Reputation based Trust Management System for Improving Public Health Care System in Pakistan

Health is the most important facility for the people by the government if it is provided properly.There is a lack of trust among people about healthcare system particularly in public hospitals ofPakistan. People do not trust doctors and hospitals due to poor management and low standardscheck-up by the doctors. This is due to the lack of feedback system which leads to themismanagement of healthcare institutions. In this research, we have developed a reputation-basedfeedback management system that will overcome all the problems related to the trust of the patientsregarding their medical care. This will enhance the system and will let doctors, staff, andmanagement to work honestly to make their repute well

Trust and Reputation Management for Blockchain-enabled IoT

In recent years, there has been an increasing interest in incorporating blockchain for the Internet of Things (IoT) to address the inherent issues of IoT, such as single point of failure and data silos. However, blockchain alone cannot ascertain the authenticity and veracity of the data coming from IoT devices. The append-only nature of blockchain exacerbates this issue, as it would not be possible to alter the data once recorded on-chain. Trust and Reputation Management (TRM) is an effective approach to overcome the aforementioned trust issues. However, designing TRM frameworks for blockchain-enabled IoT applications is a non-trivial task, as each application has its unique trust challenges with their unique features and requirements. In this paper, we present our experiences in designing TRM framework for various blockchain-enabled IoT applications to provide insights and highlight open research challenges for future opportunities.Comment: COMSNETS 2023 Invited Pape

Implementation of Secure and Energy Efficient Routing Protocol for Mobile Adhoc Network

Mobile adhoc network are networks consisting of spatially distributed autonomous sensors, which are capable of sensing the physical or environmental conditions and have set of applications in various domains. But MANET is also prone to various active and passive attacks due to the lack of security mechanism, centralized management in routing protocol and. The prime task of WSN is to sense and collect information, process and transmit to the sink. One of the major security threats in MANET is attacks; attacks may be active or passive. First of all implementation of reference work carried out in NS 2 environment for various numbers of nodes in the range from 10 to 50 followed by integration of attacker node. In our research work specifically black hole attack has been taken to see the impact on network parameters. To overcome such active attacks an advanced Ad hoc On-Demand Distance Vector routing protocol techniques incorporated hash function with security algorithm so that data cannot be accessed by unauthorized person. Network matrices are improved by implementing advanced AODV routing protocol. In the distributed network trust among various sensing nodes is a powerful tool to increase the performance of device networks. In our research work depth analysis carried out on the security and trust communication between the device nodes with routing techniques to discover and prevent information packet from the being exposed to black hole attack. Further various mobility pattern can be investigated with different attacks

A Secure Integrated Framework for Fog-Assisted Internet of Things Systems

Fog-Assisted Internet of Things (Fog-IoT) systems are deployed in remote and unprotected environments, making them vulnerable to security, privacy, and trust challenges. Existing studies propose security schemes and trust models for these systems. However, mitigation of insider attacks, namely blackhole, sinkhole, sybil, collusion, self-promotion, and privilege escalation, has always been a challenge and mostly carried out by the legitimate nodes. Compared to other studies, this paper proposes a framework featuring attribute-based access control and trust-based behavioural monitoring to address the challenges mentioned above. The proposed framework consists of two components, the security component (SC) and the trust management component (TMC). SC ensures data confidentiality, integrity, authentication, and authorization. TMC evaluates Fog-IoT entities’ performance using a trust model based on a set of QoS and network communication features. Subsequently, trust is embedded as an attribute within SC’s access control policies, ensuring that only trusted entities are granted access to fog resources. Several attacking scenarios, namely DoS, DDoS, probing, and data theft are designed to elaborate on how the change in trust triggers the change in access rights and, therefore, validates the proposed integrated framework’s design principles. The framework is evaluated on a Raspberry Pi 3 Model B to benchmark its performance in terms of time and memory complexity. Our results show that both SC and TMC are lightweight and suitable for resource-constrained devices

On the Secure and Resilient Design of Connected Vehicles: Methods and Guidelines

Vehicles have come a long way from being purely mechanical systems to systems that consist of an internal network of more than 100 microcontrollers and systems that communicate with external entities, such as other vehicles, road infrastructure, the manufacturer’s cloud and external applications. This combination of resource constraints, safety-criticality, large attack surface and the fact that millions of people own and use them each day, makes securing vehicles particularly challenging as security practices and methods need to be tailored to meet these requirements.This thesis investigates how security demands should be structured to ease discussions and collaboration between the involved parties and how requirements engineering can be accelerated by introducing generic security requirements. Practitioners are also assisted in choosing appropriate techniques for securing vehicles by identifying and categorising security and resilience techniques suitable for automotive systems. Furthermore, three specific mechanisms for securing automotive systems and providing resilience are designed and evaluated. The first part focuses on cyber security requirements and the identification of suitable techniques based on three different approaches, namely (i) providing a mapping to security levels based on a review of existing security standards and recommendations; (ii) proposing a taxonomy for resilience techniques based on a literature review; and (iii) combining security and resilience techniques to protect automotive assets that have been subject to attacks. The second part presents the design and evaluation of three techniques. First, an extension for an existing freshness mechanism to protect the in-vehicle communication against replay attacks is presented and evaluated. Second, a trust model for Vehicle-to-Vehicle communication is developed with respect to cyber resilience to allow a vehicle to include trust in neighbouring vehicles in its decision-making processes. Third, a framework is presented that enables vehicle manufacturers to protect their fleet by detecting anomalies and security attacks using vehicle trust and the available data in the cloud

Face Biometric Cloud Authentication Access Using Extreme Learning Class Specific Linear Discriminant Regression Classification Method

The Extreme Learning Class Specific Linear Discriminant Regression Classification used in this proposed system aims at improving the accuracy and recognition rate of the face biometric identification for secured cloud access. The accuracy is improved by maximizing and minimizing the reconstruction error. The between class reconstruction error (BCRE) and within-class reconstruction error (WCRE) are the two values simultaneously increased and decreased for every sample to provide improved accuracy. By selecting the suitable value of WCRE, the learned projection matrix for the discriminant subspace is identified. The class specific representation is implemented for the label created in feature vector to further improve the efficiency of identifying a face. Based on the classification results given by the proposed EL-CSLDRC method, an efficient access of secured data from the big data cloud system is promoted