Browse > Article
http://dx.doi.org/10.33851/JMIS.2020.7.2.125

An Attack-based Filtering Scheme for Slow Rate Denial-of-Service Attack Detection in Cloud Environment  

Gutierrez, Janitza Nicole Punto (Dept. of Computer Science and Engineering, Seoul National University of Science and Technology)
Lee, Kilhung (Dept. of Computer Science and Engineering, Seoul National University of Science and Technology)
Publication Information
Journal of Multimedia Information System / v.7, no.2, 2020 , pp. 125-136 More about this Journal
Abstract
Nowadays, cloud computing is becoming more popular among companies. However, the characteristics of cloud computing such as a virtualized environment, constantly changing, possible to modify easily and multi-tenancy with a distributed nature, it is difficult to perform attack detection with traditional tools. This work proposes a solution which aims to collect traffic packets data by using Flume and filter them with Spark Streaming so it is possible to only consider suspicious data related to HTTP Slow Rate Denial-of-Service attacks and reduce the data that will be stored in Hadoop Distributed File System for analysis with the FP-Growth algorithm. With the proposed system, we also aim to address the difficulties in attack detection in cloud environment, facilitating the data collection, reducing detection time and enabling an almost real-time attack detection.
Keywords
Cloud computing; Denial-of-Service Attack; Slow Rate DoS Attack;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Flume User Guide, https://flume.apache.org/FlumeUserGuide.html, 2018.
2 Fortinet FortiDDoS: Protection Profile Settings, https://help.fortinet.com/fddos/4-3-0/FortiDDoS/Managing_thresholds.htm, 2019.
3 B.B. Gupta, and O.P. Badve, "Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a Cloud computing environment," Neural Computing and Applications, vol. 28, Apr. 2016.
4 K. Bhushana, and B. B. Gupta, "Hypothesis Test for Low-rate DDoS Attack Detection in Cloud Computing Environment," Procedia Computer Science, vol. 132, pp. 947-955, May 2018.   DOI
5 T. Tamanna, T. Fatema, and R. Saha, "SDN, A research on SDN assets and tools to defense DDoS attack in cloud computing environment," in 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, pp. 1670-1674, 2017.
6 Cloud Security Alliance, "The Treacherous 12-Top Threats to Cloud Computing + Industry Insights"; https://downloads.cloudsecurityalliance.org/assets/research/top-threats/treacherous-12-top-threats.pdf, 2017.
7 VeriSign Distributed Denial of Service Trends Report (Q1 2018), https://www.a10networks.com/sites/default/files/A10-TPS-EB-Verisign_Distributed_Denial_of_Service_Trends_Report.pdf, 2018.
8 M. Idhammad, K. Afdel, and M. Belouch, "Detection System of HTTP DDoS Attacks in a Cloud Environment Based on Information Theoretic Entropy and Random Forest," Security and Communication Networks, vol. 2018, Article ID 1263123, 13 pages, 2018.
9 O. Osanaiye, Kim-Kwang R. Choo, and M. Dlodlo, "Distributed denial of service (DDoS) resilience in cloud: Review and conceptual cloud DDoS mitigation framework," Journal of Network and Computer Applications, vol. 67, pp. 147-165, 2016.   DOI
10 P. Sharma, R. Sharma, E. S. Pilli, and A. K. Mishra, "A Detection Algorithm for DoS Attack in the Cloud Environment," in Proceedings of the 8th Annual ACM India Conference (Compute '15), New York, pp. 107-110, 2015.
11 L. Gao, Y. Li, L. Zhang, F. Lin, and M. Ma, "Research on Detection and Defense Mechanisms of DoS Attacks Based on BP Neural Network and Game Theory," IEEE Access, vol. 7, pp. 43018-43030, 2019.   DOI
12 Spark Streaming Programming Guide, https://spark.apache.org/docs/latest/streaming-programming-guide.html, 2018.
13 V. Shah, and A. K. Aggarwal, "Heterogeneous Fusion of IDS Alerts for Detecting DOS Attacks," in Proceedings of International Conference on Computing Communication Control and Automation, Pune, pp. 153-158, 2015.
14 N Hoque, D. K. Bhattacharyya, and J. K. Kalita, "Denial of Service Attack Detection using Multivariate Correlation Analysis," in Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies (ICTCS '16), New York, 2016.
15 N. A. Singh, K. J. Singh, and T. De, "Distributed denial of service attack detection using Naive Bayes Classifier through Info Gain Feature Selection," in Proceedings of the International Conference on Informatics and Analytics (ICIA-16), New York, 2016.
16 M. Khandelwal, D. K. Gupta, and P. Bhale, "DoS attack detection technique using back propagation neural network," in Proceedings of International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, pp. 1064-1068, 2016.
17 J. Brynielsson, and R. Sharma, "Detectability of low-rate HTTP server DoS attacks using spectral analysis," in Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Paris, pp. 954-961 2015.
18 A. Ahmad, M. N. Kama, O. M. Yusop, N. A. A. Bakar, and N. B. Idris, "Cloud denial of service detection by dendritic cell mechanism," in Proceedings of 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, pp. 179-184, 2018.
19 X. Wu, D. Tang, L. Tang, J. Man, S. Zhan, and Q. Liu, "A Low-Rate DoS Attack Detection Method Based on Hilbert Spectrum and Correlation," in Proceedings of IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/S CI), Guangzhou, pp. 1358-1363, 2018.
