scholarly journals Enhanced Security In Cloud With Multi-Level Intrusion Detection System

2015 ◽  
pp. 9-12
Author(s):  
M. KUZHALISAI ◽  
G. GAYATHRI
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Large Scale ◽  
Detection System ◽  
Computing System ◽  
Cyber Attacks ◽  
Security Level ◽  
Security Service ◽  
Cloud Computing System ◽  
System Resource

Cloud computing is a new type of service which provides large scale computing resource to each customer. Cloud Computing Systems can be easily threatened by various cyber attacks, because most of Cloud computing system needs to contain some Intrusion Detection Systems (IDS) for protecting each Virtual Machine (VM) against threats. In this case, there exists a tradeoff between the security level of the IDS and the system performance. If the IDS provide stronger security service using more rules or patterns, then it needs much more computing resources in proportion to the strength of security. So the amount of resources allocating for customers decreases. Another problem in Cloud Computing is that, huge amount of logs makes system administrators hard to analyse them. In this paper, we propose a method that enables cloud computing system to achieve both effectiveness of using the system resource and strength of the security service without trade-off between them.

Intrusion Detection Based on Dynamic Gemini Population DE-K-mediods Clustering on Hadoop Platform

2020 ◽  
pp. 2150001
Author(s):  
Wentie Wu ◽  
Shengchao Xu
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Differential Evolution ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Detection System ◽  
Differential Evolution Algorithm ◽  
Detection Algorithm ◽  
Local Optimum ◽  
Evolution Algorithm

In view of the fact that the existing intrusion detection system (IDS) based on clustering algorithm cannot adapt to the large-scale growth of system logs, a K-mediods clustering intrusion detection algorithm based on differential evolution suitable for cloud computing environment is proposed. First, the differential evolution algorithm is combined with the K-mediods clustering algorithm in order to use the powerful global search capability of the differential evolution algorithm to improve the convergence efficiency of large-scale data sample clustering. Second, in order to further improve the optimization ability of clustering, a dynamic Gemini population scheme was adopted to improve the differential evolution algorithm, thereby maintaining the diversity of the population while improving the problem of being easily trapped into a local optimum. Finally, in the intrusion detection processing of big data, the optimized clustering algorithm is designed in parallel under the Hadoop Map Reduce framework. Simulation experiments were performed in the open source cloud computing framework Hadoop cluster environment. Experimental results show that the overall detection effect of the proposed algorithm is significantly better than the existing intrusion detection algorithms.

scholarly journals Optimal Resource Scheduling for IaaS Cloud Computing using Cuckoo Search Algorithm

2017 ◽  
Vol 9 (1-3) ◽  
Author(s):  
Syed Hamid Hussain Madni ◽  
Muhammad Shafie Abd Latiff ◽  
Shafi’i Muhammad Abdulhamid
Keyword(s):  
Cloud Computing ◽  
Search Algorithm ◽  
Resource Scheduling ◽  
Cuckoo Search ◽  
Computing System ◽  
Cuckoo Search Algorithm ◽  
Cloud Computing System ◽  
Optimal Resource ◽  
System Resource ◽  
Np Hard Problem

Effective resource scheduling is essential for the overall performance of cloud computing system. Resource scheduling problem in IaaS cloud computing is investigated in this paper. It is established to be an NP-hard problem. A recently developed Cuckoo Search (CS) meta-heuristic algorithm is proposed in this paper, to minimize the response time, makespan and throughput for the resource scheduling in IaaS cloud computing. Simulation results show that CS algorithm outperforms that of Ant Colony Optimization (ACO) algorithm based on the considered parameters. 

scholarly journals Big Data Handling Approach for Unauthorized Access of Cloud Computing

2021 ◽  
Author(s):  
Abdul Razaque ◽  
Shaldanbayeva Nazerke ◽  
Bandar Alotaibi ◽  
Munif Alotaibi ◽  
Akhmetov Murat ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Access Control ◽  
Intrusion Detection ◽  
Data Access ◽  
Computing System ◽  
Data Handling ◽  
Unauthorized Access ◽  
Data Access Control ◽  
Cloud Computing System

