What is it about?
This paper provides an extensive overview of the current methods used to detect and investigate attacks on web applications. It discusses various techniques and tools employed to identify these attacks, including firewalls, intrusion detection systems, and honeypots. The paper also explores how data mining and machine learning can be applied to enhance the detection and forensic analysis of web attacks. By examining these technologies, the authors aim to offer insights into more effective ways to safeguard web applications against both known and unknown threats.
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Why is it important?
The importance of this study lies in its comprehensive examination of web application security, a critical area in today's digital world where web applications are widely used for e-commerce, social networking, and various online services. The paper addresses the increasing sophistication of web attacks and the limitations of traditional detection methods, proposing advanced techniques like data mining and machine learning to improve security measures. This research is crucial for developing more robust defenses against cyber threats and ensuring the integrity and reliability of web applications, which are essential for both businesses and individuals.
Perspectives
- The study emphasizes the integration of multiple detection techniques to enhance overall security. - It highlights the evolving nature of web attacks and the necessity for adaptive and intelligent security solutions. - The paper also discusses the challenges faced in forensic analysis of web attacks due to the massive volume of data generated, proposing advanced data mining techniques to address these challenges.
Dr. Enis Karaarslan
Mugla Sitki Kocman Universitesi
Read the Original
This page is a summary of: Web application attack detection and forensics: A survey, March 2018, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/isdfs.2018.8355378.
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