A Summary of Detection of Denial-of-QoS Attacks on DiffServ Networks
نویسندگان
چکیده
MAHADIK, VINAY ASHOK. Detection of Denial of QoS Attacks on DiffServ Networks. (Under the direction of Dr. Douglas S. Reeves.) In this work, we describe a method of detecting denial of Quality of Service (QoS) attacks on Differentiated Services (DiffServ) networks. Our approach focusses on real time and quick detection, scalability to large networks, and a negligible false alarm generation rate. This is the first comprehensive study on DiffServ monitoring. Our contributions to this research area are 1. We identify several potential attacks, develop/use research implementations of each on our testbed and investigate their effects on the QoS sensitive network flows. 2. We study the effectiveness of several anomaly detection approaches; select and adapt SRI’s NIDES statistical inference algorithm and EWMA Statistical Process Control technique for use in our anomaly detection engine. 3. We then emulate a Wide Area Network on our testbed. We measure the effectiveness of our anomaly detection system in detecting the attacks and present the results obtained as a justification of our work. 4. We verify our findings through simulation of the network and the attacks on NS2 (the Network Simulator, version 2). We believe that given the results of the tests with our implementation of the attacks and the detection system, further validated by the simulations, the method is a strong candidate for QoS-intrusion detection for a low-cost commercial deployment. Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 2002 2. REPORT TYPE 3. DATES COVERED 00-00-2002 to 00-00-2002 4. TITLE AND SUBTITLE Detection of Denial of QoS Attacks on Diffserv Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) North Carolina State University,Department of Computer Networking,Raleigh,NC,27695 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES The original document contains color images. 14. ABSTRACT 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 94 19a. NAME OF RESPONSIBLE PERSON a. REPORT unclassified b. ABSTRACT unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 DETECTION OF DENIAL OF QoS ATTACKS ON DIFFSERV NETWORKS
منابع مشابه
DETECTION OF DENIAL OF QoS ATTACKS ON DIFFSERV NETWORKS
MAHADIK, VINAY ASHOK. Detection of Denial of QoS Attacks on DiffServ Networks. (Under the direction of Dr. Douglas S. Reeves.) In this work, we describe a method of detecting denial of Quality of Service (QoS) attacks on Differentiated Services (DiffServ) networks. Our approach focusses on real time and quick detection, scalability to large networks, and a negligible false alarm generation rate...
متن کاملHF-Blocker: Detection of Distributed Denial of Service Attacks Based On Botnets
Abstract—Today, botnets have become a serious threat to enterprise networks. By creation of network of bots, they launch several attacks, distributed denial of service attacks (DDoS) on networks is a sample of such attacks. Such attacks with the occupation of system resources, have proven to be an effective method of denying network services. Botnets that launch HTTP packet flood attacks agains...
متن کاملA Fuzzy Based Three Color Meter/Marker for Diffserv Networks (RESEARCH NOTE)
Differentiated Services (Diffserv) which was proposed by Internet Engineering Task Force (IETF), is a scalable and robust model for providing the end-to-end QoS. In the Diffserv networks, metering mechanisms are used to measure traffic stream. The single rate Three Color Meter (srTCM) [1],which was proposed by IETF, meters an IP packet stream and marks its packets either green, yellow, or red....
متن کاملA Lightweight Intrusion Detection System Based on Specifications to Improve Security in Wireless Sensor Networks
Due to the prevalence of Wireless Sensor Networks (WSNs) in the many mission-critical applications such as military areas, security has been considered as one of the essential parameters in Quality of Service (QoS), and Intrusion Detection System (IDS) is considered as a fundamental requirement for security in these networks. This paper presents a lightweight Intrusion Detection System to prote...
متن کاملDetection of Denial-of-QoS Attacks Based On χ Statistic And EWMA Control Charts
In this paper, we describe a method of detecting denial of Quality of Service attacks on DiffServ networks. Our approach focusses on real time and quick detection, scalability to large networks, and a negligible false alarm generation rate. Sensors sample QoS parameters like bit rate, packet dropping rate, and jitter of specific Virtual Leased Line (VLL) flows at predefined strategic points in ...
متن کامل