A Review on Attack Detection Using Machine Learning with Nature-inspired Optimization Techniques

Nature-inspired optimization techniques have been applied in various fields of study to solve optimization problems. Since designing a Fuzzy System (FS) can be considered one of the most complex optimization problems, many meta-heuristic optimizations have been developed to design FS structures. This paper aims to design a Takagi–Sugeno–Kang fuzzy Systems (TSK-FS) structure by generating the required fuzzy rules and selecting the most influential parameters for these rules. In this context, a new hybrid nature-inspired algorithm is proposed, namely Genetic–Grey Wolf Optimization (GGWO) algorithm, to optimize TSK-FSs. In GGWO, a hybridization of the genetic algorithm (GA) and the grey wolf optimizer (GWO) is applied to overcome the premature convergence and poor solution exploitation of the standard GWO. Using genetic crossover and mutation operators accelerates the exploration process and efficiently reaches the best solution (rule generation) within a reasonable time. The proposed GGWO is tested on several benchmark functions compared with other nature-inspired optimization algorithms. The result of simulations applied to the fuzzy control of nonlinear plants shows the superiority of GGWO in designing TSK-FSs with high accuracy compared with different optimization algorithms in terms of Root Mean Squared Error (RMSE) and computational time.

Web application security has become real concern due to increase in attacks and data breaches. As Application becomes critical, complex and connected, the difficulty of achieving application security increases exponentially. Also there are tools and techniques to detect such attacks, threat and vulnerabilities that exist in application which developer prevent and mitigate the risk associated to it. This paper evaluates various web application attack detection mechanisms and how resistant they are against various attacking techniques. Such an evaluation is important for not only measuring the available attack defense against web application attacks but also identifying gaps to build effective solutions for different defense techniques on web application and use it for study. Based on the research, the limitations of these application attack detection techniques are identified and remedies proposed for improving the current state attack detection on web applications.

In Current scenario, many Web applications go through rapid development phases like adopting agile methodology of development with very short turnaround time, making it difficult to identify and eliminate vulnerabilities. This paper provides analysis of requirement of Secure Development and Web application security assessment mechanisms in order to identify poor coding practices that render Web applications vulnerable to attacks such as SQL injection and cross-site scripting. This paper also list out the different categories of vulnerability with the small examples along with prevention guidance and a sample of research by different vendors about the State of Software Security Report and Web Application Vulnerability Statistics of 2012. This paper reviews need of secure development, resource s available for creating secure Web applications. These resources ranges from the security features of the development, to automated tools evaluating an existing Web application, to Web sites dedicated to all facets of Web application security. In Web application security, making one single mistake can lead to many unwanted flaws. By using the different resources available, the risk of the applications to be vulnerable can be reduced to an acceptable level. In addition, some risk can be avoided at the very beginning of the project life cycle when the requirements for the system are defined.

Strong security in web applications is critical to the success of your online presence. Security importance has grown massively, especially among web applications. Dealing with web application or website security issues requires deep insight and planning, not only because of the many tools that are available but also because of the industry immaturity. Thus, finding the proper tools requires deep understanding and several steps, including analyzing the development environment, business needs, and the web applications’ complexity. In this paper, we demonstrate the architecture of web applications then list and evaluate the widespread security vulnerabilities. Those vulnerabilities are: Fingerprinting, Insufficient Transport Layer Protection, Information Leakage, Cross-Site Scripting, SQL Injection, and HTTP Splitting. In addition, this paper analyzes the tools that are used to scan for these widespread vulnerabilities in web applications. Finally, it evaluates tools due to security vulnerabilities and gives recommendations to the web applications’ users and administrators aiming to educate them.

With the increasing reliance on web applications for business and personal use, ensuring website security has become a critical concern. Cyber threats such as SQL injection, cross-site scripting (XSS), malware infections, and unauthorized access pose significant risks to websites, leading to data breaches and service disruptions. This project aims to develop a comprehensive website security scanner that systematically identifies vulnerabilities and potential security risks.The proposed system integrates automated vulnerability scanning, penetration testing techniques, and real-time monitoring to detect security loopholes. Using machine learning and heuristic-based analysis, the scanner can identify malicious scripts, outdated software versions, weak authentication mechanisms, and misconfigured security policies. The system also performs network security assessments, analyzing potential DDoS (Distributed Denial-of-Service) attack risks and firewall configurations. The scanner generates detailed security reports, providing actionable insights and recommendations for website owners and administrators to mitigate risks effectively. Designed for continuous monitoring and proactive defense, the tool enhances cybersecurity resilience against evolving threats. This project contributes to web security advancements by offering an intelligent, automated, and scalable solution for safeguarding websites from cyberattacks. Keywords: Website Security | Vulnerability Scanner | Cyber Threats | SQL Injection | Cross-Site Scripting (XSS) | Penetration Testing | Machine Learning | Malware Detection | DDoS Protection | Authentication Security | Firewall Analysis | Web Application Security | Risk Assessment | Cybersecurity Resilience

