FVBA: A combined statistical approach for low rate degrading and high bandwidth disruptive DDoS attacks detection in ISP domain

2008 ◽  
Author(s):  
B. B. Gupta ◽  
Manoj Misra ◽  
R. C. Joshi
Keyword(s):  
Statistical Approach ◽  
Ddos Attacks ◽  
High Bandwidth ◽  
Low Rate

Perimeter-based defense against high bandwidth DDoS attacks

2005 ◽  
Vol 16 (6) ◽  
pp. 526-537 ◽  
Author(s):  
S. Chen ◽  
Q. Song
Keyword(s):  
Ddos Attacks ◽  
High Bandwidth

scholarly journals SOFT COMPUTING BASED AUTONOMOUS LOW RATE DDOS ATTACK DETECTION AND SECURITY FOR CLOUD COMPUTING

2019 ◽  
Vol 2019 (2) ◽  
pp. 80-90 ◽  
Author(s):  
Mugunthan S. R.
Keyword(s):  
Cloud Computing ◽  
Soft Computing ◽  
High Speed ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Counter Measures ◽  
Cloud Architecture ◽  
Ddos Attack Detection ◽  
Low Rate

The fundamental advantage of the cloud environment is its instant scalability in rendering the service according to the various demands. The recent technological growth in the cloud computing makes it accessible to people from everywhere at any time. Multitudes of user utilizes the cloud platform for their various needs and store their complete details that are personnel as well as confidential in the cloud architecture. The storage of the confidential information makes the cloud architecture attractive to its hackers, who aim in misusing the confidential/secret information’s. The misuse of the services and the resources of the cloud architecture has become a common issue in the day to day usage due to the DDOS (distributed denial of service) attacks. The DDOS attacks are highly mature and continue to grow at a high speed making the detecting and the counter measures a challenging task. So the paper uses the soft computing based autonomous detection for the Low rate-DDOS attacks in the cloud architecture. The proposed method utilizes the hidden Markov Model for observing the flow in the network and the Random forest in classifying the detected attacks from the normal flow. The proffered method is evaluated to measure the performance improvement attained in terms of the Recall, Precision, specificity, accuracy and F-measure.

Detection of Low-Rate Cloud DDoS Attacks in Frequency Domain Using Fast Hartley Transform

2020 ◽  
Vol 112 (3) ◽  
pp. 1735-1762
Author(s):  
Neha Agrawal ◽  
Shashikala Tapaswi
Keyword(s):  
Frequency Domain ◽  
Ddos Attacks ◽  
Hartley Transform ◽  
Low Rate

scholarly journals Proposed statistical-based approach for detecting distribute denial of service against the controller of software defined network (SADDCS)

2018 ◽  
Vol 218 ◽  
pp. 02012 ◽  
Author(s):  
Mohammad A. AL-Adaileh ◽  
Mohammed Anbar ◽  
Yung-Wey Chong ◽  
Ahmed Al-Ani
Keyword(s):  
Statistical Analysis ◽  
Denial Of Service ◽  
Attack Detection ◽  
Detection Accuracy ◽  
Ddos Attacks ◽  
Large Area ◽  
Network Resources ◽  
Network Resource ◽  
High Bandwidth ◽  
Sdn Controller

Software-defined networkings (SDNs) have grown rapidly in recent years be-cause of SDNs are widely used in managing large area networks and securing networks from Distributed Denial of Services (DDoS) attacks. SDNs allow net-works to be monitored and managed through centralized controller. Therefore, SDN controllers are considered as the brain of networks and are considerably vulnerable to DDoS attacks. Thus, SDN controller suffer from several challenges that exhaust network resources. For SDN controller, the main target of DDoS attacks is to prevent legitimate users from using a network resource or receiving their services. Nevertheless, some approaches have been proposed to detect DDoS attacks through the examination of the traffic behavior of networks. How-ever, these approaches take too long to process all incoming packets, thereby leading to high bandwidth consumption and delays in the detection of DDoS at-tacks. In addition, most existing approaches for the detection of DDoS attacks suffer from high positive/negative false rates and low detection accuracy. This study proposes a new approach to detecting DDoS attacks. The approach is called the statistical-based approach for detecting DDoS against the controllers of software-defined networks. The proposed approach is designed to detect the presence of DDoS attacks accurately, reduce false positive/negative flow rates, and minimize the complexity of targeting SDN controllers according to a statistical analysis of packet features. The proposed approach passively captures net-work traffic, filters traffic, and selects the most significant features that contribute to DDoS attack detection. The general stages of the proposed approach are (i) da-ta preprocessing, (ii) statistical analysis, (iii) correlation identification between two vectors, and (iv) rule-based DDoS detection.

Low-rate DDoS attacks detection method using data compression and behavior divergence measurement

2021 ◽  
Vol 100 ◽  
pp. 102107
Author(s):  
Xinqian Liu ◽  
Jiadong Ren ◽  
Haitao He ◽  
Qian Wang ◽  
Chen Song
Keyword(s):  
Data Compression ◽  
Detection Method ◽  
Ddos Attacks ◽  
Using Data ◽  
And Behavior ◽  
Low Rate

A TCP-friendly AQM algorithm to mitigate low-rate DDoS attacks

2016 ◽  
Vol 9 (1/2) ◽  
pp. 149 ◽  
Author(s):  
Jiarun Lin ◽  
Changwang Zhang ◽  
Zhiping Cai ◽  
Qiang Liu ◽  
Jianping Yin
Keyword(s):  
Ddos Attacks ◽  
Tcp Friendly ◽  
Low Rate
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