Soa performance enhancement through xml fragment caching

10.1287/isre.1110.0368Information Systems Research232505-53

Can BlockChain Technology Provide Information Systems with Trusted Database? The Case of HyperLedger Fabric

BlockChain technology has imposed a new perspective in the area of data management, i.e., the possibility of realizing immutable and distributed ledgers. Furthermore, the introduction of the concept of smart contract has further extended the potential applicability of this potentially disruptive technology. Although BlockChain was developed to support virtual currencies and is usually associated with them, novel platforms are under development, that are not at all related to the original application context. An example is HyperLedger Fabric. Developed by the Linux Foundation, it is aimed to provide information systems with distributed databases where the transaction log is immutable. This should ensure trusted cooperation among many parties. In this paper, we briefly present main concepts and functionalities provided by HyperLedger Fabric. We then discuss its potential applicability and current limitations

A state machine system for insider threat detection

The risk from insider threats is rising significantly, yet the majority of organizations are ill-prepared to detect and mitigate them. Research has focused on providing rule-based detection systems or anomaly detection tools which use features indicative of malicious insider activity. In this paper we propose a system complimentary to the aforementioned approaches. Based on theoretical advances in describing attack patterns for insider activity, we design and validate a state-machine system that can effectively combine policies from rule-based systems and alerts from anomaly detection systems to create attack patterns that insiders follow to execute an attack. We validate the system in terms of effectiveness and scalability by applying it on ten synthetic scenarios. Our results show that the proposed system allows analysts to craft novel attack patterns and detect insider activity while requiring minimum computational time and memory