Parallel processing using big data and machine learning techniques for intrusion detection

Boukhalfa, Alaeddine; Hmina, Nabil; Chaoni, Habiba

doi:10.11591/ijai.v9.i3.pp553-560

Cited by 9 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed model is effective in DDSA detection, especially in software-defined networks and cloud computing environments. A SVM-based DDSA detection model was proposed in [21]. The proposed model is designed to be applied in the cloud computing environments.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Bernoulli Naive Bayes model for detection of distributed denial of service attacks

Salau

Assegie²,

Akindadelo³

et al. 2023

Bulletin EEI

View full text Add to dashboard Cite

Distributed denial of service is a form of cyber-attack that involves sending several network traffic to a target system such as DHCP, domain name server (DNS), and HTTP server. The attack aims to exhaust computing resources such as memory and the processor of a target system by blocking the legitimate users from getting access to the service provided by the server. Network intrusion prevention ensures the security of a network and protects the server from such attacks. Thus, this paper presents a predicitive model that identifies distributed denial of service attacks (DDSA) using Bernoulli-Naive Bayes. The developed model is evaluated on the publicly available Kaggle dataset. The method is tested with a confusion matrix, receiver operating characteristics (ROC) curve, and accuracy to measure its performance. The experimental results show an 85.99% accuracy in detecting DDSA with the proposed method. Hence, Bernoulli-Naive Bayes-based method was found to be effective and significant for the protection of network servers from malicious attacks.

show abstract

Section: Related Workmentioning

confidence: 99%

Evaluation of Bernoulli Naive Bayes model for detection of distributed denial of service attacks

Salau

Assegie²,

Akindadelo³

et al. 2023

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…Throughout the years several types of research have been carried out in the field of object detection and many different algorithms have been discovered namely conventional neural network (CNN), regions with conventional neural network (R-CNN), single shot detector (SSD), and transfer learning [1]. Object detection is used in many real-life situations like counting people in crowds [2] and intrusion detection in private and highly secured areas [3]. The primary target of the object detection models is to locate specific targets in an input image and further the movement of the target in the successive frames can be tracked.…”

Section: Introductionmentioning

confidence: 99%

Various object detection algorithms and their comparison

Mishra

Rout

Mishra

et al. 2022

IJEECS

View full text Add to dashboard Cite

This paper presents a detailed and comparative analysis of various object detection algorithms. The challenge of object detection is taken care of while studying various algorithms. Throughout the year various methods have been discovered in this field, each having its advantages and drawbacks. This paper aims at providing the systematic study of all the popular algorithms including the conventional ones. Although many methods and techniques come up each year and each of them having superiority other the previous models but at the same time even complexity increases. In this paper, some famous and basic methods of object detection and tracking are discussed. Using these developed techniques good results can be obtained and also the comparison of the efficiency of all the models can be done. Real-time applications and the outcomes are also discussed.

show abstract

“…It is the primary driving force behind the existing business. Because large data analytics is a computationally intensive operation, the user experience during large data analytics is influenced by the setup of different software and devices [57]- [66].…”

Section: Introductionmentioning

confidence: 99%

“…Lunga et al [85] proposed a framework that makes use of Spark's distributed computing capabilities as well as deep learning architecture for multiple layers perceptron (MLP) using cascade learning to train multiple layers perceptrons is proposed. A framework for in-depth training learning models with Apache Spark has been created and developed in [47], [48], [50], [51], [57], [69], [86]- [92]. This framework shortens the training time by taking advantage of the advantages of both data and parity modeling at the same time.…”

Section: Introductionmentioning

confidence: 99%

Large scale data analysis using MLlib

et al. 2021

View full text Add to dashboard Cite

Recent advancements in the internet, social media, and internet of things (IoT) devices have significantly increased the amount of data generated in a variety of formats. The data must be converted into formats that is easily handled by the data analysis techniques. It is mathematically and physically expensive to apply machine learning algorithms to big and complicated data sets. It is a resource-intensive process that necessitates a huge amount of logical and physical resources. Machine learning is a sophisticated data analytics technology that has gained in importance as a result of the massive amount of data generated daily that needs to be examined. Apache Spark machine learning library (MLlib) is one of the big data analysis platforms that provides a variety of outstanding functions for various machine learning tasks, spanning from classification to regression and dimension reduction. From a computational standpoint, this research investigated Apache Spark MLlib 2.0 as an open source, autonomous, scalable, and distributed learning library. Several real-world machine learning experiments are carried out in order to evaluate the properties of the platform on a qualitative and quantitative level. Some of the fundamental concepts and approaches for developing a scalable data model in a distributed environment are also discussed.

show abstract

Parallel processing using big data and machine learning techniques for intrusion detection

Cited by 9 publications

References 16 publications

Evaluation of Bernoulli Naive Bayes model for detection of distributed denial of service attacks

Evaluation of Bernoulli Naive Bayes model for detection of distributed denial of service attacks

Various object detection algorithms and their comparison

Large scale data analysis using MLlib

Contact Info

Product

Resources

About