Biblio

Continuous development of Internet of Things, big data and other emerging technologies has brought new challenges to the reliability of security-critical system products in various industries. Fault detection and evaluation in the early stage of software plays an important role in improving the reliability of software. However, fault prediction and evaluation, which are currently focused on the early stage of software, hardly provide high guidance for actual project development. In this study, a fault diagnosis method based on object-oriented Bayesian network (OOBN) is proposed. Starting from the time dimension and internal logic, a two-dimensional metric fault propagation model is established to calculate the failure rate of each early stage of software respectively, and the fault relationship of each stage is analyzed to find out the key fault units. In particular, it explores and validates the relationship between the failure rate of code phase and the failure caused by faults in requirement analysis stage and design stage in a train control system, to alert the developer strictly accordance with the industry development standards for software requirements analysis, design and coding, so as to reduce potential faults in the early stage. There is evidence that the study plays a crucial role to optimize the cost of software development and avoid catastrophic consequences.

The primary innovations behind Software Defined Networks (SDN)are the decoupling of the control plane from the data plane and centralizing the network management through a specialized application running on the controller. Despite all its capabilities, the introduction of various architectural entities of SDN poses many security threats and potential target. Especially, Distributed Denial of Services (DDoS) is a rapidly growing attack that poses a tremendous threat to both control plane and forwarding plane of SDN. Asthe control layer is vulnerable to DDoS attack, the goal of this paper is to provide a defense system which is based on Learning Automata (LA) concepts. It is a self-operating mechanism that responds to a sequence of actions in a certain way to achieve a specific goal. The simulation results show that this scheme effectively reduces the TCP connection setup delay due to DDoS attack.