scholarly journals Data Mining and Machine Learning Techniques for Cyber Security Intrusion Detection

2020 ◽  
Vol 9 (3) ◽  
pp. 4084-4090
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Cyber Security ◽  
Data Stream ◽  
Machine Learning Techniques ◽  
Complex Nature ◽  
Use Of Data ◽  
Learning Techniques ◽  
Examination Paper ◽  
Discovery Rates

An interference discovery framework is customizing that screens a singular or an arrangement of PCs for toxic activities that are away for taking or blue-penciling information or spoiling framework shows. The most methodology used as a piece of the present interference recognition framework is not prepared to deal with the dynamic and complex nature of computerized attacks on PC frameworks. In spite of the way that compelling adaptable methodologies like various frameworks of AI can realize higher discovery rates, cut down bogus alert rates and reasonable estimation and correspondence cost. The use of data mining can realize ceaseless model mining, request, gathering and littler than ordinary data stream. This examination paper portrays a connected with composing audit of AI and data delving procedures for advanced examination in the assistance of interference discovery. In perspective on the number of references or the congruity of a rising methodology, papers addressing each procedure were recognized, examined, and compacted. Since data is so fundamental in AI and data mining draws near, some striking advanced educational records used as a piece of AI and data burrowing are depicted for computerized security is shown, and a couple of recommendations on when to use a given system are given.

scholarly journals Dr. Phish: Phishing Website Detector

2021 ◽  
Vol 297 ◽  
pp. 01032
Author(s):  
Harish Kumar ◽  
Anshal Prasad ◽  
Ninad Rane ◽  
Nilay Tamane ◽  
Anjali Yeole
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Cyber Crime ◽  
Data Mining Algorithms ◽  
Learning Techniques ◽  
Mining Algorithms ◽  
Host Properties ◽  
New Strategies

Phishing is a common attack on credulous people by making them disclose their unique information. It is a type of cyber-crime where false sites allure exploited people to give delicate data. This paper deals with methods for detecting phishing websites by analyzing various features of URLs by Machine learning techniques. This experimentation discusses the methods used for detection of phishing websites based on lexical features, host properties and page importance properties. We consider various data mining algorithms for evaluation of the features in order to get a better understanding of the structure of URLs that spread phishing. To protect end users from visiting these sites, we can try to identify the phishing URLs by analyzing their lexical and host-based features.A particular challenge in this domain is that criminals are constantly making new strategies to counter our defense measures. To succeed in this contest, we need Machine Learning algorithms that continually adapt to new examples and features of phishing URLs.

scholarly journals Research on threat detection in cyber security based on machine learning

2021 ◽  
Vol 2113 (1) ◽  
pp. 012074
Author(s):  
Qiwei Ke
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Information Privacy ◽  
Machine Learning Techniques ◽  
Threat Detection ◽  
Vulnerability Detection ◽  
New Information ◽  
Learning Techniques ◽  
Potential Problems ◽  
Insight Into

Abstract The volume of the data has been rocketed since the new information era arrives. How to protect information privacy and detect the threat whenever the intrusion happens has become a hot topic. In this essay, we are going to look into the latest machine learning techniques (including deep learning) which are applicable in intrusion detection, malware detection, and vulnerability detection. And the comparison between the traditional methods and novel methods will be demonstrated in detail. Specially, we would examine the whole experiment process of representative examples from recent research projects to give a better insight into how the models function and cooperate. In addition, some potential problems and improvements would be illustrated at the end of each section.

Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security

2021 ◽  
pp. 1085-1099
Author(s):  
S. Abijah Roseline ◽  
S. Geetha
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Security Threat ◽  
Signature Detection ◽  
Learning Techniques ◽  
Feature Based

Malware is the most serious security threat, which possibly targets billions of devices like personal computers, smartphones, etc. across the world. Malware classification and detection is a challenging task due to the targeted, zero-day, and stealthy nature of advanced and new malwares. The traditional signature detection methods like antivirus software were effective for detecting known malwares. At present, there are various solutions for detection of such unknown malwares employing feature-based machine learning algorithms. Machine learning techniques detect known malwares effectively but are not optimal and show a low accuracy rate for unknown malwares. This chapter explores a novel deep learning model called deep dilated residual network model for malware image classification. The proposed model showed a higher accuracy of 98.50% and 99.14% on Kaggle Malimg and BIG 2015 datasets, respectively. The new malwares can be handled in real-time with minimal human interaction using the proposed deep residual model.

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