Computer Forensics: Dark Net Forensic Framework and Tools Used for Digital Evidence Detection

As the development of technology increases and its use becomes increasingly more widespread, computer crimes grow. Hence, computer forensics research is becoming more crucial in developing good forensic frameworks and digital evidence detection tools to deter more cyber-attacks. In this paper, we explore the science of computer forensics, a dark web forensic framework, and digital evidence detection tools

Lightweight wireless network authentication scheme for constrained oracle sensors

x, 212 leaves : ill. (some col.) ; 29 cmIncludes abstract and appendices.Includes bibliographical references (leaves 136-147).With the significant increase in the dependence of contextual data from constrained IoT, the blockchain has been proposed as a possible solution to address growing concerns from organizations. To address this, the Lightweight Blockchain Authentication for Constrained Sensors (LBACS) scheme was proposed and evaluated using quantitative and qualitative methods. LBACS was designed with constrained Wireless Sensor Networks (WSN) in mind and independent of a blockchain implementation. It asserts the authentication and provenance of constrained IoT on the blockchain utilizing a multi-signature approach facilitated by symmetric and asymmetric methods and sufficient considerations for key and certificate registry management. The metrics, threat assessment and comparison to existing WSN authentication schemes conducted asserted the pragmatic use of LBACS to provide authentication, blockchain provenance, integrity, auditable, revocation, weak backward and forward secrecy and universal forgeability. The research has several implications for the ubiquitous use of IoT and growing interest in the blockchain

Improving reliability of service oriented systems with consideration of cost and time constraints in clouds

Web service technology is more and more popular for the implementation of service oriented systems. Additionally, cloud computing platforms, as an efficient and available environment, can provide the computing, networking and storage resources in order to decrease the budget of companies to deploy and manage their systems. Therefore, more service oriented systems are migrated and deployed in clouds. However, these applications need to be improved in terms of reliability, for certain components have low reliability. Fault tolerance approaches can improve software reliability. However, more redundant units are required, which increases the cost and the execution time of the entire system. Therefore, a migration and deployment framework with fault tolerance approaches with the consideration of global constraints in terms of cost and execution time may be needed. This work proposes a migration and deployment framework to guide the designers of service oriented systems in order to improve the reliability under global constraints in clouds. A multilevel redundancy allocation model is adopted for the framework to assign redundant units to the structure of systems with fault tolerance approaches. An improved genetic algorithm is utilised for the generation of the migration plan that takes the execution time of systems and the cost constraints into consideration. Fault tolerant approaches (such as NVP, RB and Parallel) can be integrated into the framework so as to improve the reliability of the components at the bottom level. Additionally, a new encoding mechanism based on linked lists is proposed to improve the performance of the genetic algorithm in order to reduce the movement of redundant units in the model. The experiments compare the performance of encoding mechanisms and the model integrated with different fault tolerance approaches. The empirical studies show that the proposed framework, with a multilevel redundancy allocation model integrated with the fault tolerance approaches, can generate migration plans for service oriented systems in clouds with the consideration of cost and execution time