Honey bee based trust management system for cloud computing

Cloud computing has been considered as the new computing paradigm that would offer computer resources over the Internet as service.With the widespread use of cloud, computing would become another utility similar to electricity, water, gas and telephony where the customer would be paying only for the services consumed contrary to the current practice of paying a monthly or annual fixed charge irrespective of use.For cloud computing to become accepted by everybody, several issues need to be resolved.One of the most important issues to be addressed is cloud security.Trust management is one of the important components of cloud security that requires special attention. In this paper, the authors propose the concept that honey bee algorithm which has been developed to solve complex optimization problems can be successfully used to address this issue.The authors have taken a closer look at the optimization problems that had been solved using the honey bee algorithm and the similarity between these problems and the cloud computing environment.Thus concluding that the honey bee algorithm could be successfully used to solve the trust management issue in cloud computing

A GENERIC TRUST MANAGEMENT FRAMEWORK FOR HETEROGENEOUS SENSORS IN CYBER PHYSICAL SYSTEMS

Objective: Wireless Technology†is the magic word in today's era. In which, Cyber Physical Systems (CPS) is the booming world which binds the physical world and cyber world together. The CPS is also called as Safety Critical System because of the human life involvement. In this emerging technology, lots of heterogeneous sensors are involved and each sensor will play an important role. If something goes wrong with sensor or sensor data. It will definitely affect the human life involved in it.Methods: In this paper, we proposed a generic trust management framework for heterogeneous sensors which will detect the sensor data falsification (Data Integrity), faulty sensor reading, and packet dropping nodes (Selfish Nodes) through rules and rating concept.Results: The efficiency of the proposed framework is evaluated with the help of Network Simulator 2 (NS-2.35). The maximum numbers of untrusted nodes are identified in point 0.40 than Multi-Level Trust Framework for Wireless Sensor Network (MTF-WSN) and Framework for Packet-Droppers Mitigation (FPDM). It is also evident that Trust Management Framework for Cyber Physical Systems (TRMF-CPS) identifies maximum number of untrusted nodes in the detection range of 0.35 and 0.45. Therefore, 0.35 and 0.45 are considered as maximum and minimum threshold points for effective untrusted nodes. Conclusion:The experimentation results and comparative study shows that, our trust management framework will easily detected sensors which misbehave.Â

Use of prediction based meter reputation factors in power systems

Reputation systems provide a protocol for participants to interact based on their past performance. The concept of a prediction based meter reputation factor is introduced as a number between 0.1 and 1 that is assigned to every meter and that varies based on the accuracy of a meter's predictions. A system architecture is presented that allows the instantiation of rules for economic interaction between metered participants in a power system using reputation factors. This will create a system in which individuals are incentivised to provide accurate predictions, giving planners more reliable information. It also provides a basis for the allocation of rewards for flexibility and penalties for inflexibility. Two algorithms to allocate meter reputation factors are presented and assessed using a defined performance index and metering information from the OpenLV project. It is demonstrated that the performance of the meter reputation algorithms can be moderated according to system requirements. It is concluded that instantiation of the algorithms in such a way that makes persecution of individuals impossible is crucial

Robust multi-dimensional trust computing mechanism for cloud computing

Cloud computing has become the most promising way of purchasing computing resources over the Internet.The main advantage of .cloud computing is its economic advantages over the traditional computing resource provisioning.For cloud computing to become acceptable to wider audience, it is necessary to maintain the quality of service (QoS) commitments specified in the service level agreement.In this paper, the authors propose a robust multi-level trust computing mechanism that can be used to track the performance of cloud systems using multiple QoS attributes.In addition, tests carried out show that the proposed mechanism is more robust than the ones published in the literature

FSDA: Framework for Secure Data Aggregation in Wireless Sensor Network for Enhancing Key Management

An effective key management plays a crucial role in imposing a resilient security technique in Wireless Sensor Network (WSN). After reviewing the existing approaches of key management, it is confirmed that existing approachs does not offer good coverage on all potential security breaches in WSN. With WSN being essential part of Internet-of-Things (IoT), the existing approaches of key management can definitely not address such security breaches. Therefore, this paper introduces a Framework for Secure Data Aggregation (FSDA) that hybridizes the public key encryption mechanism in order to obtain a novel key management system. The proposed system does not target any specific attacks but is widely applicable for both internal and external attacks in WSN owing to its design principle. The study outcome exhibits that proposed FSDA offers highly reduced computational burden, minimal delay, less energy consumption, and higher data transmission perforance in contrast to frequency used encryption schemes in WSN

Building Ethics into Artificial Intelligence

As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies

CRM: a new dynamic cross-layer reputation computation model in wireless networks

This is the author accepted manuscript. The final version is available from University Press (OUP) via the DOI in this record.Multi-hop wireless networks (MWNs) have been widely accepted as an indispensable component of next-generation communication systems due to their broad applications and easy deployment without relying on any infrastructure. Although showing huge benefits, MWNs face many security problems, especially the internal multi-layer security threats being one of the most challenging issues. Since most security mechanisms require the cooperation of nodes, characterizing and learning actions of neighboring nodes and the evolution of these actions over time is vital to construct an efficient and robust solution for security-sensitive applications such as social networking, mobile banking, and teleconferencing. In this paper, we propose a new dynamic cross-layer reputation computation model named CRM to dynamically characterize and quantify actions of nodes. CRM couples uncertainty based conventional layered reputation computation model with cross-layer design and multi-level security technology to identify malicious nodes and preserve security against internal multi-layer threats. Simulation results and performance analyses demonstrate that CRM can provide rapid and accurate malicious node identification and management, and implement the security preservation against the internal multi-layer and bad mouthing attacks more effectively and efficiently than existing models.The authors would like to thank anonymous reviewers and editors for their constructive comments. This work is supported by: 1. Changjiang Scholars and Innovative Research Team in University (IRT1078), 2. the Key Program of NSFC-Guangdong Union Foundation (U1135002), 3. National Natural Science Foundation of China (61202390), 4. Fujian Natural Science Foundation：2013J01222, 5. the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications, Ministry of Education)