Towards Effective Wireless Intrusion Detection using AWID Dataset

In the field of network security, intrusion detection system plays a vital role in the procedure of applying machine learning (ML) techniques with the dataset. This study is an IDS related in machine, developed the literature by utilizing AWID dataset. There tends to be a need in balancing a dataset and its existing approaches from the analysis of its respective works. A taxonomy of balancing technique was introduced due to the lack of treatment of imbalance. This attempt has provided a proper structure defined on all levels and a hierarchical group was formed with the collected papers. This describes a comparative study on the proposed or treated aspects. The main aspect from the surveyed papers were found that: understanding of the existing taxonomies were not in detail and there were no treatment of imbalance for the utilized dataset. So, this study concludes a gathered information in these aspects. Regardless, there are factors or weakness have been seen in any adaptations of the intrusion detection system. In this context, there are few findings that are multifold with contributions. Thus, to best of our knowledge, the study provides an integration with the observation of threshold limit and feature drop selection method by random samples. Thus, the work contributes a better understanding towards imbalanced techniques from the literature surveyed. Hence, this research would benefit for the development of IDS using ML

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Evaluating practitioner cyber-security attack graph configuration preferences

Attack graphs and attack trees are a popular method of mathematically and visually rep- resenting the sequence of events that lead to a successful cyber-attack. Despite their popularity, there is no standardised attack graph or attack tree visual syntax configuration, and more than seventy self-nominated attack graph and twenty attack tree configurations have been described in the literature - each of which presents attributes such as preconditions and exploits in a different way. This research proposes a practitioner-preferred attack graph visual syntax configuration which can be used to effectively present cyber-attacks. Comprehensive data on participant ( n=212 ) preferences was obtained through a choice based conjoint design in which participants scored attack graph configuration based on their visual syntax preferences. Data was obtained from multiple participant groups which included lecturers, students and industry practitioners with cyber-security specific or general computer science backgrounds. The overall analysis recommends a winning representation with the following attributes. The flow of events is represented top-down as in a flow diagram - as opposed to a fault tree or attack tree where it is presented bottom-up, preconditions - the conditions required for a successful exploit, are represented as ellipses and exploits are represented as rectangles. These results were consistent across the multiple groups and across scenarios which differed according to their attack complexity. The research tested a number of bottom-up approaches - similar to that used in attack trees. The bottom-up designs received the lowest practitioner preference score indicating that attack trees - which also utilise the bottom-up method, are not a preferred design amongst practitioners - when presented with an alternative top-down design. Practitioner preferences are important for any method or framework to become accepted, and this is the first time that an attack modelling technique has been developed and tested for practitioner preferences

Cluster-based feedback control of turbulent post-stall separated flows

We propose a novel model-free self-learning cluster-based control strategy for general nonlinear feedback flow control technique, benchmarked for high-fidelity simulations of post-stall separated flows over an airfoil. The present approach partitions the flow trajectories (force measurements) into clusters, which correspond to characteristic coarse-grained phases in a low-dimensional feature space. A feedback control law is then sought for each cluster state through iterative evaluation and downhill simplex search to minimize power consumption in flight. Unsupervised clustering of the flow trajectories for in-situ learning and optimization of coarse-grained control laws are implemented in an automated manner as key enablers. Re-routing the flow trajectories, the optimized control laws shift the cluster populations to the aerodynamically favorable states. Utilizing limited number of sensor measurements for both clustering and optimization, these feedback laws were determined in only $O(10)$ iterations. The objective of the present work is not necessarily to suppress flow separation but to minimize the desired cost function to achieve enhanced aerodynamic performance. The present control approach is applied to the control of two and three-dimensional separated flows over a NACA 0012 airfoil with large-eddy simulations at an angle of attack of $9^\circ$, Reynolds number $Re = 23,000$ and free-stream Mach number $M_\infty = 0.3$. The optimized control laws effectively minimize the flight power consumption enabling the flows to reach a low-drag state. The present work aims to address the challenges associated with adaptive feedback control design for turbulent separated flows at moderate Reynolds number.Comment: 32 pages, 18 figure

Visualizing Contextual Information for Network Vulnerability Management

The threat of data breach rises every day, and many organizations lack the resources to patch every vulnerability they might have. Yet, these organizations do not prioritize what vulnerabilities to patch in an optimal way, in part due to a lack of context needed to make these decisions. Our team proposes the Vulnerability Visualization (VV) tool, a web visualization dashboard for increasing analyst prioritization capabilities through visualization of context for network scans. Evaluations demonstrate that the VV tool enhances the vulnerability management (VM) process through augmenting the discovery and prioritization of vulnerabilities. We show that adding context to the VM process through visualization allows people to make better decisions for vulnerability remediation