scholarly journals Sampling Based Approaches to Handle Imbalances in Network Traffic Dataset for Machine Learning Techniques

2013 ◽  
Author(s):  
Raman Singh ◽  
Harish Kumar ◽  
Singla R.K
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals A STUDY ON ADVANCED BOTNETS DETECTION IN VARIOUS COMPUTING SYSTEMS USING MACHINE LEARNING TECHNIQUES

2020 ◽  
pp. 167-173
Author(s):  
K. Vamshi Krishna
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Emerging Technologies ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Botnet Detection ◽  
Detection Techniques ◽  
Learning Techniques ◽  
Network Equipment ◽  
Internet Network

Due to the rapid growth and use of Emerging technologies such as Artificial Intelligence, Machine Learning and Internet of Things, Information industry became so popular, meanwhile these Emerging technologies have brought lot of impact on human lives and internet network equipment has increased. This increment of internet network equipment may bring some serious security issues. A botnet is a number of Internet-connected devices, each of which is running one or more bots.The main aim of botnet is to infect connected devices and use their resource for automated tasks and generally they remain hidden. Botnets can be used to perform Distributed Denial-of-Service (DDoS) attacks, steal data, send spam, and allow the attacker to access the device and its connection. In this paper we are going to address the advanced Botnet detection techniques using Machine Learning. Traditional botnet detection uses manual analysis and blacklist, and the efficiency is very low. Applying machine learning to batch automatic detection of botnets can greatly improve the efficiency of detection. Using machine learning to detect botnets, we need to collect network traffic and extract traffic characteristics, and then use X-Means, SVM algorithm to detect botnets. According to the difference of detection features, botnet detection based on machine learning technology is divided into network traffic analysis and correlation analysis-based detection technology. KEYWORDS: Botnet, Study, Security, Internet-network, Machine Learning, Techniques.

scholarly journals Network intrusion detection using machine learning

2021 ◽  
Author(s):  
Seyed Pedrum Jalali Mosallam
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Network Traffic ◽  
Machine Learning Techniques ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Learning Techniques ◽  
Network Intrusions ◽  
Regular Network

In this research we have studied the use of machine learning techniques in detecting network intrusions. Most research in the field has used the very outdated dataset (KDDCup99) which consists of a set handcrafted features. In our research we present models that work well on both the older dataset and on newer datasets such as ISCX2014 and ISCX2012. We also present methods for extracting features from these datasets. Another issue we found with most research in this field is that they do not study the effect of surges in regular network traffic and how that might affect the model. We put our model to test in 10x traffic and show its effectiveness under these conditions. We also study how semi-supervised models can be used in training NIDS models without directly showing them labeled data.

scholarly journals Network intrusion detection using machine learning

2021 ◽  
Author(s):  
Seyed Pedrum Jalali Mosallam
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Network Traffic ◽  
Machine Learning Techniques ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Learning Techniques ◽  
Network Intrusions ◽  
Regular Network

In this research we have studied the use of machine learning techniques in detecting network intrusions. Most research in the field has used the very outdated dataset (KDDCup99) which consists of a set handcrafted features. In our research we present models that work well on both the older dataset and on newer datasets such as ISCX2014 and ISCX2012. We also present methods for extracting features from these datasets. Another issue we found with most research in this field is that they do not study the effect of surges in regular network traffic and how that might affect the model. We put our model to test in 10x traffic and show its effectiveness under these conditions. We also study how semi-supervised models can be used in training NIDS models without directly showing them labeled data.

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