Design architecture is the edifice that strengthens the functionalities as well as the security of web applications. In order to facilitate architectural security from the web application’s design phase itself, practitioners are now adopting the novel mechanism of security tactics. With the intent to conduct a research from the perspective of security tactics, the present study employs a hybrid multi-criteria decision-making approach named fuzzy analytic hierarchy process-technique for order preference by similarity ideal solution (AHP-TOPSIS) method for selecting and assessing multi-criteria decisions. The adopted methodology is a blend of fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity ideal solution (fuzzy TOPSIS). To establish the efficacy of this methodology, the results are obtained after the evaluation have been tested on fifteen different web application projects (Online Quiz competition, Entrance Test, and others) of the Babasaheb Bhimrao Ambedkar University, Lucknow, India. The tabulated outcomes demonstrate that the methodology of the Multi-Level Fuzzy Hybrid system is highly effective in providing accurate estimation for strengthening the security of web applications. The proposed study will help experts and developers in developing and managing security from any web application design phase for better accuracy and higher security.

The powerful Open Web Application Security Project (OWASP)-Zed Attack Proxy (ZAP) tool secures web applications with extensive security testing. Its main goal is to find and fix web application vulnerabilities before they can be exploited. The goal is to improve web application security using OWASP ZAP scans and inspections. Simulating SQL injection and cross-site scripting attacks using the tool reveals an application's security flaws. OWASP ZAP automates testing to protect sensitive data and web application integrity. The purpose is to protect online applications from attacks to reduce security breaches and ensure industry compliance. Modern online applications' security and dependability depend on OWASP ZAP's systematic vulnerability discovery and mitigation. By enhancing scan performance and providing actionable security information, the experimental results show that the OWASP ZAP helps safeguard online applications and reduce the danger of cyberattacks. The objective is to enhance its skills to provide comprehensive, prompt, dependable security evaluations across various situations.

In current information era, every small-scale industry, MNC's, Schools, and Colleges utilise Web Applications to promote their organizations and provide services to society. Web Applications have become a simple and vital medium for communicating with society. This leads to an increase in website utilization. Threats and cyber-attacks are increasing according to the rate of growth of Web Applications. Any organization must ensure the privacy and security of its users' data in order to maintain the company's and users' integrity. Web application vulnerabilities represent major security risks, making it critical to conduct extensive assessments and penetration testing to discover and remediate such flaws. The paper provides a complete overview of web application vulnerability assessment and penetration testing, emphasizing the need of proactive security measures in protecting sensitive data and preserving application integrity. This study aims to identify and categorize web application vulnerabilities (OWASPs) that may compromise the security of the application and result in user data breaches. The primary focus of this project is the security and privacy of user data. Integrating security practices into the software development life cycle through investigating real-world case studies and industry standards. Ultimately, our research adds to the growing body of knowledge in web application security, assisting organizations in creating resilient defenses against cyber threats.

In SQLIA, attacker injects an input in the query in order to change the structure of the query intended by the programmer and therefore, gain access to the data in the underlying database. Due to the significance of the stored data, web application's security against SQLIA is vital. In this paper we propose a tool that is capable of reporting the transformations needed to reinforce the security of a Java-based web application and its database against SQLIAs. This tool which is based on static analysis and runtime validation uses our new technique for detection and prevention of SQLIAs. In our technique user inputs in SQL queries are removed and some information is gathered in order to make the detection easier and faster at runtime. According to these information the tool reports the transformations needed and the location of the transformations in source code and therefore after applying the transformations the result would be a reinforced web application against SQLIAs.

As complexity inherent in web application is growing rapidly. Testing web applications with more sophisticated approaches is essentially needed. Several approaches for security testing are available, but only a few of them are appreciated in common IT industries and hence in practice. The paper recapitulates the current approaches, considering the limitations of real world applications. An effort has been made in the direction of bridging the gaps with the study of foremost web security concerns and the current web testing techniques, including their strengths and weaknesses. The paper highlights the security issues pertinent to web applications, along with actual practices in industries related to these issues. It also includes gap between practices and theories in the industry.

