The security of safety-critical IoT systems, such as the Internet of Vehicles (IoV), has a great interest, focusing on using Intrusion Detection Systems (IDS) to recognise cyber-attacks in IoT networks. Deep learning methods are commonly used for the anomaly detection engines of many IDSs because of their ability to learn from heterogeneous data. However, while this type of machine learning model produces high false-positive rates and the reasons behind its predictions are not easily understood, even by experts. The ability to understand or comprehend the reasoning behind the decision of an IDS to block a particular packet helps cybersecurity experts validate the system’s effectiveness and develop more cyber-resilient systems. This paper proposes an explainable deep learning-based intrusion detection framework that helps improve the transparency and resiliency of DL-based IDS in IoT networks. The framework employs a SHapley Additive exPlanations (SHAP) mechanism to interpret decisions made by deep learning-based IDS to experts who rely on the decisions to ensure IoT networks’ security and design more cyber-resilient systems. The proposed framework was validated using the ToN_IoT dataset and compared with other compelling techniques. The experimental results have revealed the high performance of the proposed framework with a 99.15% accuracy and a 98.83% F1 score, illustrating its capability to protect IoV networks against sophisticated cyber-attacks.

Raw sensor data containing high-fidelity information is highly desirable for valuable post-processing. We developed a machine learning model that performs deep regression to infer rotorcraft component vibration spectra from a few flight conditional indicators (CI). The model consists of a deep neural network of fully connected layers (DNN) that performs high-dimensional and non-linear multivariate regression to reconstruct raw accelerometer data. The network architecture hyperparameters were optimized using an evolutionary genetic algorithm (GA) that was more effective than random and manual search methods. The best GA design was further tuned to achieve spectrum reconstruction accuracies above 95% on validation datasets. An automated model generator workflow was developed to train and evaluate thousands of DNN designs using parallel asynchronous execution on a Cray XC50, which were monitored and studied. Finally, as a verification step of the DNN inference model operation and performance, a detailed sensitivity analysis was performed using a modified Sobol sampling technique to understand response behavior and limitations. The sensitivity analysis method utilized Dask-distributed across multiple nodes on our HPC to evaluate millions of generated samples in parallel.

Deriving user interface requirements is a key step in user interface generation and maintenance. For single purpose numeric routines, user interface requirements are relatively simple to derive. However, general numeric packages, which are solvers for entire classes of problems, are densely interleaved with strands shared and mixed among user options. This complexity forms a significant barrier to the derivation of user interface requirements and therefore to user interface generation and maintainance. Our methodology uses a graph representation to find potential user decision points implied by the control structure of the code. This graph is then iteratively refined to form a decision point diagram, a state machine representation of all possible user traversals through a user interface for the underlying code.