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Title |
A Cost-Effective Solution to Detect DDoS Attacks using Big Data Analytics |
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Author |
G. Dileep Kumar, Dr. CV. Guru Rao |
| Citation |
Vol. 26 No. 6 pp. 31-36 |
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Abstract |
Attacks are increasing in volume, complexity and damage. Internet traffic measurement and Big Data analysis have been a challenging job because large packet trace files captured on fast links could not be easily handled on a single server with limited computing resources. While this pace of traffic and data generation is very exciting, it has created entirely new set of challenges and has forced to find new ways to handle. A DDoS attack is a large-scale, coordinated attack on the availability of services of a victim system or network resource, launched indirectly through many compromised computers on the Internet. However, since the attacker controls a large set of computers to make requests, the number and the size of network logs are also extreme large, so if the old network log analysis method is used for these large network logs, it will take a substantial amount of time to complete. As a result, utilizing Big Data technology is really necessary for this type of task. The study propose a Big Data based attacks detection framework which uses the low-cost commodity hardware, scalable and distributed open source framework for implementation. |
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Keywords |
DDoS Attack, Big Data, Hadoop, MapReduce, Detection |
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