Annotated Bibliography on Effective Firewalls
Manaseer, S., & Al Hwaitat, A. (2018). Centralized Web Application Firewall Security System. Modern Applied Science, 12(10), 164. doi: 10.5539/mas.v12n10p164
In this article, the authors propose a centralized web firewall system for web application security, which enhances a new method of the synchronized system. The system can detect and hinder many web application attacks for a wide range of hosts within a given time. They use a centralized command and control system, attacked the customer and then send the information to a centralized control server and command centre which in effects distributes the attack information to all of the integrated clients connected to it. The information distributed has all of the information from the attackers, including the IP address of the attacking criminals, the kind of an attack, and time of the attack. The procedure of information received from the attackers and the distribution of this information through a centralized web firewall is done automatically and as soon as the attack is carried out. All the customers receiving the information will take actions against the threat depending on the distributed data sent to them. These include banning the IP address of the attacking entity with the main focus being client protection from the same kind of attack or attacking entity. This article is significant since it focuses on the protection of multiple attacks from the same type of attack or entity
Moradi Vartouni, A., Teshnehlab, M., & Sedighian Kashi, S. (2019). Leveraging deep neural networks for anomaly-based web application firewall. IET Information Security. doi: 10.1049/iet-ifs.2018.5404
According to this article, web applications are one of the most common platforms for the exchange of information and services on the internet. The authors argue that with the introduction of web 2.0, information was able to flourish via business online and social networking platforms. So, according to the authors, these networking websites were in most cases scenarios attacked directly, and in effects, the industry had to take a close look on the security of the web applications in addition to the security under computing networks. Intelligent systems based on machine learning have shown excellent results on tasks such as anomaly detection in web requests. However, the present ways are based on traditional approaches can't extract high-level characteristics from big data. The article has designed a proposed method based on the deep neural system and a parallel characterized fusion that features engineering as an important factor on them and plays a key role in their performance. The approach proposed uses stacked autoencoder and deep belief network as a featuring learning method, which only normal data is applied in the classification of the training phase. Also, elliptic envelope, one class SVM, and Isolation forest are applied as classifiers. The article is very vital since it provides insight into web applications and especially on the concept of web 2.0. It will be advantageous when writing the final project since the insight provided are very applicable in our lives today
Han, D., Liu, Q., & Fan, W., (2018). A new image classification method using CNN transfer learning and web data augmentation. Expert Systems With Applications, 95, 43-56. doi: 10.1016/j.eswa.2017.11.028
The authors of this article argue that with the increasing information sharing and other activities on the Web, the Web has been the major target for the attackers to cause troubles. The most optimal method to detect Web attacks is critical and significant to guarantee Web security. Recently, many machine learning method has been applied to detect Web attacks. It presents a method of deep learning that enables the detection of web attacks by use of a specially designed CNN. It is based on analyzing the HTTP packets requested, to which only some preprocessing is required while the cumbersome feature extraction is carried out by the CNN itself. This article will be applicable in designing an effective firewall that enhances the security of the information. The insight provided will be highly relied on.
References
Survey on Fast and Intelligent Deep Web Crawler Using Machine Learning Approach. (2015). International Journal Of Science And Research (IJSR), 4(11), 2250-2253. doi: 10.21275/v4i11.nov151713
Manaseer, S., & Al Hwaitat, A. (2018). Centralized Web Application Firewall Security System. Modern Applied Science, 12(10), 164. doi: 10.5539/mas.v12n10p164
Prema Sindhuri, B., & Kameswara Rao, M. (2018). IoT security through web application firewall. International Journal Of Engineering & Technology, 7(2.7), 58. doi: 10.14419/ijet.v7i2.7.10259
Surekha, M., Kiran Kumar, K., V.S.Prasanth, M., & S.G.Aruna Sri, P. (2018). Web application firewall using XSS. International Journal Of Engineering & Technology, 7(2.7), 941. doi: 10.14419/ijet.v7i2.7.11429
Cho, S., Choi, S., & ., .. (2018). A Study on Comparison of Network Location Efficiency of Web Application Firewall. International Journal Of Engineering & Technology, 7(3.33), 183. doi: 10.14419/ijet.v7i3.33.21009
Yan, R., Xiao, X., Hu, G., Peng, S., & Jiang, Y. (2018). New deep learning method to detect code injection attacks on hybrid applications. Journal Of Systems And Software, 137, 67-77. doi: 10.1016/j.jss.2017.11.001
Robinson, Akbar, M., & Fadhly Ridha, M. (2018). SQL Injection and Cross Site Scripting Prevention using OWASP ModSecurity Web Application Firewall. JOIV : International Journal On Informatics Visualization, 2(4), 286. doi: 10.30630/joiv.2.4.107
Kyalo, F., Otieno, C., & Njagi, D. (2018). Securing Web Applications against Structured Query Language Injection Attacks using a Hybrid Approach: Input Filtering and Web Application Firewall. International Journal Of Computer Applications, 182(9), 20-27. doi: 10.5120/ijca2018917666
Sujarwo, A., & Tan, J. (2018). Enterprise firewall virtualization design. MATEC Web Of Conferences, 154, 03004. doi: 10.1051/matecconf/201815403004
Salih, N., & Samad, A. (2016). Protection Web Applications using Real-Time Technique to Detect Structured Query Language Injection Attacks. International Journal Of Computer Applications, 149(6), 26-32. doi: 10.5120/ijca2016911424
Zhao, J., Mao, X., & Chen, L. (2018). Learning deep features to recognise speech emotion using merged deep CNN. IET Signal Processing, 12(6), 713-721. doi: 10.1049/iet-spr.2017.0320
Chora, M., & Kozik, R. (2014). Machine learning techniques applied to detect cyber attacks on web applications. Logic Journal Of IGPL, 23(1), 45-56. doi: 10.1093/jigpal/jzu038
A method for detecting man-in-the-middle attacks using time synchronization one time password in interlock protocol based internet of things. (2016). Journal Of Applied And Physical Sciences, 2(2). doi: 10.20474/japs-2.2.2
Kyalo, F., Otieno, C., & Njagi, D. (2018). Securing Web Applications against Structured Query Language Injection Attacks using a Hybrid Approach: Input Filtering and Web Application Firewall. International Journal Of Computer Applications, 182(9), 20-27. doi: 10.5120/ijca2018917666