Novel Intrusion Detection System for Cloud Computing: A Case Study

2015 ◽  
pp. 386-398
Author(s):  
Ming-Yi Liao ◽  
Zhi-Kai Mo ◽  
Mon-Yen Luo ◽  
Chu-Sing Yang ◽  
Jiann-Liang Chen
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System

scholarly journals Effective Intrusion Detection System by using LOS Classifier

2019 ◽  
Vol 9 (2) ◽  
pp. 2961-2966
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Social Networking ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Approaches ◽  
Area Unit ◽  
Hybrid Development

With winning advances like catch of Things, Cloud Computing and Social Networking, mammoth proportions of framework traffic associated information area unit made Intrusion Detection System for sort out security suggests the strategy to look at partner unapproved access on framework traffic. For Intrusion Detection System we are going to call attention to with respect to Machine Learning Approaches. it's accomplice rising field of enrolling which can explicitly act with a decent arrangement of less human affiliation. System gains from the data intentionally affirmation and makes perfect objectives. all through this paper we keep an eye on zone unit going to separated styles of Machine Learning pulls in near and had done relative examination in it. inside the last we keep an eye on territory unit going to foreseen the idea of hybrid development, that might be a blend of host principally and framework based for the most part Intrusion Detection System.

An Online Intrusion Detection System to Cloud Computing Based on Neucube Algorithms

2020 ◽  
pp. 1042-1059 ◽  
Author(s):  
Ammar Almomani ◽  
Mohammad Alauthman ◽  
Firas Albalas ◽  
O. Dorgham ◽  
Atef Obeidat
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Network Architecture ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Malicious Attack ◽  
Online Mode ◽  
Learning Techniques ◽  
Network Operations

This article describes how as network traffic grows, attacks on traffic become more complicated and harder to detect. Recently, researchers have begun to explore machine learning techniques with cloud computing technologies to classify network threats. So, new and creative ways are needed to enhance intrusion detection system. This article addresses the source of the above issues through detecting an intrusion in cloud computing before it further disrupts normal network operations, because the complexity of malicious attack techniques have evolved from traditional malicious attack technologies (direct malicious attack), which include different malicious attack classes, such as DoS, Probe, R2L, and U2R malicious attacks, especially the zero-day attack in online mode. The proposed online intrusion detection cloud system (OIDCS) adopts the principles of the new spiking neural network architecture called NeuCube algorithm. It is proposed that this system is the first filtering system approach that utilizes the NeuCube algorithm. The OIDCS inherits the hybrid (supervised/unsupervised) learning feature of the NeuCube algorithm and uses this algorithm in an online system with lifelong learning to classify input while learning the system. The system is accurate, especially when working with a zero-day attack, reaching approximately 97% accuracy based on the to-be-remembered (TBR) encoding algorithm.

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