scholarly journals Network Intrusion Detection with a Hashing Based Apriori Algorithm Using Hadoop MapReduce

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 86 ◽  
Author(s):  
Azeez ◽  
Ayemobola ◽  
Misra ◽  
Maskeliūnas ◽  
Damaševičius
Keyword(s):  
Intrusion Detection ◽  
Effective Means ◽  
Network Intrusion Detection ◽  
Apriori Algorithm ◽  
Operational Mode ◽  
Mapreduce Framework ◽  
Hadoop Mapreduce ◽  
Network Intrusion ◽  
Internet Users ◽  
Network Intrusions

Ubiquitous nature of Internet services across the globe has undoubtedly expanded the strategies and operational mode being used by cybercriminals to perpetrate their unlawful activities through intrusion on various networks. Network intrusion has led to many global financial loses and privacy problems for Internet users across the globe. In order to safeguard the network and to prevent Internet users from being the regular victims of cyber-criminal activities, new solutions are needed. This research proposes solution for intrusion detection by using the improved hashing-based Apriori algorithm implemented on Hadoop MapReduce framework; capable of using association rules in mining algorithm for identifying and detecting network intrusions. We used the KDD dataset to evaluate the effectiveness and reliability of the solution. Our results obtained show that this approach provides a reliable and effective means of detecting network intrusion.

Building Recommender Systems for Network Intrusion Detection Using Intelligent Decision Technologies

2013 ◽  
pp. 49-62 ◽  
Author(s):  
Mrutyunjaya Panda ◽  
Manas Ranjan Patra ◽  
Sachidananda Dehuri
Keyword(s):  
Intrusion Detection ◽  
Recommender Systems ◽  
Machine Learning Algorithms ◽  
Network Intrusion Detection ◽  
Current Generation ◽  
Network Intrusion ◽  
Intelligent Decision ◽  
Wide Range ◽  
Network Intrusions ◽  
Decision Technologies

This chapter presents an overview of the field of recommender systems and describes the current generation of recommendation methods with their limitations and possible extensions that can improve the capabilities of the recommendations made suitable for a wide range of applications. In recent years, machine learning algorithms have been considered to be an important part of the recommendation process to take intelligent decisions. The chapter will explore the application of such techniques in the field of network intrusion detection in order to examine the vulnerabilities of different recommendation techniques. Finally, the authors outline some of the major issues in building secure recommendation systems in identifying possible network intrusions.

Dimension Reduction and its Effects on Clustering for Intrusion Detection

2011 ◽  
pp. 169-192
Author(s):  
Peyman Kabiri ◽  
Ali Ghorbani
Keyword(s):  
Intrusion Detection ◽  
Dimension Reduction ◽  
Clustering Algorithms ◽  
Feature Space ◽  
Information Value ◽  
Network Intrusion Detection ◽  
The Past ◽  
Network Intrusion ◽  
Network Intrusions ◽  
Network Feature

With recent advances in network based technology and the increased dependency of our every day life on this technology, assuring reliable operation of network based systems is very important. During recent years, a number of attacks on networks have dramatically increased and consequently interest in network intrusion detection has increased among the researchers. During the past few years, different approaches for collecting a dataset of network features, each with its own assumptions, have been proposed to detect network intrusions. Recently, many research works have been focused on better understanding of the network feature space so that they can come up with a better detection method. The curse of dimensionality is still a big obstacle in front of the researchers in network intrusion detection. In this chapter, DARPA’99 dataset is used for the study. Features in that dataset are analyzed with respect to their information value. Using the information value of the features, the number of dimensions in the data is reduced. Later on, using several clustering algorithms, effects of the dimension reduction on the dataset are studied and the results are reported.

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.

scholarly journals High-Performance Feature Selection Model for Network Intrusion Detection System

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1595-1597
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feature Selection Method ◽  
Reduction Process ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Network Intrusions

Network intrusions detection is a continuous vigilant task and to efficiently analyze the traffic in the corporate network to detect network intrusions. The efficiency of the Network Intrusion Detection System (NIDS) performance can be improved by adopting feature selection or reduction process to suit the present day high speed real time networks. This work is focused on identifying the key features of the audit dataset used to build an efficient light-weight NIDS. The NSL KDD dataset is used in this work titled Attribute Richness Based Feature Selection (ARFS) in order to analyze its performance.The obtained results are compared with the Correlation-based Feature Selection (CFS) and Information Gain (IG) feature selection methods. The proposed feature selection method produced better detection rate comparatively.

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

2020 ◽  
Vol 38 (1B) ◽  
pp. 6-14
Author(s):  
ٍٍSarah M. Shareef ◽  
Soukaena H. Hashim
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Multinomial Logistic Regression ◽  
Network Intrusion Detection ◽  
Limiting Factor ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Normal Behavior

Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate.

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