Antesar M. Shabut

Recommendation Based Trust Model with an Effective Defence Scheme for MANETs

The reliability of delivering packets through multi-hop intermediate nodes is a significant issue in the mobile ad hoc networks (MANETs). The distributed mobile nodes establish connections to form the MANET, which may include selfish and misbehaving nodes. Recommendation based trust management has been proposed in the literature as a mechanism to filter out the misbehaving nodes while searching for a packet delivery route. However, building a trust model that adopts recommendations by other nodes in the network is a challenging problem due to the risk of dishonest recommendations like bad-mouthing, ballot-stuffing, and collusion. This paper investigates the problems related to attacks posed by misbehaving nodes while propagating recommendations in the existing trust models. We propose a recommendation based trust model with a defence scheme, which utilises clustering technique to dynamically filter out attacks related to dishonest recommendations between certain time based on number of interactions, compatibility of information and closeness between the nodes. The model is empirically tested under several mobile and disconnected topologies in which nodes experience changes in their neighbourhood leading to frequent route changes. The empirical analysis demonstrates robustness and accuracy of the trust model in a dynamic MANET environment.

Enhancing Dynamic Recommender Selection Using Multiple Rules for Trust and Reputation Models in MANETs

Trust and reputation models are utilised by several researchers as one vital factor in the security mechanisms in MANETs to deal with selfish and misbehaving nodes and ensure packet delivery from source to destination. However, in the presence of new attacks, it is important to build a trust model to resist countermeasures related to propagation of dishonest recommendations, and aggregation which may easily degrade the effectiveness of using trust models in a hostile environment such as MANETs. However, dealing with dishonest recommendation attacks in MANETs remains an open and challenging area of research. In this work, we propose a dynamic selection algorithm to filter out recommendations in order to achieve resistance against certain existing attacks such as bad-mouthing and ballot-stuffing. The selection algorithm is based on three different rules: (i)majority rule based, (ii) personal experience based, and (iii)service reputation based. Recommendations are clustered, filtered, and selected based on these three rules in order to givethe trust and reputation model greater robustness andaccuracy over the dynamic and changeable MANETenvironment.

Social factors for data sparsity problem of trust models in MANETs

The use of recommendation in trust-based models has its advantages in enhancing the correctness and quality of the rating provided by mobile and autonomous nodes in MANETs. However, building a trust model with a recommender system in dynamic and distributed networks is a challenging problem due to the risk of dishonest recommendations. Dealing with dishonest recommendations can result in the additional problem of data sparsity, which is related to the availability of information in the early rounds of the network time or when nodes are inactive in providing recommendations. This paper investigates the problems of data sparsity and cold start of recommender systems in existing trust models. It proposes a recommender system with clustering technique to dynamically seek similar recommendations based on a certain timeframe. Similarity between different nodes is evaluated based on important attributes includes use of interactions, compatibility of information and closeness between the mobile nodes. The recommender system is empirically tested and empirical analysis demonstrates robustness in alleviating the problems of data sparsity and cold start of recommender systems in a dynamic MANET environment.

A Brain-Inspired Trust Management Model to Assure Security in a Cloud Based IoT Framework for Neuroscience Applications

Abstract Rapid advancement of Internet of Things (IoT) and cloud computing enables neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable data communication between end-to-end (E2E) devices supported by current IoT and cloud infrastructures, trust management is needed at the IoT and user ends. This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) brain-inspired trust management model (TMM) to secure IoT devices and relay nodes, and to ensure data reliability. The proposed TMM utilizes both node behavioral trust and data trust, which are estimated using ANFIS, and weighted additive methods respectively, to assess the nodes trustworthiness. In contrast to existing fuzzy based TMMs, simulation results confirm the robustness and accuracy of our proposed TMM in identifying malicious nodes in the communication network. With growing usage of cloud based IoT frameworks in Neuroscience research, integrating the proposed TMM into existing infrastructure will assure secure and reliable data communication among E2E devices.

Route optimasation based on multidimensional trust evaluation model in mobile ad hoc networks

With the increased numbers of mobile devices working in an ad hoc manner, there are many problems in secure routing protocols. Finding a path between source and destination faces more challenges in Mobile ad hoc network (MANET) environment because of the node movement and frequent topology changes, besides, the dependence on the intermediate nodes to relay packets. Therefore, trust technique is utilised in such environment to secure routing and stimulate nodes to cooperate in packet forwarding process. In this paper, an investigation of the use of trust to choose the optimised path between two nodes is provided. It comes up with a proposal to select the most reliable path based on multidimensional trust evaluation technique to include number of hubs, trust opinion, confidence in providing trust, and energy level of nodes on the path. The model overcomes the limitation of considering only trustworthiness of the nodes on the path and uses a route optimisation approach to select the path between source and destination. The empirical analysis shows robustness and accuracy of the trust model in a dynamic MANET environment.

An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time

Abstract This paper presents an investigation into the development of an intelligent mobile-enabled expert system to perform an automatic detection of tuberculosis (TB) disease in real-time. One third of the global population are infected with the TB bacterium, and the prevailing diagnosis methods are either resource-intensive or time consuming. Thus, a reliable and easy–to-use diagnosis system has become essential to make the world TB free by 2030, as envisioned by the World Health Organisation. In this work, the challenges in implementing an efficient image processing platform is presented to extract the images from plasmonic ELISAs for TB antigen-specific antibodies and analyse their features. The supervised machine learning techniques are utilised to attain binary classification from eighteen lower-order colour moments. The proposed system is trained off-line, followed by testing and validation using a separate set of images in real-time. Using an ensemble classifier, Random Forest, we demonstrated 98.4% accuracy in TB antigen-specific antibody detection on the mobile platform. Unlike the existing systems, the proposed intelligent system with real time processing capabilities and data portability can provide the prediction without any opto-mechanical attachment, which will undergo a clinical test in the next phase.