Vulnerability Analysis and Prevention on Software as a Service (SaaS) of Archive Websites

Web Archive is a SaaS service that has an important role in providing better document storage and management. Good document management has a positive impact on optimizing business operations, increasing collaboration, reducing costs, and protecting sensitive information. Cybercrime, which has an increasingly high intensity, is a serious threat to the security of data stored in web archives. This research aims to improve data security on web archives by conducting ongoing testing. Testing was carried out on a server with a Linux operating system and web archives managed by a file manager system. This study tests the attack using the OWASP application method, and an XSS attack on a web archive with a Linux server and using a file management application. The testing phase includes Information Gathering, Vulnerability Assessment, Exploiting, and Reporting. Based on the results of the research, it was obtained that the first vulnerability test contained 9 vulnerabilities in 9 categories. The second vulnerability test obtained 7 vulnerabilities and the third test found no vulnerabilities. At the end of each test, recommendations for improvements to the web archive are made to the web archive manager and a re-testing process for vulnerabilities is carried out. This process is carried out repeatedly with continuous improvement. Testing the attack and repair of the web archive was carried out repeatedly and managed to get a vulnerability level of Level 0.1-3.9 points with Low status

Multi-robot region-of-interest reconstruction with Dec-MCTS

© 2019 IEEE. We consider the problem of reconstructing regions of interest of a scene using multiple robot arms and RGB-D sensors. This problem is motivated by a variety of applications, such as precision agriculture and infrastructure inspection. A viewpoint evaluation function is presented that exploits predicted observations and the geometry of the scene. A recently proposed non-myopic planning algorithm, Decentralised Monte Carlo tree search, is used to coordinate the actions of the robot arms. Motion planning is performed over a navigation graph that considers the high-dimensional configuration space of the robot arms. Extensive simulated experiments are carried out using real sensor data and then validated on hardware with two robot arms. Our proposed targeted information gain planner is compared to state-of-the-art baselines and outperforms them in every measured metric. The robots quickly observe and accurately detect fruit in a trellis structure, demonstrating the viability of the approach for real-world applications

Multi-modal active perception for information gathering in science missions

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Robotic science missions in remote environments, such as deep ocean and outer space, can involve studying phenomena that cannot directly be observed using on-board sensors but must be deduced by combining measurements of correlated variables with domain knowledge. Traditionally, in such missions, robots passively gather data along prescribed paths, while inference, path planning, and other high level decision making is largely performed by a supervisory science team located at a different location, often at a great distance. However, communication constraints hinder these processes, and hence the rate of scientific progress. This paper presents an active perception approach that aims to reduce robots’ reliance on human supervision and improve science productivity by encoding scientists’ domain knowledge and decision making process on-board. We present a Bayesian network architecture to compactly model critical aspects of scientific knowledge while remaining robust to observation and modeling uncertainty. We then formulate path planning and sensor scheduling as an information gain maximization problem, and propose a sampling-based solution based on Monte Carlo tree search to plan informative sensing actions which exploit the knowledge encoded in the network. The computational complexity of our framework does not grow with the number of observations taken and allows long horizon planning in an anytime manner, making it highly applicable to field robotics with constrained computing. Simulation results show statistically significant performance improvements over baseline methods, and we validate the practicality of our approach through both hardware experiments and simulated experiments with field data gathered during the NASA Mojave Volatiles Prospector science expedition