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 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.

Machine learning methods for cyber security intrusion detection: Datasets and comparative study

2021 ◽  
Vol 188 ◽  
pp. 107840
Author(s):  
Ilhan Firat Kilincer ◽  
Fatih Ertam ◽  
Abdulkadir Sengur
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Cyber Security ◽  
Learning Methods ◽  
Machine Learning Methods

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

Inter-dataset generalization strength of supervised machine learning methods for intrusion detection

2020 ◽  
Vol 54 ◽  
pp. 102564
Author(s):  
Laurens D’hooge ◽  
Tim Wauters ◽  
Bruno Volckaert ◽  
Filip De Turck
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

Comparative Study of Machine Learning Methods for In-Vehicle Intrusion Detection

2019 ◽  
pp. 85-101 ◽  
Author(s):  
Ivo Berger ◽  
Roland Rieke ◽  
Maxim Kolomeets ◽  
Andrey Chechulin ◽  
Igor Kotenko
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Learning Methods ◽  
Machine Learning Methods

A new hybrid approach for intrusion detection using machine learning methods

2019 ◽  
Vol 49 (7) ◽  
pp. 2735-2761 ◽  
Author(s):  
Ünal Çavuşoğlu
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Hybrid Approach ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Implementation of K-Means Clustering for Intrusion Detection

2019 ◽  
pp. 1232-1241
Author(s):  
Saba Karim ◽  
Mr Rousanuzzaman ◽  
Patel Ayaz Yunus ◽  
Patha Hamid Khan ◽  
Mohammad Asif
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
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.

scholarly journals A post-method condition analysis of using ensemble machine learning for cancer prognosis and diagnosis: a systematic review

2019 ◽  
Author(s):  
Leila Mirsadeghi ◽  
Ali Mohammad Banaei-Moghaddam ◽  
Seyed Reza Beh-Afarin ◽  
Reza Haji Hosseini ◽  
Kaveh Kavousi
Keyword(s):  
Machine Learning ◽  
Empirical Studies ◽  
Ensemble Methods ◽  
Feature Space ◽  
Ensemble Classifier ◽  
Cancer Prognosis ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Different Types

Abstract Background: Ensemble methods are supervised learning approaches that integrate different types of data or multiple individual classifiers. It has been shown that these methods can improve professional performance.Methods: This study is an attempt to provide an in-depth review on 45 most relevant articles and aims to introduce 42 ensemble classifier (EC) machine learning methods used for the detection of 18 different types of cancer. Compared to other types of cancer, breast cancer, and the 22 ensemble methods introduced for its identification, is extensively investigated. The purpose of this study is to identify, map, and analyze the current academic discourse on EC machine learning methods in order to: 1. identify overarching themes emerging from empirical studies as regards EC methods, 2. determine their input data and decision-making strategies, and 3. evaluate relevant statistical procedures.Results: By comparing various approaches, we can introduce Relevance Vector Machine (RVM)-based ensemble learning method that can provide optimal solutions for problems such as curse the dimensionality and high-dimensionality of feature space without missing data values.Conclusions: To obtain robust performance and achieve better results, it is tactfully suggested to use multi-omics data integration, which has demonstrated to identify cancers and their subtypes more efficiently.

Sign in / Sign up

Export Citation Format

Share Document