No abstract

This article was originally presented as a keynote address at the Ninth International Workshop on Structural Health Monitoring with the intent of provoking discussions relating to the transformation of aircraft maintenance practices by exploiting opportunities and benefits offered by Information Age technology and techniques. The US Air Force currently manages its aircraft using a schedule-based maintenance philosophy. This schedule-based approach works well for ensuring aircraft integrity; however, it is very costly, labor-intensive, and reduces aircraft availability. Structural health monitoring systems have the potential to analyze near-real-time and historical weapon systems data to provide a predictive maintenance capability. However, much aerospace structural health monitoring research has focused on in situ structural inspection techniques instead of structural monitoring. Structural inspections typically entail examining key locations of an airframe for material degradation or flaws. These examinations usually occur at predefined time intervals. As such, each inspection is considered an independent evaluation. Conversely, structural monitoring involves continuous condition surveillance of an airframe over an extended period of time. Structural monitoring uses past conditions and expected future conditions for producing a comprehensive understanding of the current health state. A new architecture, Cognitive Architecture for State Exploitation, is introduced as a monitoring technique that combines diagnostic or state (i.e. health) assessments, prognostic assessments, and mission objectives into a common framework to enable goal-based decision making. Results from a laboratory experiment are utilized to demonstrate the application of Cognitive Architecture for State Exploitation and to illustrate the potential to improve effectiveness and efficiency metrics compared to those of the current US Air Force maintenance procedures.

No abstract

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, Internet of Things (IoT) has earned a wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack tending to be treated as a normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide a comprehensive development of a new intelligent and autonomous deep learning-based detection and classification system for cyber-attacks in IoT communication networks leveraging the power of convolutional neural networks, abbreviated as (IoT-IDCS-CNN). The proposed IoT-IDCS-CNN makes use of the high-performance computing employing the robust CUDA based Nvidia GPUs and the parallel processing employing the high-speed I9-Cores based Intel CPUs. In particular, the proposed system is composed of three subsystems: Feature Engineering subsystem, Feature Learning subsystem and Traffic classification subsystem. All subsystems are developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the NSL-KDD dataset which includes all the key attacks in the IoT computing. The simulation results demonstrated more than 99.3% and 98.2% of cyber-attacks’ classification accuracy for the binary-class classifier (normal vs anomaly) and the multi-class classifier (five categories) respectively. The proposed system was validated using k-fold cross validation method and was evaluated using the confusion matrix parameters (i.e., TN, TP, FN, FP) along with other classification performance metrics including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning based IDCS systems in the same area of study.

No abstract