20 R. Kumar, S. P. Lal, and A. Sharma, "Detecting Denial of Service Attacks in the Cloud," in Proceedings of IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), Auckland, pp. 309-316, 2016.
21 S. S. Vernekar, and A. Buchade, "MapReduce based log file analysis for system threats and problem identification," in Proceedings of the 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, pp. 831-835, 2013.
22 J. Therdphapiyanak, and K. Piromsopa. "Applying Hadoop for log analysis toward distributed IDS," in Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication (ICUIMC '13), New York, 2013.
23 M. A. Latib, S. A. Ismail, O. M. Yusop, P. Magalingam, and A. Azmi, "Analysing Log Files for Web Intrusion Investigation Using Hadoop," in Proceedings of the 7th International Conference on Software and Information Engineering (ICSIE '18), New York, pp. 12-21, 2018.
24 N. Z. Bawany, J. A. Shamsi, and K. Salah, "DDoS Attack Detection and Mitigation Using SDN: Methods, Practices, and Solutions," Arabian Journal for Science and Engineering, vol. 42, no. 2, pp. 425-441, Feb. 2017.   DOI
25 J. Zhang, P. Liu, J. He, and Y. Zhang, "A Hadoop Based Analysis and Detection Model for IP Spoofing Typed DDoS Attack," in Proceedings of 2016 IEEE Trustcom/BigDataSE/ISPA, Tianjin, pp. 1976-1983, 2016.
26 A. Alsirhani, S. Sampalli, and P. Bodorik, "DDoS Attack Detection System: Utilizing Classification Algorithms with Apache Spark," in Proceedings of the 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Paris, pp. 1-7, 2018.
27 R. More, A. Unakal, V. Kulkarni, and R. H. Goudar, "Real time threat detection system in cloud using big data analytics," in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, pp. 1262-1264, 2017.
28 M. Idhammad, K. Afdel, and M. Belouch, "Distributed Intrusion Detection System for Cloud Environments based on Data Mining techniques," Procedia Computer Science, vol. 127, pp. 35-41, 2018.   DOI
29 S. Alzahrani, and L. Hong, "Detection of Distributed Denial of Service (DDoS) Attacks Using Artificial Intelligence on Cloud," in Proceedings of 2018 IEEE World Congress on Services (SERVICES), San Francisco, pp. 35-36, 2018.
30 P. Mell, and T. Grance, "The NIST definition of cloud computing," in National Institute of Standards and Technology, Gaithersburg, pp. 1-7, 2011.
31 E. Morioka and M. S. Sharbaf, "Digital forensics research on cloud computing: An investigation of cloud forensics solutions," in Proceedings of 2016 IEEE Symposium on Technologies for Homeland Security (HST), Waltham, pp. 1-6, 2016.
32 G. Sibiya, H. S. Venter and T. Fogwill, "Digital forensics in the Cloud: The state of the art," in Proceedings of 2015 IST-Africa Conference, Lilongwe, pp. 1-9, 2015.
33 L. Coppolino, S. D'Antonio, G. Mazzeo, and L. Romano, "Cloud security: Emerging threats and current solutions," Computers & Electrical Engineering, vol. 59, pp. 126-140, 2017.   DOI
34 S. Basu et al., "Cloud computing security challenges & solutions-A survey," in Proceedings of 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, pp. 347-356, 2018.
35 A. Odebade, T. Welsh, S. Mthunzi and E. Benkhelifa, "Mitigating anti-forensics in the Cloud via resourcebased privacy preserving activity attribution," in Proceedings of 2017 Fourth International Conference on Software Defined Systems (SDS), Valencia, pp. 143-149. 2017.
36 H. Arshad, A. B. Jantan, and O. I. Abiodun, "Digital Forensics: Review of Issues in Scientific Validation of Digital Evidence," Journal of Information Processing Systems, vol. 14, no. 2, pp. 346-376, 2018.   DOI
37 K. K. R. Choo, C. Esposito and A. Castiglione, "Evidence and Forensics in the Cloud: Challenges and Future Research Directions," in Proceedings of IEEE Cloud Computing, vol. 4, no. 3, pp. 14-19, 2017.
38 S. Zawoad and R. Hasan, "Trustworthy Digital Forensics in the Cloud," Computer, vol. 49, no. 3, pp. 78-81, Mar. 2016.   DOI
39 S. Nanda and R. A. Hansen, "Forensics as a Service: Three-Tier Architecture for Cloud Based Forensic Analysis," in Proceedings of the 15th International Symposium on Parallel and Distributed Computing (ISPDC), Fuzhou, pp. 178-183, 2016.
40 S. T. Zargar, J. Joshi and D. Tipper, "A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks," in Proceedings of IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 2046-2069, 2013.
41 M. H. Bhuyan, D.K. Bhattacharyya, and J.K. Kalita, "An empirical evaluation of information metrics for low-rate and high-rate DDoS attack detection," Pattern Recognition Letters, vol. 51, pp. 1-7, 2015.   DOI
42 Apache Hadoop, https://hadoop.apache.org/, 2018.
43 Hadoop Distributed File System, https://hadoop.apache.org/docs/r1.2.1/hdfs_design.html, 2018.