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GLOBALSOFT TECHNOLOGIES 
IEEE PROJECTS & SOFTWARE DEVELOPMENTS 
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE 
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS 
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmai l.com 
A System for Denial-of-Service Attack Detection Based on 
Multivariate Correlation Analysis 
ABSTRACT: 
Interconnected systems, such as Web servers, database servers, cloud computing 
servers etc, are now under threads from network attackers. As one of most 
common and aggressive means, Denial-of-Service (DoS) attacks cause serious 
impact on these computing systems. In this paper, we present a DoS attack 
detection system that uses Multivariate Correlation Analysis (MCA) for accurate 
network traffic characterization by extracting the geometrical correlations between 
network traffic features. Our MCA-based DoS attack detection system employs the 
principle of anomaly-based detection in attack recognition. This makes our 
solution capable of detecting known and unknown DoS attacks effectively by 
learning the patterns of legitimate network traffic only. Furthermore, a triangle-area- 
based technique is proposed to enhance and to speed up the process of MCA. 
The effectiveness of our proposed detection system is evaluated using KDD Cup 
99 dataset, and the influences of both non-normalized data and normalized data on 
the performance of the proposed detection system are examined. The results show
that our system outperforms two other previously developed state-of-the-art 
approaches in terms of detection accuracy. 
EXISTING SYSTEM: 
Generally, network-based detection systems can be classified into two main 
categories, namely misuse-based detection systems and anomaly-based detection 
systems. Misuse-based detection systems detect attacks by monitoring network 
activities and looking for matches with the existing attack signatures. In spite of 
having high detection rates to known attacks and low false positive rates, misuse-based 
detection systems are easily evaded by any new attacks and even variants of 
the existing attacks. Furthermore, it is a complicated and labor intensive task to 
keep signature database updated because signature generation is a manual process 
and heavily involves network security expertise. 
DISADVANTAGES OF EXISTING SYSTEM: 
 Most existing IDS are optimized to detect attacks with high accuracy. 
However, they still have various disadvantages that have been outlined in a 
number of publications and a lot of work has been done to analyze IDS in 
order to direct future research. 
 Besides others, one drawback is the large amount of alerts produced. 
PROPOSED SYSTEM: 
In this paper, we present a DoS attack detection system that uses Multivariate 
Correlation Analysis (MCA) for accurate network traffic characterization by
extracting the geometrical correlations between network traffic features. Our 
MCA-based DoS attack detection system employs the principle of anomaly-based 
detection in attack recognition. 
The DoS attack detection system presented in this paper employs the principles of 
MCA and anomaly-based detection. They equip our detection system with 
capabilities of accurate characterization for traffic behaviors and detection of 
known and unknown attacks respectively. A triangle area technique is developed to 
enhance and to speed up the process of MCA. A statistical normalization technique 
is used to eliminate the bias from the raw data. 
ADVANTAGES OF PROPOSED SYSTEM: 
 More detection accuracy 
 Less false alarm 
 Accurate characterization for traffic behaviors and detection of known and 
unknown attacks respectively 
SYSTEM ARCHITECTURE: 
BLOCK DIAGRAM:
Client Router Correlation 
Graph Analysis Server Attack Detection 
MODULES: 
1. Feature Normalization 
2. Multivariate Correlation Analysis 
3. Decision Making Module 
4. Evaluation of Attack detection 
MODULES DESCRIPTION: 
1. Feature Normalization Module: 
Analysis 
In this module, basic features are generated from ingress network traffic to the 
internal network where protected servers reside in and are used to form traffic 
records for a well-defined time interval. Monitoring and analyzing at the 
destination network reduce the overhead of detecting malicious activities by 
concentrating only on relevant inbound traffic. This also enables our detector to 
provide protection which is the best fit for the targeted internal network because
legitimate traffic profiles used by the detectors are developed for a smaller number 
of network services. 
2. Multivariate Correlation Analysis: 
In this Multivariate Correlation Analysis, in which the “Triangle Area Map 
Generation” module is applied to extract the correlations between two distinct 
features within each traffic record coming from the first step or the traffic record 
normalized by the “Feature Normalization” module in this step. The occurrence of 
network intrusions cause changes to these correlations so that the changes can be 
used as indicators to identify the intrusive activities. All the extracted correlations, 
namely triangle areas stored in Triangle Area Maps (TAMs), are then used to 
replace the original basic features or the normalized features to represent the traffic 
records. This provides higher discriminative information to differentiate between 
legitimate and illegitimate traffic records. 
