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In the effort of making a more realistic simulation environment for VANET systems, we employ real traffic data to create a mobility scenario for ns-3. We then test how our simulated system performs when running different variations for network protocols. One version is optimized for general use, while the other is optimized for a VANET environment. We then evaluate how effective the optimizations for VANET are as well as the accuracy of our test system. We found that 802.11p performed better than 802.11ac in our simulated system in terms of both packet delivery and computational efficiency, and that our system seemed to be a reasonable model of the real world.

Wineinformatics is a novel data science application domain to understand more about wine via wine related datasets, including physicochemical laboratory data and wine reviews. Many interesting works has been done in this novel domain, including predict the wines' score based on the wine expert reviews (in human language format) and wine region estimation. In this paper, Granule computing was applied to Wineinformatics. The information granules are evaluated and found out that the results produced within the information granules surpass the predictive models build on the whole dataset. It suggests that the information granule do have the power to pick up the correlation between wine attributes and wine scores to produce higher quality results. Important attributes are also reported in this paper. It is the first time that the granule computing concept being applied in any wine related experiment or dataset. This paper also enables many possible future works in Wineinformatics.

The use and deployment of Industrial Control Systems (ICS) have become standard across many industries, though the security of these important systems have not kept pace with their current Internet focused deployment technologies. This technology gap has exposed an exploitable vector for would be attackers as ICS protocols do not have security mechanisms in place to handle Internet connectivity. This paper focuses on a critical component of a Network Telemetry based Intrusion Detection system (IDS) that can help eliminate this exploitable vector. This component is a method for extracting features using session duration based instantiation. After integrating this feature extraction method into the telemetry based IDS, it is able to achieve 99.98% accuracy when distinguishing between an engineer and an attacker on the same network.

This paper presents an overview of the privacy issues that are expected to emerge as a result of the Internet of Things (IoT). IoT expands digital world to include different things in our physical world. This ambitious aspiration is becoming true with the utilization of already existing technologies that enable things to be identified and to communicate with each other. Unfortunately, the ubiquitous collection and dissemination of data that characterize IoT come at the expense of data privacy. We have witnessed many privacy breaches during the last decade, more vicious breaches will find its way to the huge amounts of qualitative data that are collected in IoT with more expected catastrophic consequences to individuals and organizations. Evaluating some papers, the lessons to be learnt and some possible directions for future research were highlighted.

As the use of mobile devices continues to increase, so does the need for sophisticated malware detection algorithms. The preliminary research presented in this paper focuses on examining permission requests made by Android apps as a means for detecting malware. By using a machine learning algorithm, we are able to differentiate between benign and malicious apps. The model presented achieved a classification accuracy between 75% and 80% for our dataset and the best combination of parameters. Future work will seek to improve the model by expanding the training dataset, taking more features into account, and exploring other machine learning algorithms.

One of the biggest efforts of securing cyberspace is the ability to secure the critical infrastructure power grid. This meshed network of geographically distributed control systems has recently been interlaced with network capable devices. This article manipulates power system state parameters to identify stealthy cyber-events based on the feature identification method principal component analysis. Principle component analysis is used to examine power system instances contained in an in-control set. To develop a set of observable power system instances the Newton-Rhapson method is used to solve the power flow equations. Cyber-events were created by changing the in-control instances of the power system parameters over a range of ±40%. If a changed instance, simulated malicious instance, was mapped via principle component analysis within close range of the average Hotelling's T2 value that instance was deemed stealthy. Results indicate certain features have a higher likelihood of remaining in the stealthy regions.

Research efforts to develop malicious application detection algorithms have been a priority ever since the discovery of the first "viruses". Various methods are used to search and identify these malicious applications. One such method, n-gram analysis, can be implemented to extract features from binary files. These features are then be used by machine learning algorithms to classify them as malicious or benign. However, the resulting high dimensionality of the features makes accurate detection in some cases impossible. This is known as "the curse of dimensionality". To counteract this effect, a feature reduction technique known as randomized projection was implemented. Through this reduction, not only are classification times decreased but also an increase in true positive and decreases false positive rates are observed. By varying the n-gram size and target feature size it is possible to fine-tune the accuracy of machine learning algorithms to reach an average accuracy of 99%.

Visualization is a promising approach in improving the teaching of algorithms because it can give a pictorial representation of the effect of every step of an algorithm. However, traditional implementations of visualizations require much additional coding to support the infrastructure necessary to step through an algorithm. In this work, we embark on a different path for implementing visualizations, PCIL (PseudoCode Interpreted Language). We believe that PCIL distinguishes itself from other approaches to algorithm visualization by incorporating visualization into its specification. Each language primitive, such as a variable, natively supports a graphical representation. The PCIL interpreter automatically derives visualizations from algorithm implementations. In addition, PCIL includes constructs to facilitate pedagogically effective visualizations, such as the ability to specify custom inputs to algorithms and the ability to ask the student to predict algorithmic behavior. Experimental results indicate that not only do students enjoy using PCIL, they also perform much better on tests after using it compared to students who simply use traditional study aides. Furthermore, the students who use the application for longer amounts of time derive more benefit from the tool than those who only use it for a short time.

This work describes a web browser-based remote visualization capability for large datasets. We discuss recent enhancements including access to databases and remote resources, as well as the addition of two image compression algorithms and their effect on performance. Results indicate that the existing sequential unified- channel image run transmission (SQUIRT) image compression algorithm performs best for large bandwidth situations while the new binary set splitting with k-d trees (BISK) algorithm works better than the previous JPEG compression scheme. The results from a study on encryption effects on the data stream show that encryption does not add a significant amount of overhead.