NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

The inference problem in multilevel secure databases

Conventional access control models, such as role-based access control, protect sensitive data from unauthorized disclosure via direct accesses, however, they fail to prevent unauthorized disclosure happening through indirect accesses. Indirect data disclosure via inference channels occurs when sensitive information can be inferred from nonsensitive data and metadata, which is also known as “the inference problem”. This problem has draw n much attention from researcher in the database community due to its great compromise of data security. It has been studied under four settings according to where it occurs. They are statistical databases, multilevel secure databases, data mining, and web-based applications. This thesis investigates previous efforts dedicated to inference problems in multilevel secure databases, and presents the latest findings of our research on this problem. Our contribution includes two methods. One is a dynamic control over this problem, which designs a set of accessing key distribution schemes to remove inference after all inference channels in the database has been identified. The other combines rough sets and entropies to form a computational solution to detect and remove inferences, which for the first time provides an integrated solution to the inference problem. Comparison with previous work has also been done, and we have proved both of them are effective and easy to implement. Since the inference problem is described as a problem of detecting and removing inference channels, this thesis contains two main parts: inference detecting techniques and inference removing techniques. In both two aspects, some techniques are selectively but extensively examined

A comprehensive view on quantity based aggregation for cadastral databases

Quantity Based Aggregation (QBA) control is a subject that is closely related to inference control in databases. The goal is to enforce k out of n disclosure control. In this paper we work on QBA problems in the context of cadastral databases: how to prevent a user from knowing 1) the owners of all parcels in a region, and 2) all parcels belonging to the same owner. This work combines and extends our previous work on the subject [1, 2, 3]. We overview the legislative context surrounding cadastral databases. We give important definitions related to the QBA concept. We present a complete model for QBA control in cadastral databases. We show how to implement the security policy efficiently, and we present our prototype of secure cadastral databases with some performance evaluations

Database and Data Mining in Social Networking

Today’s data driven world exploiting the latest trends of database and its allied technologies like Data Warehouse and Data Mining. Data Mining in recent years emerged as one of the most efficient database technique proved to be very reliable almost in every organisation enabling to find previously unknown hidden data patterns for the benefit of organisation. At the same time it is imposing serious problems concerned to data privacy and its potential misuse

Minimization of DDoS false alarm rate in Network Security; Refining fusion through correlation

Intrusion Detection Systems are designed to monitor a network environment and generate alerts whenever abnormal activities are detected. However, the number of these alerts can be very large making their evaluation a difficult task for a security analyst. Alert management techniques reduce alert volume significantly and potentially improve detection performance of an Intrusion Detection System. This thesis work presents a framework to improve the effectiveness and efficiency of an Intrusion Detection System by significantly reducing the false positive alerts and increasing the ability to spot an actual intrusion for Distributed Denial of Service attacks. Proposed sensor fusion technique addresses the issues relating the optimality of decision-making through correlation in multiple sensors framework. The fusion process is based on combining belief through Dempster Shafer rule of combination along with associating belief with each type of alert and combining them by using Subjective Logic based on Jøsang theory. Moreover, the reliability factor for any Intrusion Detection System is also addressed accordingly in order to minimize the chance of false diagnose of the final network state. A considerable number of simulations are conducted in order to determine the optimal performance of the proposed prototype

Flexible Information-Flow Control

As more and more sensitive data is handled by software, its trustworthinessbecomes an increasingly important concern. This thesis presents work on ensuringthat information processed by computing systems is not disclosed to thirdparties without the user\u27s permission; i.e. to prevent unwanted flows ofinformation. While this problem is widely studied, proposed rigorousinformation-flow control approaches that enforce strong securityproperties like noninterference have yet to see widespread practical use.Conversely, lightweight techniques such as taint tracking are more prevalent inpractice, but lack formal underpinnings, making it unclear what guarantees theyprovide.This thesis aims to shrink the gap between heavyweight information-flow controlapproaches that have been proven sound and lightweight practical techniqueswithout formal guarantees such as taint tracking. This thesis attempts toreconcile these areas by (a) providing formal foundations to taint trackingapproaches, (b) extending information-flow control techniques to more realisticlanguages and settings, and (c) exploring security policies and mechanisms thatfall in between information-flow control and taint tracking and investigating whattrade-offs they incur