3. Decision Making Module: 
In this module, the anomaly-based detection mechanism is adopted in Decision 
Making. It facilitates the detection of any DoS attacks without requiring any attack 
relevant knowledge. Furthermore, the labor-intensive attack analysis and the 
frequent update of the attack signature database in the case of misuse-based 
detection are avoided. Meanwhile, the mechanism enhances the robustness of the 
proposed detectors and makes them harder to be evaded because attackers need to 
generate attacks that match the normal traffic profiles built by a specific detection 
algorithm. This, however, is a labor-intensive task and requires expertise in the 
targeted detection algorithm. Specifically, two phases (i.e., the “Training Phase” 
and the “Test Phase”) are involved in Decision Making. The “Normal Profile
Generation” module is operated in the “Training Phase” to generate profiles for 
various types of legitimate traffic records, and the generated normal profiles are 
stored in a database. The “Tested Profile Generation” module is used in the “Test 
Phase” to build profiles for individual observed traffic records. Then, the tested 
profiles are handed over to the “Attack Detection” module, which compares the 
individual tested profiles with the respective stored normal profiles. A threshold-based 
classifier is emp loyed in the “Attack Detection” module to distinguish DoS 
attacks from legitimate traffic. 
4. Evaluation of Attack detection 
During the evaluation, the 10 percent labeled data of KDD Cup 99 dataset is used, 
where three types of legitimate traffic (TCP, UDP and ICMP traffic) and six 
different types of DoS attacks (Teardrop, Smurf, Pod, Neptune, Land and Back 
attacks) are available. All of these records are first filtered and then are further 
grouped into seven clusters according to their labels. We show the evaluation 
results in graph. 
SYSTEM REQUIREMENTS: 
HARDWARE REQUIREMENTS: 
 System : Pentium IV 2.4 GHz. 
 Hard Disk : 40 GB. 
 Floppy Drive : 1.44 Mb. 
 Monitor : 15 VGA Colour. 
 Mouse : Logitech. 
 Ram : 512 Mb.
SOFTWARE REQUIREMENTS: 
 Operating system : Windows XP/7. 
 Coding Language : ASP.net, C#.net 
 Tool : Visual Studio 2010 
 Database : SQL SERVER 2008 
REFERENCE: 
Zhiyuan Tan, Aruna Jamdagni, Xiangjian He‡, Senior Member, IEEE, Priyadarsi 
Nanda, Member, IEEE, and Ren Ping Liu, Member, IEEE, “A System for Denial-of- 
Service Attack Detection Based on Multivariate Correlation Analysis”, IEEE 
TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. , 
NO. , 2014.

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2014 IEEE DOTNET PARALLEL DISTRIBUTED PROJECT A system-for-denial-of-service-attack-detection-based-on-multivariate-correlation-analysis

  • 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmai l.com A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis ABSTRACT: Interconnected systems, such as Web servers, database servers, cloud computing servers etc, are now under threads from network attackers. As one of most common and aggressive means, Denial-of-Service (DoS) attacks cause serious impact on these computing systems. In this paper, we present a DoS attack detection system that uses Multivariate Correlation Analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. Our MCA-based DoS attack detection system employs the principle of anomaly-based detection in attack recognition. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle-area- based technique is proposed to enhance and to speed up the process of MCA. The effectiveness of our proposed detection system is evaluated using KDD Cup 99 dataset, and the influences of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The results show
  • 2. that our system outperforms two other previously developed state-of-the-art approaches in terms of detection accuracy. EXISTING SYSTEM: Generally, network-based detection systems can be classified into two main categories, namely misuse-based detection systems and anomaly-based detection systems. Misuse-based detection systems detect attacks by monitoring network activities and looking for matches with the existing attack signatures. In spite of having high detection rates to known attacks and low false positive rates, misuse-based detection systems are easily evaded by any new attacks and even variants of the existing attacks. Furthermore, it is a complicated and labor intensive task to keep signature database updated because signature generation is a manual process and heavily involves network security expertise. DISADVANTAGES OF EXISTING SYSTEM:  Most existing IDS are optimized to detect attacks with high accuracy. However, they still have various disadvantages that have been outlined in a number of publications and a lot of work has been done to analyze IDS in order to direct future research.  Besides others, one drawback is the large amount of alerts produced. PROPOSED SYSTEM: In this paper, we present a DoS attack detection system that uses Multivariate Correlation Analysis (MCA) for accurate network traffic characterization by
