GR-23 Machine Learning Techniques for Malware Network Traffic Detection

Persistent malware variants are a constant threat to computing infrastructure across all regions and business sectors. Traditional detection systems focus primarily on signature-based analysis but this approach cannot adequately keep pace with the velocity and volume of new malware variants that are continuously deployed onto the internet. Most network traffic detection techniques are focused on analyzing raw packets and have not deterred the surge of persistent malware. Therefore, it is important to develop new research techniques that are focused on optimized metadata from malware network traffic to effectively identify an ever-increasing expanse of malicious software. Recent research efforts by Letteri et al. have produced a quality data set (MTA-KDD’19) that is utilized for this research project. New information in the area of malware network traffic detection is pursued through this research proposal. Specifically, I seek to find a defensible answer to the following question: Can machine learning techniques produce highly accurate classification models for malicious network traffic detection based on analysis of a statistically optimized data set? I believe that an affirmative answer to this research question provides a beneficial contribution to the academic community. The principal tool utilized to analyze the optimized data set for this research project is the Waikato Environment for Knowledge Analysis (WEKA). There are 64,550 instances and 33 features in the MTA-KDD’19 data set that are analyzed along with cross-validation and percentage split alternatives. The classification experiment performed by the authors of the MTA-KDD’19 data set is used as a baseline. The following machine learning classification models have been applied for this research investigation: Multilayer Perceptron, Decision Tree, Support Vector Machine, and K-Nearest Neighbors. The preliminary settings for these machine learning models include 10-fold cross-validation and 80% train 20% test data split. The Decision Tree classifier produced the best preliminary result with 100% accuracy when set to run an 80% training 20% test split and 99.9954% accuracy when set to run 10-fold cross-validation. This preliminary result has outperformed the results observed in the experiment presented by the authors of the MTA-KDD’19 data set. Other preliminary metrics illustrate that the selected models exhibit consistent and highly accurate performance. The multilayer perceptron classifier produced a preliminary result of 99.3649% accuracy when set to run an 80% training 20% test split and 99.3416% accuracy when set to run 10-fold cross-validation. The K-Nearest Neighbor classifier (K=1) produced a preliminary result of 98.9311% accuracy when set to run an 80% training 20% test split and 99.0024% accuracy when set to run 10-fold cross-validation. The Support Vector Machine classifier produced a preliminary result of 97.8081% accuracy when set to run an 80% training 20% test split and 97.7755% accuracy when set to run 10-fold cross-validation. The final stage of this research project will include implementation of additional machine learning methodologies. These methods will include feature selection techniques and ensemble learning models.Advisors(s): Dr. Seyedamin PouriyehTopic(s): SecurityCYBR 724

Usage habits of business information system in Hungary

The IT functions of the companies can be executed in different ways in-house solution, outsourcing, in sourcing, formation a spin-off company. Predominantly this function is provided within the company in Hungary. The larger a company is; it is more likely that a separate IT manager will be entrusted for the supervision of IT functions. Only a very small number of small-sized enterprises said that they paid special attention to formulating an IT strategy, while it was not considered important by microenterprises at all

Decision support system for the long-term city metabolism planning problem

A Decision Support System (DSS) tool for the assessment of intervention strategies (Alternatives) in an Urban Water System (UWS) with an integral simulation model called “WaterMet²” is presented. The DSS permits the user to identify one or more optimal Alternatives over a fixed long-term planning horizon using performance metrics mapped to the TRUST sustainability criteria (Alegre et al., 2012). The DSS exposes lists of in-built intervention options and system performance metrics for the user to compose new Alternatives. The quantitative metrics are calculated by the WaterMet² model and further qualitative or user-defined metrics may be specified by the user or by external tools feeding into the DSS. A Multi-Criteria Decision Analysis (MCDA) approach is employed within the DSS to compare the defined Alternatives and to rank them with respect to a pre-specified weighting scheme for different Scenarios. Two rich, interactive Graphical User Interfaces, one desktop and one web-based, are employed to assist with guiding the end user through the stages of defining the problem, evaluating and ranking Alternatives. This mechanism provides a useful tool for decision makers to compare different strategies for the planning of UWS with respect to multiple Scenarios. The efficacy of the DSS is demonstrated on a northern European case study inspired by a real-life urban water system for a mixture of quantitative and qualitative criteria. The results demonstrate how the DSS, integrated with an UWS modelling approach, can be used to assist planners in meeting their long-term, strategic level sustainability objectives

