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MN623 Cyber Security and Analytics

Referencing Styles : IEEE | Pages : 4

Introduction

Over the years, firms have had to deal with cyber threats such as ransomwares. However, as of 2017 new threats begun to emerge and did so in a fast rate which saw the introduction of threats such as Cryptojacking which was motivated with the popularity of the cryptocurrency where business computers are hacked to be used for mining cryptocurrency [1]. Other threats include: Internet of Things (IoT) device threats, Geopolitical risks, Cross-site scripting, and Mobile malware. With the increasing number of threats to the cyber protection of organizations, the issue of how to protect company resources such as data, finances, intrusion, etcetera remains of concern to business executives. An article written in 2018 suggests security analytics as a solution to cyber threats. “…Security Analytics is an approach to cybersecurity focused on the analysis of data to produce proactive security measures.” [2]. The whole point of security analytics lies in its ability to enable the transition from protection to detection and provide a unified view of the enterprise which offers the firm a means through which to detect external threats and gather intelligence [2].

Objective

In this paper, we will conduct intrusion detection using WEKA data analytic technique to examine the intelligent security solutions based on data analytics and report on our findings.

Data analytics tools and techniques

Data analytic tools

We will be using the WEKA tool on windows 10 which is basically a standard Java tool used in performing both machine learning experiments as well as embedding trained models in Java applications. Weka therefore is the best tool for us based on our research objectives which intends to perform intrusion detection and given the wide usage of java applications in technological products including operating systems [3]. 

Data analytics techniques

Our main focus is to compare different intrusion detection methods. Our objective is to classify an activity as either normal or an anomaly, which makes it a binary problem that can be tackled using classification techniques or prediction. As such, we will use Random Forest and Logistic regression machine learning algorithms.

Random Forest

One of the best algorithms in classical machine learning is the random forest model which according to the words of Niklas Donges is, “a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time.” [4]. Classified under supervised learning algorithms, the Random Forest follows a simple application which can be summarized as building multiple decision trees then merging them to obtain more accurate and stable prediction results [4].

Perhaps the biggest merit of this algorithm is the fact that it can be used for both prediction and regression problems making it suitable when the objective is to determine how different predictor attributes affect a response attribute and how different attributes are grouped together.

Since decision trees are developed using the greedy algorithm which selects an optimum split over each split process, the Random forest is an improvement of bagged decision trees and disrupts the greedy splitting algorithm. When applying the model to our data, our main focus will lie on the number of attributes we use for each split point.

Logistic Regression

Logistic regression is a machine learning classification algorithm adopted when the problem involves the need to “…assign observations to a discrete set of classes” [5] in which the outcome is either binary or dichotomous. The objective of a logistic regression model is to explain the relationship between the response (outcome) and explanatory variables.

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