INTRUSION DETECTION USING BIG DATA AND DEP MIND NETWORK

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INTRUSION DETECTION USING BIG DATA AND DEP MIND NETWORK

CHAPTER ONE

INTRODUCTION

1.1 Background of the study

Providing protection and privacy of big data is one of the most important challenges facing developers of security management systems, especially with the large expansion of the use of Internet networks and the rapid growth of the volume of data generated from several sources. This expansion and growth gave more space to hackers to launch their malicious attacks and use development techniques and tools for intrusion. On the other hand, researchers and developers of intrusion detection systems seek to increase the efficiency of malicious attack detection and the prediction of early attacks. Intrusion detection systems are one of the most important systems used in cyber security. Intrusion refers to attempts to compromise the confidentiality, integrity, availability of security mechanisms of computer or network resources or to bypass them. Intrusion detection systems (IDSs) are the hardware or software that monitors and analyzes data flowing through computers and networks to detect security breaches that threaten confidentiality, integrity or availability of a system’s resources [9]. Intrusion detection systems use two basic methods to analyze events and detect attacks: misuse detection and anomaly detection. Misuse detection (or signature-based detection) is an analysis of system activities to search and detect patterns of attacks identical to or similar to previously known attack patterns and stored in a database intrusion detection system. An anomaly detection is the detection of unusual patterns of behavior in network traffic and it relies on building models that represent the normal behavior of users, hosts or the network where patterns of behavior that deviate from these models are detected and often represent abnormal behavior. The anomaly detection approach is based on machine learning, artificial neural networks, and deep learning techniques that have been widely used recently in the development of intrusion detection systems for mining and extracting knowledge through the training and testing of datasets [5]. Recently big data is being used in intrusion detection. Big data is data that is difficult to store, manage or manipulate using traditional techniques. The characteristics of big data include volume, variety and velocity [35] and they represent a major challenge for intrusion detection systems [28]. Volume refers to the quantity of data where data are generated from several different sources having exploded very dramatically over recent years. This requires monitoring and analyzing network traffic to integrate with the management and processing of big data. The large volume of data is often associated with another challenge, which is variety, meaning different data sources and therefore various data types including structured, semi-structured and unstructured data. Moreover, variety refers to heterogeneous data. Large IT infrastructures can generate huge quantities of data from many resources, such as application servers, networks, and workstations. Analyzing and monitoring heterogeneous data is a complex challenge and exacerbates the problems facing intrusion detection systems [36]. The huge change in the size and variety of data has also led to a change in the speed of data generation and streaming, referred to as velocity.

1.2 Statement of the problem

Big data management and computing technologies have been created and developed over the last few years, including Hadoop [26], Apache Spark [33], Hive [31] and NoSQL [14]. Big data techniques have many advantages, such as speed in receiving, storing and processing data of various types. This work proposes an assessment of integration between the management and computation of big data and deep learning techniques using Apache Spark and the Keras deep learning library. A deep neural network, random forest and gradient boosted tree were used for classificaton, and kmeans clustering technique is used feature selection by calculating the degree of homogeneity. These suggested approaches are applied on two recent datasets, namely CICIDS2018 and UNSW-NB15, both of which contain a set of common and updated attacks.

1.3 Objectives of the study

1. To understand the impact big data and deep mind network on intrusion

2. To understand the relationship between big data and deep mind and the detection of intrusion

1.4 Research Questions

1. What is the impact big data and deep mind network on intrusion

2. What is the relationship between big data and deep mind and the detection of intrusion

1.5 Research Hypothesis

H0: There is no relationship between big data and deep mind and the detection of intrusion

H1: There is a relationship between big data and deep mind and the detection of intrusion

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