| Title | Towards a Stochastic Model for Integrated Detection and Filtering of DoS Attacks in Cloud Environments |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | El Mir, Iman, Kim, Dong Seong, Haqiq, Abdelkrim |
| Conference Name | Proceedings of the 2Nd International Conference on Big Data, Cloud and Applications |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4852-2 |
| Keywords | cloud computing, DoS attacks, Performance modeling, pubcrawl, queueing theory, resilience, Resiliency, Scalability, security, Stochastic computing, Stochastic Computing Security |
| Abstract | Cloud Data Center (CDC) security remains a major challenge for business organizations and takes an important concern with research works. The attacker purpose is to guarantee the service unavailability and maximize the financial loss costs. As a result, Distributed Denial of Service (DDoS) attacks have appeared as the most popular attack. The main aim of such attacks is to saturate and overload the system network through a massive data packets size flooding toward a victim server and to block the service to users. This paper provides a defending system in order to mitigate the Denial of Service (DoS) attack in CDC environment. Basically it outlines the different techniques of DoS attacks and its countermeasures by combining the filtering and detection mechanisms. We presented an analytical model based on queueing model to evaluate the impact of flooding attack on cloud environment regarding service availability and QoS performance. Consequently, we have plotted the response time, throughput, drop rate and resource computing utilization varying the attack arrival rate. We have used JMT (Java Modeling Tool) simulator to validate the analytical model. Our approach was appeared powerful for attacks mitigation in the cloud environment. |
| URL | http://doi.acm.org/10.1145/3090354.3090383 |
| DOI | 10.1145/3090354.3090383 |
| Citation Key | el_mir_towards_2017 |
Towards a Stochastic Model for Integrated Detection and Filtering of DoS Attacks in Cloud Environments