Nowadays, cloud computing is one of the important and rapidly growing paradigms that extend its capabilities and applications in various areas of life. The cloud computing system challenges many security issues, such as scalability, integrity, confidentiality, and unauthorized access, etc. An illegitimate intruder may gain access to the sensitive cloud computing system and use the data for inappropriate purposes that may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for Big data in cloud computing. The HUDU aims to restrict illegitimate users from accessing the cloud and data security provision. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. HUDH involves three algorithms; Advanced Encryption Standards (AES) for encryption, Attribute-Based Access Control (ABAC) for data access control, and Hybrid Intrusion Detection (HID) for unauthorized access detection. The proposed scheme is implemented using Python and Java language. Testing results demonstrate that the HUDH can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% high accuracy.

scholarly journals High-Throughput Cloud Computing with the Cloudscheduler VM Provisioning Service

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
F. Berghaus ◽  
K. Casteels ◽  
C. Driemel ◽  
M. Ebert ◽  
F. F. Galindo ◽  
...  
Keyword(s):  
Cloud Computing ◽  
High Throughput ◽  
Particle Physics ◽  
Large Scale ◽  
Computing System ◽  
Current Status ◽  
Scale Production ◽  
Original Design ◽  
Cloud Computing System ◽  
Vm Provisioning

AbstractWe describe a high-throughput computing system for running jobs on public and private computing clouds using the HTCondor job scheduler and the cloudscheduler VM provisioning service. The distributed cloud computing system is designed to simultaneously use dedicated and opportunistic cloud resources at local and remote locations. It has been used for large-scale production particle physics workloads for many years using thousands of cores on three continents. A decade after its initial design and implementation, cloudscheduler has been modernized to take advantage of new software designs, improved operating system capabilities and support packages. The updated cloudscheduler is more resilient and scalable, with expanded capabilities. We present an overview of the original design and then describe the new version of the distributed compute cloud system. We conclude with a review of the current status and future plans.

Cognitive Visual Analytics of Multi-Dimensional Cloud System Monitoring Data

2017 ◽  
Vol 9 (1) ◽  
pp. 20-34
Author(s):  
George Baciu ◽  
Yungzhe Wang ◽  
Chenhui Li
Keyword(s):  
Cloud Computing ◽  
System Dynamics ◽  
Resource Utilization ◽  
Visual Analytics ◽  
High Performance ◽  
Large Scale ◽  
Computing System ◽  
Cloud System ◽  
Past Behavior ◽  
Cloud Computing System

Hardware virtualization has enabled large scale computational service delivery models with high cost leverage and improved resource utilization on cloud computing platforms. This has completely changed the landscape of computing in the last decade. It has also enabled large–scale data analytics through distributed high performance computing. Due to the infrastructure complexity, end–users and administrators of cloud platforms can rarely obtain a full picture of the state of cloud computing systems and data centers. Recent monitoring tools enable users to obtain large amounts of data with respect to many utilization parameters of cloud platforms. However, they fail to get the maximal overall insight into the resource utilization dynamics of cloud platforms. Furthermore, existing tools make it difficult to observe large-scale patterns, making it difficult to learn from the past behavior of cloud system dynamics. In this work, the authors describe a perceptual-based interactive visualization platform that gives users and administrators a cognitive view of cloud computing system dynamics.

Adaptive Threshold Based Scheduler for Batch of Independent Jobs for Cloud Computing System

2018 ◽  
Vol 9 (4) ◽  
pp. 20-39
Author(s):  
TAJ ALAM ◽  
PARITOSH DUBEY ◽  
ANKIT KUMAR
Keyword(s):  
Cloud Computing ◽  
Distributed Systems ◽  
High Performance ◽  
Large Scale ◽  
Real Life ◽  
Interval Estimation ◽  
Computing System ◽  
Adaptive Threshold ◽  
Cloud Computing System ◽  
Heterogeneous Distributed Systems

Distributed systems are efficient means of realizing high-performance computing (HPC). They are used in meeting the demand of executing large-scale high-performance computational jobs. Scheduling the tasks on such computational resources is one of the prime concerns in the heterogeneous distributed systems. Scheduling jobs on distributed systems are NP-complete in nature. Scheduling requires either heuristic or metaheuristic approach for sub-optimal but acceptable solutions. An adaptive threshold-based scheduler is one such heuristic approach. This work proposes adaptive threshold-based scheduler for batch of independent jobs (ATSBIJ) with the objective of optimizing the makespan of the jobs submitted for execution on cloud computing systems. ATSBIJ exploits the features of interval estimation for calculating the threshold values for generation of efficient schedule of the batch. Simulation studies on CloudSim ensures that the ATSBIJ approach works effectively for real life scenario.

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