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Discriminating DDoS Attacks from Flash Crowds Using Flow Correlation Coefficient

Platform : DOT NET

IEEE Projects Years : 2012 - 13

Discriminating DDoS Attacks from Flash Crowds Using Flow Correlation Coefficient

Abstract:

Distributed Denial of Service (DDoS) attack is a critical threat to the Internet, and botnets are usually the engines behind them. Sophisticated botmasters attempt to disable detectors by mimicking the traffic patterns of flash crowds. This poses a critical challenge to those who defend against DDoS attacks. In our deep study of the size and organization of current botnets, we found that the current attack flows are usually more similar to each other compared to the flows of flash crowds. Based on this, we proposed a discrimination algorithm using the flow correlation coefficient as a similarity metric among suspicious flows. We formulated the problem, and presented theoretical proofs for the feasibility of the proposed discrimination method in theory. Our extensive experiments confirmed the theoretical analysis and demonstrated the effectiveness of the proposed method in practice.

 

Existing System:

Previous work focused on extracting DDoS attack features, and was followed by detecting and filtering DDoS attack packets by the known features. However, these Methods cannot actively detect DDoS attacks. Flash crowds are unexpected, but legitimate, dramatic surges of access to a server, such as breaking news. The work of discriminating DDoS attacks from flash crowds has been explored for around a decade. This method involves human responses and can be annoying to users.

 

Proposed System:

The current most popular defence against flash crowd attacks is the use of graphical puzzles to differentiate between humans and bots. This method involves human responses and can be annoying to users. Bots are caught by honeypots and analyzed thoroughly via inverse engineering techniques. The proposed algorithm works independently of specific DDoS flooding attack genres. Therefore, it is effective against unknown forthcoming flooding attacks. The proposed correlation coefficient-based method is delay proof. This property is very effective against explicit random delay insertion among attack flows. We used the flow correlation coefficient as a metric to measure the similarity among suspicious flows to differentiate DDoS attacks from genuine flash crowds.

 

 

 

 

 

 

 

 

 

 

 

HARDWARE & SOFTWARE REQUIREMENTS:

 

HARDWARE REQUIREMENTS:

 

Processor                     :        Intel Pentium-IV

Speed                          :         1.6GHz

RAM                           :         2GB

Hard Disk                   :         500GB

General                        :        Key Board, Monitor, Mouse

 

 

SOFTWARE REQUIREMENTS:

 

Operating System       :           Windows XP, 7.

Software                     :           VS .NET 2008, SQL Server Tools 2005.

 

 



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