The integrated use of enterprise and system dynamics modelling techniques in support of business decisions

Enterprise modelling techniques support business process re-engineering by capturing existing processes and based on perceived outputs, support the design of future process models capable of meeting enterprise requirements. System dynamics modelling tools on the other hand are used extensively for policy analysis and modelling aspects of dynamics which impact on businesses. In this paper, the use of enterprise and system dynamics modelling techniques has been integrated to facilitate qualitative and quantitative reasoning about the structures and behaviours of processes and resource systems used by a Manufacturing Enterprise during the production of composite bearings. The case study testing reported has led to the specification of a new modelling methodology for analysing and managing dynamics and complexities in production systems. This methodology is based on a systematic transformation process, which synergises the use of a selection of public domain enterprise modelling, causal loop and continuous simulationmodelling techniques. The success of the modelling process defined relies on the creation of useful CIMOSA process models which are then converted to causal loops. The causal loop models are then structured and translated to equivalent dynamic simulation models using the proprietary continuous simulation modelling tool iThink

Results of Environmental Scanning Applied to the Design of a Deer Management Decision Support System (DSS) For The United States and California

Using freely available internet search tools for environmental scanning, information related to deer management was collected, categorized, and evaluated with the goal of providing public decision support. Key issues raised in the public debate discovered by the search are addressed with relevant information formatted as output for a decision support system – dashboard elements. A graph addresses contradictory reports about the current direction of the deer population; the trend since 2006 appears to be down. Another graph illustrates the approximate longterm population trend; the current U.S. white-tailed deer population is about the same as in 1500. A table summarizes profiles of state deer issues and strategies. Only eleven states are trying to reduce their deer population. A graph illustrates the rise and fall of the California population, the most dramatic population decline in the U.S. over the past 100 years. Hunting pressure and herd demographic management are found to be related to the decline, making these candidate variables for attention in the decision support system. This case application is designed to illustrate methods the author has learned in creating a variety of decision support applications for technology companies

Investigating and learning lessons from early experiences of implementing ePrescribing systems into NHS hospitals:a questionnaire study

Background: ePrescribing systems have significant potential to improve the safety and efficiency of healthcare, but they need to be carefully selected and implemented to maximise benefits. Implementations in English hospitals are in the early stages and there is a lack of standards guiding the procurement, functional specifications, and expected benefits. We sought to provide an updated overview of the current picture in relation to implementation of ePrescribing systems, explore existing strategies, and identify early lessons learned.Methods: a descriptive questionnaire-based study, which included closed and free text questions and involved both quantitative and qualitative analysis of the data generated.Results: we obtained responses from 85 of 108 NHS staff (78.7% response rate). At least 6% (n = 10) of the 168 English NHS Trusts have already implemented ePrescribing systems, 2% (n = 4) have no plans of implementing, and 34% (n = 55) are planning to implement with intended rapid implementation timelines driven by high expectations surrounding improved safety and efficiency of care. The majority are unclear as to which system to choose, but integration with existing systems and sophisticated decision support functionality are important decisive factors. Participants highlighted the need for increased guidance in relation to implementation strategy, system choice and standards, as well as the need for top-level management support to adequately resource the project. Although some early benefits were reported by hospitals that had already implemented, the hoped for benefits relating to improved efficiency and cost-savings remain elusive due to a lack of system maturity.Conclusions: whilst few have begun implementation, there is considerable interest in ePrescribing systems with ambitious timelines amongst those hospitals that are planning implementations. In order to ensure maximum chances of realising benefits, there is a need for increased guidance in relation to implementation strategy, system choice and standards, as well as increased financial resources to fund local activitie

DECISION SUPPORT SYSTEM FOR MANAGING AND DETERMINING INTERNATIONAL CLASS PROGRAM: GA AND AHP APPROACH

This study proposes a new method, a hybrid model for managing and determining the proposed International class based on many criteria of academic performance in university. The approach has been implemented as a decision support system allowing evaluation of various criteria and scenarios. The new model combines two different methods in decision support system: Analytical hierarchy Process (AHP) and Grey Analysis, the proposed model uses the AHP pairwise comparisons and the measure scale to generate the weights for the criteria which are much better and guarantee more fairly preference of criteria. Applying the system as decision-support facility for the management has resulted in significant acceleration of planning procedures and implementation, raised the overall effectiveness with respect to the underlying methodology and ultimately enabled more efficient academic administration