A Confidence Interval Based Filtering Against DDoS Attack in Cloud Environment

2020 ◽  
Vol 14 (4) ◽  
pp. 42-56
Author(s):  
Mohamed Haddadi ◽  
Rachid Beghdad
Keyword(s):  
Confidence Interval ◽  
Denial Of Service ◽  
False Negative ◽  
Computation Time ◽  
Distributed Denial Of Service ◽  
Cloud Environment ◽  
Ddos Attacks ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Novel Approach

Distributed denial of service (DDoS) attacks have become a serious danger against the availability of services in cloud computing environment. Current defending mechanisms cannot detect DDoS attacks with high accuracy. This is mainly due to the fact that the unrealistic value of the studied variables was used. In view of this problem, the authors propose a novel approach called confidence interval-based filtering (CIF) to detect DDoS attacks. The proposed approach is implemented using VMware and JAVA applications. The simulation results showed that CIF outperforms the existing approaches in terms of detection rate and false negative and positive rates with an acceptable computation time.

scholarly journals Adaptive Learning and Automatic Filtering of Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environment

2019 ◽  
Vol 9 (1S3) ◽  
pp. 200-205
Keyword(s):  
Cloud Computing ◽  
Adaptive Learning ◽  
Denial Of Service ◽  
Cloud Services ◽  
Detection Accuracy ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Automatic Filtering

Distributed Denial of Service (DDoS) attacks has become the most powerful cyber weapon to target the businesses that operate on the cloud computing environment. The sophisticated DDoS attack affects the functionalities of the cloud services and affects its core capabilities of cloud such as availability and reliability. The current intrusion detection system (IDS) must cope with the dynamicity and intensity of immense traffic at the cloud hosted applications and the security attack must be inspected based on the attack flow characteristics. Hence, the proposed Adaptive Learning and Automatic Filtering of Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environment is designed to adapt with varying kind of protocol attacks using misuse detection. The system is equipped with custom and threshold techniques that satisfies security requirements and can identify the different DDoS security attacks. The proposed system provides promising results in detecting the DDoS attacks in cloud environment with high detection accuracy and good alert reduction. Threshold method provides 98% detection accuracy with 99.91%, 99.92% and 99.94% alert reduction for ICMP, UDP and TCP SYN flood attack. The defense system filters the attack sources at the target virtual instance and protects the cloud applications from DDoS attacks.

scholarly journals A Reliable Friedman Hypothesis-Based Detection and Adaptive Load Balancing Scheme for Mitigating Reduction of Quality DDoS Attacks in Cloud Computing

2019 ◽  
Vol 9 (1) ◽  
pp. 360-366
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Cloud Environment ◽  
Cloud Infrastructure ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
On Demand ◽  
Demand Service

The computing resource availability in a cloud computing environment is considered as the vital attribute among the security essentialities due to the consequence of on its on demand service. The class of adversaries related to the Distributed Denial of Service (DDoS) attack is prevalent in the cloud infrastructure for exploiting the vulnerabilities during the implementation of their attack that still make the process of providing security and availability at the same time as a challenging objective. In specific, The in cloud computing is the major threat during the process of balancing security and availability at the same time. In this paper, A Reliable Friedman Hypothesis-based Detection and Adaptive Load Balancing Scheme (RFALBS-RoQ-DDOS) is contributed for effective detection of RoQDDoS attacks through Friedman hypothesis testing. It also inherited an adaptive load balancing approach that prevents the degree of imbalance in the cloud environment. The simulation results of the proposed RFALBS-RoQ-DDoS technique confirmed a superior detection rate and a adaptive load balancing rate of nearly 23% and 28% predominant to the baseline DDoS mitigation schemes considered for investigation.

A Novel Approach to Develop and Deploy Preventive Measures for Different Types of DDoS Attacks

2020 ◽  
Vol 14 (2) ◽  
pp. 1-19
Author(s):  
Khundrakpam Johnson Singh ◽  
Janggunlun Haokip ◽  
Usham Sanjota Chanu
Keyword(s):  
Preventive Measures ◽  
Denial Of Service ◽  
External Environment ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
New Era ◽  
Denial Of Service Attack ◽  
Novel Approach ◽  
Different Types ◽  
Service Attack

In the new era of computers, everyone relies on the internet for basic day-to-day activities to sophisticated and secret tasks. The cyber threats are increasing, not only theft and manipulation of someone's information, but also forcing the victim to deny other requests. A DDoS (Distributed Denial of Service) attack, which is one of the serious issues in today's cyber world needs to be detected and their advance towards the server should be blocked. In the article, the authors are focusing mainly on preventive measures of different types of DDoS attacks using multiple IPtables rules and Windows firewall advance security settings configuration, which would be feasibly free on any PC. The IPtables when appropriately selected and implemented can establish a relatively secure barrier for the system and the external environment.

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