In the contemporary digital landscape, the security of web applications is a pressing concern. Cyber threats continue to evolve, challenging the resilience of existing defense mechanisms. This project introduces an innovative Web Application Firewall (WAF) strengthened by Neural Network (NN) technologies. The primary aim of this project is to enhance web application security by proactively identifying and thwarting malicious attacks. Leveraging deep learning techniques, the NN-based WAF analyzes incoming HTTP requests in real time, distinguishing between legitimate and potentially harmful traffic. Through meticulous data collection, feature engineering, and model training, the WAF exhibits robust and adaptive behavior. This project represents a pivotal advancement in web application security. By harnessing the power of neural networks, it provides real-time protection against a wide spectrum of cyber threats. The NN-based WAF adapts to the evolving threat landscape, making it an indispensable tool for safeguarding web applications in an era of dynamic and persistent security challenges.

In recent years, there has been a demand for Web applications with complex functions. In addition, most web applications efficiently manage data based on databases. While the key and critical dimension of developing these Web applications is analysis and design, most object-oriented analysis and design methods do not have a consistent view of the database. In addition, Java Enterprise Edition (EE) -based technologies are used in Web application implementations, but they do not provide any correlation with the database. On the other hand, as users' demands for security increase, security becomes more important. To this end, Java EE and database systems provide security solutions. However, it does not provide any correlation with object-oriented analysis and design methodology. As a result, it is difficult to develop secure web applications in a consistent way from analysis to implementation. In this paper, we propose a consistent software development methodology from analysis to implementation of secure web applications. The proposed software development methodology for web application development uses UMLsec, a security-emphasized modeling language, and object-relational (O-R) mapping for relational database design. It also uses Java servlets and SQL to implement analysis and design results based on role-based access control (RBAC). The software development methodology for the secure web application proposed in this paper has been applied to the development of the online banking system, from the design stage of the user's requirements analysis to the implementation of the web application.

The growing importance of cybersecurity in the digital age necessitates a comprehensive approach to securing web applications, particularly through robust front-end development practices. This review paper proposes a conceptual framework for integrating security best practices into front-end development to mitigate common vulnerabilities such as Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF). The framework emphasizes key components: threat modeling, secure coding practices, security automation, integration into Continuous Integration/Continuous Deployment (CI/CD) pipelines, and continuous monitoring. By embedding security into every stage of the development process, the framework enhances the security of web applications. It aligns with U.S. national interests in bolstering cybersecurity. The paper also discusses the implications of secure front-end development for national cybersecurity, highlighting the framework's potential to reduce the attack surface of critical web applications significantly. Recommendations are provided for policymakers and industry leaders to promote adopting secure front-end practices, ensuring a resilient digital infrastructure. Keywords: Secure Front-End Development, Cybersecurity, Web Application Security, Threat Modeling, Security Automation, National Cybersecurity.

Web application security has become a major requirement for any business, especially with the wide web attacks spreading despite the defensive measures and the continuous development of software frameworks and servers. In this study, we present a proposed model for a web application firewall that used machine learning and features engineering to detect common web attacks. Our proposed model analyses incoming requests to the webserver, parses these requests to extract four features that describe completely HTTP request parts (URL, payload, and headers), and classifies whether a request is normal or an anomaly. We took into consideration the limitation of previous works that use URL and payload only in classification and provided five features that describe and summarize all parts of the HTTP request using features engineering and previous experience in the field of the software security domain. Extracted features are length of request, percentage of characters allowed, percentage of special characters, and attack weight. These features were calculated for four different datasets CSIC 2010, HTTPParams 2015, Hybrid dataset (CSIC 2010 and HTTPParams), and real logs for the compromised web server. We evaluated our proposed model by using these updated datasets with four classification algorithms (Naive Bayes, logistic regression, decision tree, and support vector machine) with two methods (train test split and cross-validation) to negate the probability of overfitting and ensure that features are effective. Features values for a normal request are usually short request length, large allowed character ratio, small special character ratio, and zero attack weight or close to zero. Features values for anomaly requests are large request length, small allowed character percentage, large special character percentage, and very large numerically attack weight. Our proposed model achieved a classification accuracy of 99.6% with datasets used in research studies in this field and 98.8% with datasets of real web servers.

SQL injection attack (SQLIA) is considered one of most threats used to attack web applications. Therefore, attackers used SQL injection vulnerability to gain ultimate access to databases that belong to applications and expose their sensitive information. Thus, attackers use SQL injections vulnerability to manipulate data also it could be used to take full control of the target machine. Accordingly, several methods were proposed in the literature to address this vulnerability widely because of its importance and high impact on the security of web applications. Thus, we propose a model to detect and prevent SQL injection attacks, which uses runtime validation to detect the occurrence of such attacks, our proposed model is adaptable to any existing system, with no need to modify the client or server and either no need to know web application source code. Furthermore, the modification independence is done by adding additional middleware between client and server. Thus, any check process is done on this middleware, and it is represented as a proxy that can do sanitize the inputs for detecting and preventing SQLIA. Furthermore, our proposed model accuracy reaches 86:6% for detecting and preventing SQLIA.