The Application of Fuzzy Logic Controller to Compute a Trust Level for Mobile Agents in a Smart Home

Agents that travel through many hosts may cause a threat on the security of the visited hosts. Assets, system resources, and the reputation of the host are few possible targets for such an attack. The possibility for multi-hop agents to be malicious is higher compared to the one-hop or two-hop boomerang agents. The travel history is one of the factors that may allow a server to evaluate the trustworthiness of an agent. This paper proposes a technique to define levels of trust for multi-hop agents that are roaming in a smart home environment. These levels of trust are used later to determine actions taken by a host at the arrival of an agent. This technique uses fuzzy logic as a method to calculate levels of trust and to define protective actions in regard to those levels

A Trust Based Fuzzy Algorithm for Congestion Control in Wireless Multimedia Sensor Networks (TFCC)

Network congestion has become a critical issue for resource constrained Wireless Sensor Networks (WSNs), especially for Wireless Multimedia Sensor Networks (WMSNs)where large volume of multimedia data is transmitted through the network. If the traffic load is greater than the available capacity of the sensor network, congestion occurs and it causes buffer overflow, packet drop, deterioration of network throughput and quality of service (QoS). Again, the faulty nodes of the network also aggravate congestion by diffusing useless packets or retransmitting the same packet several times. This results in the wastage of energy and decrease in network lifetime. To address this challenge, a new congestion control algorithm is proposed in which the faulty nodes are identified and blocked from data communication by using the concept of trust. The trust metric of all the nodes in the WMSN is derived by using a two-stage Fuzzy inferencing scheme. The traffic flow from source to sink is optimized by implementing the Link State Routing Protocol. The congestion of the sensor nodes is controlled by regulating the rate of traffic flow on the basis of the priority of the traffic. Finally we compare our protocol with other existing congestion control protocols to show the merit of the work.Comment: 6 pages, 5 figures, conference pape

An Intelligent Trust Cloud Management Method for Secure Clustering in 5G enabled Internet of Medical Things

5G edge computing enabled Internet of Medical Things (IoMT) is an efficient technology to provide decentralized medical services while Device-to-device (D2D) communication is a promising paradigm for future 5G networks. To assure secure and reliable communication in 5G edge computing and D2D enabled IoMT systems, this paper presents an intelligent trust cloud management method. Firstly, an active training mechanism is proposed to construct the standard trust clouds. Secondly, individual trust clouds of the IoMT devices can be established through fuzzy trust inferring and recommending. Thirdly, a trust classification scheme is proposed to determine whether an IoMT device is malicious. Finally, a trust cloud update mechanism is presented to make the proposed trust management method adaptive and intelligent under an open wireless medium. Simulation results demonstrate that the proposed method can effectively address the trust uncertainty issue and improve the detection accuracy of malicious devices

Malicious vehicle detection based on beta reputation and trust management for secure communication in smart automotive cars network

High reliance on wireless network connectivity makes the vehicular ad hoc network (VANET) vulnerable to several kinds of cyber security threats. Malicious vehicles accessing the network can lead to hazardous situation by disseminating misleading information or data in the network or by performing cyber-attacks. It is a requirement that the information must be originated from the authentic and authorized vehicle and confidentiality must be maintained. In these circumstances, to protect the network from malicious vehicles, reputation system based on beta probability distribution with trust management model has been proposed to differentiate trustworthy vehicles from malicious vehicles. The trust model is based on adaptive neuro fuzzy inference system (ANFIS) which takes trust metrics as input to evaluate the trustworthiness of the vehicles. The simulation platform for the model is in MATLAB. Simulation results show that the vehicles need at least 80% trustworthiness to be considered as a trusted vehicle in the network

A survey of intrusion detection techniques in Cloud

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks