spills into the real world, as misinformation from the internet can have very real consequences and catastrophic losses. It also sets an aggressive challenge of choice among different web services in the internet user community. In this paper, we investigate the existing practice and research work on evaluating web service credibility. We classify the available techniques and discuss their capabilities and impacts on user/web interactions. This work will help discover the venues for credibility measures and highlight the effective techniques in different web service domains. It will also help build more rigorous techniques to disseminate credibility measures and support internet user choices.

In recent years, security has been a major concern to everyone. Some devices like energy meters, health appliances have been a life changing technology for smart city. These devices have assisted in connecting several different services and in turn have made life better for people living in smart city. Other services such as emergency responder, water system, disaster management, sanitation and infrastructure have evolved with the birth of a smart city. The rapid advancements in services provided in a smart city are now drawing major security concerns. One of the major security concerns are integrated systems. The purpose of this paper is to educate the audience of the vulnerabilities that are associated with the devices of a smart city. We demonstrate an example of hands-on lab hacking a CCTV camera and some suggestions to prevent attacks on smart city.

While the number of mobile and web applications is growing exponentially, the mobile and web security threat landscape is growing explosively. Malicious malware may attack vulnerable applications and obtain personal or enterprise confidential data anywhere and anytime. Most vulnerabilities should be addressed and fixed during the early stages of software development. However, many software development professionals lack the awareness of the importance of security vulnerabilities and the necessary knowledge and skills at the software development stage. This paper addresses the needs and challenges of the lack of pedagogical materials and real-world learning environment in ProActive Control for Software Security (PASS) through effective, engaging, and investigative authentic learning approaches.

Predicting potential failure occurrences during runtime is important to achieve system resilience and avoid hazardous consequences of failures. Existing failure prediction techniques in software systems involve forecasting failure counts, effects, and occurrences. Most of these techniques predict failures that may occur in future runtime intervals and only few techniques predict them at runtime. However, they do not estimate the failure modes and they require extensive instrumentation of source code. In this paper, we provide an approach for predicting failure occurrences and modes during system runtime. Our methodology utilizes system error log records to craft runtime error-spread signature. Using system error log history, we determine a predictive function (estimator) for each failure mode based on these signatures. This estimator can be used to predict a failure mode eventuality measure (a probability of failure mode occurrence) from system error log during system runtime. An experimental evaluation using PostgreSQL opensource database is provided. Our results show high accuracy of failure occurrence and mode predictions.