  • 3. extracting the geometrical correlations between network traffic features. Our MCA-based DoS attack detection system employs the principle of anomaly-based detection in attack recognition. The DoS attack detection system presented in this paper employs the principles of MCA and anomaly-based detection. They equip our detection system with capabilities of accurate characterization for traffic behaviors and detection of known and unknown attacks respectively. A triangle area technique is developed to enhance and to speed up the process of MCA. A statistical normalization technique is used to eliminate the bias from the raw data. ADVANTAGES OF PROPOSED SYSTEM:  More detection accuracy  Less false alarm  Accurate characterization for traffic behaviors and detection of known and unknown attacks respectively SYSTEM ARCHITECTURE: BLOCK DIAGRAM:
  • 4. Client Router Correlation Graph Analysis Server Attack Detection MODULES: 1. Feature Normalization 2. Multivariate Correlation Analysis 3. Decision Making Module 4. Evaluation of Attack detection MODULES DESCRIPTION: 1. Feature Normalization Module: Analysis In this module, basic features are generated from ingress network traffic to the internal network where protected servers reside in and are used to form traffic records for a well-defined time interval. Monitoring and analyzing at the destination network reduce the overhead of detecting malicious activities by concentrating only on relevant inbound traffic. This also enables our detector to provide protection which is the best fit for the targeted internal network because
  • 5. legitimate traffic profiles used by the detectors are developed for a smaller number of network services. 2. Multivariate Correlation Analysis: In this Multivariate Correlation Analysis, in which the “Triangle Area Map Generation” module is applied to extract the correlations between two distinct features within each traffic record coming from the first step or the traffic record normalized by the “Feature Normalization” module in this step. The occurrence of network intrusions cause changes to these correlations so that the changes can be used as indicators to identify the intrusive activities. All the extracted correlations, namely triangle areas stored in Triangle Area Maps (TAMs), are then used to replace the original basic features or the normalized features to represent the traffic records. This provides higher discriminative information to differentiate between legitimate and illegitimate traffic records. 3. Decision Making Module: In this module, the anomaly-based detection mechanism is adopted in Decision Making. It facilitates the detection of any DoS attacks without requiring any attack relevant knowledge. Furthermore, the labor-intensive attack analysis and the frequent update of the attack signature database in the case of misuse-based detection are avoided. Meanwhile, the mechanism enhances the robustness of the proposed detectors and makes them harder to be evaded because attackers need to generate attacks that match the normal traffic profiles built by a specific detection algorithm. This, however, is a labor-intensive task and requires expertise in the targeted detection algorithm. Specifically, two phases (i.e., the “Training Phase” and the “Test Phase”) are involved in Decision Making. The “Normal Profile
  • 6. Generation” module is operated in the “Training Phase” to generate profiles for various types of legitimate traffic records, and the generated normal profiles are stored in a database. The “Tested Profile Generation” module is used in the “Test Phase” to build profiles for individual observed traffic records. Then, the tested profiles are handed over to the “Attack Detection” module, which compares the individual tested profiles with the respective stored normal profiles. A threshold-based classifier is emp loyed in the “Attack Detection” module to distinguish DoS attacks from legitimate traffic. 4. Evaluation of Attack detection During the evaluation, the 10 percent labeled data of KDD Cup 99 dataset is used, where three types of legitimate traffic (TCP, UDP and ICMP traffic) and six different types of DoS attacks (Teardrop, Smurf, Pod, Neptune, Land and Back attacks) are available. All of these records are first filtered and then are further grouped into seven clusters according to their labels. We show the evaluation results in graph. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS:  System : Pentium IV 2.4 GHz.  Hard Disk : 40 GB.  Floppy Drive : 1.44 Mb.  Monitor : 15 VGA Colour.  Mouse : Logitech.  Ram : 512 Mb.
  • 7. SOFTWARE REQUIREMENTS:  Operating system : Windows XP/7.  Coding Language : ASP.net, C#.net  Tool : Visual Studio 2010  Database : SQL SERVER 2008 REFERENCE: Zhiyuan Tan, Aruna Jamdagni, Xiangjian He‡, Senior Member, IEEE, Priyadarsi Nanda, Member, IEEE, and Ren Ping Liu, Member, IEEE, “A System for Denial-of- Service Attack Detection Based on Multivariate Correlation Analysis”, IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. , NO. , 2014.