scholarly journals AN EFFICIENT HYBRID MODEL FOR DETECTING DISTRIBUTED DENIAL OF SERVICE (DDOS) ATTACKS IN CLOUD COMPUTING USING MULTIVARIATE CORRELATION AND DATA MINING CLUSTERING TECHNIQUES

2018 ◽  
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Hybrid Model ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Clustering Techniques ◽  
Multivariate Correlation

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 Shield Advanced Mitigation System of Distributed Denial of Service Attack in Integration of Internet of Things and Cloud Computing Environment

2019 ◽  
Vol 9 (2) ◽  
pp. 1300-1306
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Denial Of Service ◽  
Cloud Services ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Denial Of Service Attack ◽  
Ddos Attack ◽  
Service Attack ◽  
Verification Process

Cloud services among public and business companies have become popular in recent years. For production activities, many companies rely on cloud technology. Distributed Denial of Services (DDoS) attack is an extremely damaging general and critical type of cloud attacks. Several efforts have been made in recent years to identify numerous types of DDoS attacks. This paper discusses the different types of DDoS attacks and their cloud computing consequences. Distributed Denial of Service attack (DDoS) is a malicious attempt to disrupt the normal movement of a targeted server, service or network through influx of internet traffic overwhelming the target or its infrastructure. The use of multiple affected computer systems as a source of attacks makes DDoS attacks effective. Computers and other networked tools, including IoT phones, may be included on exploited machines. A DDoS attack from a high level resembles a traffic jam that is caused by roads that prevents normal travel at their desired destination. So DDoS Attack is a major challenging problem in integrated Cloud and IoT. Hence, this paper proposes Shield Advanced Mitigation System of Distributed Denial of Service Attack in the integration of Internet of Things and Cloud Computing Environment. This secure architecture use two verification process to identify whether user is legitimate or malicious. Dynamic Captcha Testing with Equal Probability test for first verification process, moreover Zigsaw Image Puzzle Test is used for second verification process, and Intrusion Detection Prevention System is used to identify and prevent malicious user, moreover reverse proxy is used to hide server location. These functional components and flow could strengthen security in Client side network to provide cloud services furthermore to overcome distributed denial of service attack in the integration of Internet of Things and Cloud Environment.

Data Mining Techniques for Distributed Denial of Service Attacks Detection in the Internet of Things

2016 ◽  
pp. 275-334 ◽  
Author(s):  
Pheeha Machaka ◽  
Fulufhelo Nelwamondo
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Denial Of Service ◽  
Current Data ◽  
The Internet ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Data Mining Techniques ◽  
The Internet Of Things

This chapter reviews the evolution of the traditional internet into the Internet of Things (IoT). The characteristics and application of the IoT are also reviewed, together with its security concerns in terms of distributed denial of service attacks. The chapter further investigates the state-of-the-art in data mining techniques for Distributed Denial of Service (DDoS) attacks targeting the various infrastructures. The chapter explores the characteristics and pervasiveness of DDoS attacks. It also explores the motives, mechanisms and techniques used to execute a DDoS attack. The chapter further investigates the current data mining techniques that are used to combat and detect these attacks, their advantages and disadvantages are explored. Future direction of the research is also provided.

scholarly journals A Study of DDOS (Distributed-denial-of- service) Attacks and Its Preventions

2020 ◽  
pp. 176-181
Author(s):  
Bhawna Tripathi ◽  
Devesh Katiyar ◽  
Gaurav Goel
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Denial Of Service ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Ddos Attack

<p>The data security is one of the most important themes in the information World. Cloud Computing is a grooving technology and implemented by many companies, but there are many issues and one of them is DDOS. .The DDOS attack is one of the most Threatening attacks in today’s world. This paper introduces about the major problem occur in the security which is known as DDOS attacks The study of this research is to find out the various techniques to prevent these attacks along with their modification techniques and to find out any possible solution.</p>

scholarly journals Distinguishing flooding distributed denial of service from flash crowds using four data mining approaches

2017 ◽  
Vol 14 (3) ◽  
pp. 839-856
Author(s):  
Bin Kong ◽  
Kun Yang ◽  
Degang Sun ◽  
Meimei Li ◽  
Zhixin Shi
Keyword(s):  
Data Mining ◽  
Denial Of Service ◽  
High Accuracy ◽  
Entropy Method ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Extensive Analysis ◽  
Significant Damage ◽  
New Feature ◽  
Flash Crowds

Flooding Distributed Denial of Service (DDoS) attacks can cause significant damage to Internet. These attacks have many similarities to Flash Crowds (FCs) and are always difficult to distinguish. To solve this issue, this paper first divides existing methods into two categories to clarify existing researches. Moreover, after conducting an extensive analysis, a new feature set is concluded to profile DDoS and FC. Along with this feature set, this paper proposes a new method that employs Data Mining approaches to discriminate between DDoS attacks and FCs. Experiments are conducted to evaluate the proposed method based on two realworld datasets. The results demonstrate that the proposed method could achieve a high accuracy (more than 98%). Additionally, compared with a traditional entropy method, the proposed method still demonstrates better performance.

Data Mining Techniques for Distributed Denial of Service Attacks Detection in the Internet of Things

2020 ◽  
pp. 561-608
Author(s):  
Pheeha Machaka ◽  
Fulufhelo Nelwamondo
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Denial Of Service ◽  
Current Data ◽  
The Internet ◽  
Denial Of Service Attacks ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Data Mining Techniques ◽  
The Internet Of Things

This chapter reviews the evolution of the traditional internet into the Internet of Things (IoT). The characteristics and application of the IoT are also reviewed, together with its security concerns in terms of distributed denial of service attacks. The chapter further investigates the state-of-the-art in data mining techniques for Distributed Denial of Service (DDoS) attacks targeting the various infrastructures. The chapter explores the characteristics and pervasiveness of DDoS attacks. It also explores the motives, mechanisms and techniques used to execute a DDoS attack. The chapter further investigates the current data mining techniques that are used to combat and detect these attacks, their advantages and disadvantages are explored. Future direction of the research is also provided.

scholarly journals The Slow HTTP Distributed Denial of Service Attack Detection in Cloud

2019 ◽  
Vol 20 (2) ◽  
pp. 285-298 ◽  
Author(s):  
A. Dhanapal ◽  
P. Nithyanandam
Keyword(s):  
Cloud Computing ◽  
Financial Services ◽  
Denial Of Service ◽  
Attack Detection ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Denial Of Service Attack ◽  
Ddos Attack ◽  
Service Attack ◽  
The Cost

Cloud computing became popular due to nature as it provides the flexibility to add or remove the resources on-demand basis. This also reduces the cost of investments for the enterprises significantly. The adoption of cloud computing is very high for enterprises running their online applications. The availability of online services is critical for businesses like financial services, e-commerce applications, etc. Though cloud provides availability, still these applications are having potential threats of going down due to the slow HTTP Distributed Denial of Service (DDoS) attack in the cloud. The slow HTTP attacks intention is to consume all the available server resources and make it unavailable to the real users. The slow HTTP DDoS attack comes with different formats such as slow HTTP headers attacks, slow HTTP body attacks and slow HTTP read attacks. Detecting the slow HTTP DDoS attacks in the cloud is very crucial to safeguard online cloud applications. This is a very interesting and challenging topic in DDoS as it mimics the slow network. This paper proposed a novel method to detect slow HTTP DDoS attacks in the cloud. The solution is implemented using the OpenStack cloud platform. The experiments conducted exhibits the accurate results on detecting the attacks at the early stages. The slowHTTPTest open source tool is used in this experiment to originate slow HTTP DDoS attacks.

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