An Improved Distributed Intrusion Detection Architecture for Cloud Computing

2014 ◽  
pp. 105-116 ◽  
Author(s):  
Hamid Reza Ghorbani ◽  
Mahmoud Reza Hashemi
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Distributed Intrusion Detection

A Distributed Intrusion Detection Scheme for Cloud Computing

2020 ◽  
Vol 11 (1) ◽  
pp. 68-82
Author(s):  
Nurudeen Mahmud Ibrahim ◽  
Anazida Zainal
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Scheme ◽  
Distributed Intrusion Detection ◽  
Binary Segmentation

Intrusion detection systems (IDS) is an important security measure used to secure cloud resources, however, IDS often suffer from poor detection accuracy due to coordinated attacks such as a DDoS. Various research on distributed IDSs have been proposed to detect DDoS however, the limitations of these works the lack of technique to determine an appropriate period to share attack information among nodes in the distributed IDS. Therefore, this article proposes a distributed IDS that uses a binary segmentation change point detection algorithm to address the appropriate period to send attack information to nodes in distributed IDS and using parallel Stochastic Gradient Descent with Support Vector Machine (SGD-SVM) to achieve the distributed detection. The result of the proposed scheme was implemented in Apache Spark using NSL-KDD benchmark intrusion detection dataset. Experimental results show that the proposed distributed intrusion detection scheme outperforms existing distributed IDS for cloud computing.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

2015 ◽  
Vol 18 (3) ◽  
pp. 1263-1283 ◽  
Author(s):  
Neeraj Kumar ◽  
Jaskaran Preet Singh ◽  
Rasmeet S. Bali ◽  
Sudip Misra ◽  
Sana Ullah
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud ◽  
Distributed Intrusion Detection

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

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