Biblio

Radio frequency identification wireless sensing technology widely adopted and developed from last decade and has been utilized for monitoring and autonomous identification of objects. However, wider utilization of RFID technologies has introduced challenges such as preserving security and privacy of sensitive data while maintaining the high quality of service. Thus, in this work, we will deliberately build up a RFID system by utilizing learning automata based multi agent intelligent system to greatly enhance and secure message transactions and to improve operational efficiency. The incorporation of these two advancements and technological developments will provide maximum benefit in terms of expertly and securely handle data in RFID scenario. In proposed work, learning automata inbuilt RFID tags or assumed players choose their optimal strategy via enlarging its own utility function to achieve long term benefit. This is possible if they transmit their utility securely to back end server and then correspondingly safely get new utility function from server to behave optimally in its environment. Hence, our proposed authentication protocol, expertly transfer utility from learning automata inbuilt tags to reader and then to server. Moreover, we verify the security and privacy of our proposed system by utilizing automatic formal prover Scyther tool.

In a continually evolving cyber-threat landscape, the detection and prevention of cyber attacks has become a complex task. Technological developments have led organisations to digitise the majority of their operations. This practice, however, has its perils, since cybespace offers a new attack-surface. Institutions which are tasked to protect organisations from these threats utilise mainly network data and their incident response strategy remains oblivious to the needs of the organisation when it comes to protecting operational aspects. This paper presents a system able to combine threat intelligence data, attack-trend data and organisational data (along with other data sources available) in order to achieve automated network-defence actions. Our approach combines machine learning, visual analytics and information from business processes to guide through a decision-making process for a Security Operation Centre environment. We test our system on two synthetic scenarios and show that correlating network data with non-network data for automated network defences is possible and worth investigating further.