An Intrusion Detection System for Malicious Attacks in Cloud Environment Using Decision Tree Techniques

2018 ◽  
Vol 6 (8) ◽  
pp. 878-881
Author(s):  
Gopala B ◽  
M. Hanumanthappa
2021 ◽  
Vol 11 (1) ◽  
pp. 365-379
Author(s):  
Wisam Elmasry ◽  
Akhan Akbulut ◽  
Abdul Halim Zaim

Abstract Although cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature.


2014 ◽  
Vol 16 (4) ◽  
pp. 16-26 ◽  
Author(s):  
Partha Ghosh ◽  
◽  
Chameli Debnath ◽  
Dipjyoti Metia ◽  
Dr. Ruma Dutta

2021 ◽  
pp. 1826-1839
Author(s):  
Sandeep Adhikari, Dr. Sunita Chaudhary

The exponential growth in the use of computers over networks, as well as the proliferation of applications that operate on different platforms, has drawn attention to network security. This paradigm takes advantage of security flaws in all operating systems that are both technically difficult and costly to fix. As a result, intrusion is used as a key to worldwide a computer resource's credibility, availability, and confidentiality. The Intrusion Detection System (IDS) is critical in detecting network anomalies and attacks. In this paper, the data mining principle is combined with IDS to efficiently and quickly identify important, secret data of interest to the user. The proposed algorithm addresses four issues: data classification, high levels of human interaction, lack of labeled data, and the effectiveness of distributed denial of service attacks. We're also working on a decision tree classifier that has a variety of parameters. The previous algorithm classified IDS up to 90% of the time and was not appropriate for large data sets. Our proposed algorithm was designed to accurately classify large data sets. Aside from that, we quantify a few more decision tree classifier parameters.


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