scholarly journals A Survey : Data Mining and Machine Learning Methods for Cyber Security

2021 ◽  
pp. 24-34
Author(s):  
Ashish Prajapati ◽  
Shital Gupta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Intrusion Detection ◽  
Survey Data ◽  
Cyber Security ◽  
Literature Survey ◽  
Learning Methods ◽  
Survey Paper ◽  
Machine Learning Methods ◽  
Number Of Citations

This survey paper describes the literature survey for cyber analytics in support of intrusion detection of machine learnings (ML) and data mining (DM) methods. Short ML/DM method tutorial details will be given. Documents representing each method were categorized, read and summarized based on the number of citations and significance of an evolving method. Since data is so important.

scholarly journals Insider threats and Insider Intrusion Detection

2019 ◽  
Vol 8 (2S5) ◽  
pp. 158-166
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection Methods ◽  
Learning Methods ◽  
Insider Threats ◽  
Survey Paper ◽  
Machine Learning Methods ◽  
Different Types ◽  
Critical Problems

this survey paper narrates insider threats and their detection types and methods. Insider threats are emerging nowadays, it is important to identify these threats as they are generating critical problems to the system. This paper pays particular attention to the categories of threats and different types of detection methods. Based on different strategies, statistical and machine learning methods for detecting these threats, are identified and summarized here.

scholarly journals Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey

2019 ◽  
Vol 9 (20) ◽  
pp. 4396 ◽  
Author(s):  
Hongyu Liu ◽  
Bo Lang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Detection System ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Learning Methods ◽  
Machine Learning Methods

Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. Deep learning is a branch of machine learning, whose performance is remarkable and has become a research hotspot. This survey proposes a taxonomy of IDS that takes data objects as the main dimension to classify and summarize machine learning-based and deep learning-based IDS literature. We believe that this type of taxonomy framework is fit for cyber security researchers. The survey first clarifies the concept and taxonomy of IDSs. Then, the machine learning algorithms frequently used in IDSs, metrics, and benchmark datasets are introduced. Next, combined with the representative literature, we take the proposed taxonomic system as a baseline and explain how to solve key IDS issues with machine learning and deep learning techniques. Finally, challenges and future developments are discussed by reviewing recent representative studies.

scholarly journals Data Mining and Machine Learning Methods Applied to A Numerical Clinching Model

2018 ◽  
Vol 117 (3) ◽  
pp. 387-423
Author(s):  
Marco Götz ◽  
Ferenc Leichsenring ◽  
Thomas Kropp ◽  
Peter Müller ◽  
Tobias Falk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Implementation of ML Algorithm’s for Cyber Security

2021 ◽  
pp. 54-61
Author(s):  
Mohammad Asif ◽  
Prof. E. M. Chirchi
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
The Internet ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
System Movement

Machine learning is embraced in an extensive variety of areas where it demonstrates its predominance over customary lead based calculations. These strategies are being coordinated in digital recognition frameworks with the objective of supporting or notwithstanding supplanting the principal level of security experts although the total mechanization of identification and examination is a luring objective, the adequacy of machine learning in digital security must be assessed with the due steadiness. With the improvement of the Internet, digital assaults are changing quickly and the digital security circumstance isn't hopeful. Since information are so critical in ML/DL strategies, we portray a portion of the normally utilized system datasets utilized in ML/DL, examine the difficulties of utilizing ML/DL for digital security and give recommendations to look into bearings. Malware has developed over the previous decades including novel engendering vectors, strong versatility methods and different and progressively propelled assault procedures. The most recent manifestation of malware is the infamous bot malware that furnish the aggressor with the capacity to remotely control traded off machines therefore making them a piece of systems of bargained machines otherwise called botnets. Bot malware depend on the Internet for proliferation, speaking with the remote assailant and executing assorted noxious exercises. As system movement, action is one of the principle characteristics of malware and botnet task, activity investigation is frequently observed as one of the key methods for recognizing traded off machines inside the system. We present an examination, routed to security experts, of machine learning methods connected to the recognition of interruption, malware, and spam.

A Review of Intrusion Detection Systems: Datasets and machine learning methods

2021 ◽  
Author(s):  
Aouatif ARQANE ◽  
Omar Boutkhoum ◽  
Hicham Boukhriss ◽  
Abdelmajid El Moutaouakkil
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection Systems ◽  
Learning Methods ◽  
Detection Systems ◽  
Machine Learning Methods

Towards Behaviour Recognition with Unlabelled Sensor Data

2013 ◽  
pp. 86-110
Author(s):  
Sook-Ling Chua ◽  
Stephen Marsland ◽  
Hans W. Guesgen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Inverse Problem ◽  
Sensor Data ◽  
Training Set ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data ◽  
Symbolic Approach ◽  
Behaviour Recognition

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

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