Impact Analysis of HTTP and SYN Flood DDoS Attacks on Apache 2 and IIS 10.0 Web Servers

Nowadays, continuously accessing Internet services is vital for the most of people. However, due to Denial of Service (DoS) and its severe type ‘Distributed Denial of Service (DDoS), online services becomes unavailable to users in sometimes. Rather than DDoS is dangerous and has serious impact on the Internet consumers, there are multiple types of that attack such Slowrise, ping of death and UDP, ICMP, SYN flood, etc. In this paper, the effect of HTTP and SYN flood attack on the most recent and widely used web servers is studied and evaluated. Systematic performance analysis is performed on Internet Information Service 10.0 (IIS 10.0) on Windows server 2016 and Apache 2 on Linux Ubuntu 16.04 Long Term Support (LTS) server. Furthermore, the key metrics of the performance are average response time, average CPU usage and standard deviation as a responsiveness, efficiency and stability of the web servers. The results show that the IIS10.0 outperformed Apache2 web server in efficiency and responsiveness during HTTP flood attack. However, Apache2 web server achievement was more responsive and performed more stability with SYN flood attack.

[1]  Ping Guo,et al.  An experimental case study on the relationship between workload and resource consumption in a commercial web server , 2017, J. Comput. Sci..

[2]  Vijay Katkar,et al.  Detection of DoS/DDoS Attack against HTTP Servers Using Naive Bayesian , 2015, 2015 International Conference on Computing Communication Control and Automation.

[3]  John R. Vacca Computer and Information Security Handbook , 2009 .

[4]  Ioana Apostol,et al.  Analyzing websites protection mechanisms against DDoS attacks , 2017, 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI).

[5]  Abhinav Bhandari,et al.  Simulation study of application layer DDoS attack , 2015, 2015 International Conference on Green Computing and Internet of Things (ICGCIoT).

[6]  Sakir Sezer,et al.  HTTP/2 Cannon: Experimental analysis on HTTP/1 and HTTP/2 request flood DDoS attacks , 2017, 2017 Seventh International Conference on Emerging Security Technologies (EST).

[7]  Nazife Baykal,et al.  DDoS Attack Modeling and Detection Using SMO , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).

[8]  Xiapu Luo,et al.  Characterizing the Impacts of Application Layer DDoS Attacks , 2017, 2017 IEEE International Conference on Web Services (ICWS).

[9]  P. Varalakshmi,et al.  Performance Analysis of Virtual Machines and Docker Containers , 2017 .

[10]  Avinash Goud Chekkilla Monitoring and Analysis of CPU Utilization, Disk Throughput and Latency in servers running Cassandra database : An Experimental Investigation , 2017 .

[11]  S. Ramachandram,et al.  A survey on client side and server side approaches to secure web applications , 2017, 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA).

[12]  Fatwa Ramdani,et al.  Performance testing analysis on web application: Study case student admission web system , 2017, 2017 International Conference on Sustainable Information Engineering and Technology (SIET).

[13]  Truong Thu Huong,et al.  Self-organizing map-based approaches in DDoS flooding detection using SDN , 2018, 2018 International Conference on Information Networking (ICOIN).

[14]  Paramvir Singh,et al.  Impact analysis of application layer DDoS attacks on web services: a simulation study , 2017, Int. J. Intell. Eng. Informatics.

[15]  Petr Filip,et al.  Advanced web analytics tool for mouse tracking and real-time data processing , 2017, 2017 IEEE 14th International Scientific Conference on Informatics.

[16]  Sudhir T. Bagade,et al.  DoS attack mitigation using rule based and anomaly based techniques in software defined networking , 2017, 2017 International Conference on Inventive Computing and Informatics (ICICI).

[17]  Jamilson Dantas,et al.  Impact of a DDoS attack on computer systems: An approach based on an attack tree model , 2018, 2018 Annual IEEE International Systems Conference (SysCon).

[18]  Amanullah Yasin,et al.  DDoS attacks analysis in bigdata (hadoop) environment , 2018, 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST).

[19]  Fouzi Harrou,et al.  Detecting SYN flood attacks via statistical monitoring charts: A comparative study , 2017, 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B).

[20]  Rajkumar,et al.  A Survey on Latest DoS Attacks:Classificationand Defense Mechanisms , 2013 .

[21]  Endroyono,et al.  The impact analysis and mitigation of DDoS attack on local government electronic procurement service (LPSE) , 2016, 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA).

[22]  Vinod Kumar,et al.  Classification of DDoS attack tools and its handling techniques and strategy at application layer , 2016, 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall).

[23]  Indraneel Sreeram,et al.  HTTP flood attack detection in application layer using machine learning metrics and bio inspired bat algorithm , 2019, Applied Computing and Informatics.