Security Analysis and Improvement Model for Web-based Applications

Today the web has become a major conduit for information. As the World Wide Web?s popularity continues to increase, information security on the web has become an increasing concern. Web information security is related to availability, confidentiality, and data integrity. According to the reports from http://www.securityfocus.com in May 2006, operating systems account for 9% vulnerability, web-based software systems account for 61% vulnerability, and other applications account for 30% vulnerability. In this dissertation, I present a security analysis model using the Markov Process Model. Risk analysis is conducted using fuzzy logic method and information entropy theory. In a web-based application system, security risk is most related to the current states in software systems and hardware systems, and independent of web application system states in the past. Therefore, the web-based applications can be approximately modeled by the Markov Process Model. The web-based applications can be conceptually expressed in the discrete states of (web_client_good; web_server_good, web_server_vulnerable, web_server_attacked, web_server_security_failed; database_server_good, database_server_vulnerable, database_server_attacked, database_server_security_failed) as state space in the Markov Chain. The vulnerable behavior and system response in the web-based applications are analyzed in this dissertation. The analyses focus on functional availability-related aspects: the probability of reaching a particular security failed state and the mean time to the security failure of a system. Vulnerability risk index is classified in three levels as an indicator of the level of security (low level, high level, and failed level). An illustrative application example is provided. As the second objective of this dissertation, I propose a security improvement model for the web-based applications using the GeoIP services in the formal methods. In the security improvement model, web access is authenticated in role-based access control using user logins, remote IP addresses, and physical locations as subject credentials to combine with the requested objects and privilege modes. Access control algorithms are developed for subjects, objects, and access privileges. A secure implementation architecture is presented. In summary, the dissertation has developed security analysis and improvement model for the web-based application. Future work will address Markov Process Model validation when security data collection becomes easy. Security improvement model will be evaluated in performance aspect

Security Analysis and Improvement Model for Web-based Applications

Today the web has become a major conduit for information. As the World Wide Web?s popularity continues to increase, information security on the web has become an increasing concern. Web information security is related to availability, confidentiality, and data integrity. According to the reports from http://www.securityfocus.com in May 2006, operating systems account for 9% vulnerability, web-based software systems account for 61% vulnerability, and other applications account for 30% vulnerability. In this dissertation, I present a security analysis model using the Markov Process Model. Risk analysis is conducted using fuzzy logic method and information entropy theory. In a web-based application system, security risk is most related to the current states in software systems and hardware systems, and independent of web application system states in the past. Therefore, the web-based applications can be approximately modeled by the Markov Process Model. The web-based applications can be conceptually expressed in the discrete states of (web_client_good; web_server_good, web_server_vulnerable, web_server_attacked, web_server_security_failed; database_server_good, database_server_vulnerable, database_server_attacked, database_server_security_failed) as state space in the Markov Chain. The vulnerable behavior and system response in the web-based applications are analyzed in this dissertation. The analyses focus on functional availability-related aspects: the probability of reaching a particular security failed state and the mean time to the security failure of a system. Vulnerability risk index is classified in three levels as an indicator of the level of security (low level, high level, and failed level). An illustrative application example is provided. As the second objective of this dissertation, I propose a security improvement model for the web-based applications using the GeoIP services in the formal methods. In the security improvement model, web access is authenticated in role-based access control using user logins, remote IP addresses, and physical locations as subject credentials to combine with the requested objects and privilege modes. Access control algorithms are developed for subjects, objects, and access privileges. A secure implementation architecture is presented. In summary, the dissertation has developed security analysis and improvement model for the web-based application. Future work will address Markov Process Model validation when security data collection becomes easy. Security improvement model will be evaluated in performance aspect

Mitigating Cross-Site Request Forgery (CSRF) Attacks Using Reinforcement Learning and Predictive Analytics

Cross-Site Request Forgery (CSRF) attacks pose a significant threat to web application security, allowing attackers to perform unauthorized actions on behalf of authenticated users. Traditional CSRF mitigation techniques, such as using secure tokens and validating request origins, have limitations in adapting to attack patterns and optimizing security policies. This research explores the application of reinforcement learning (RL) and predictive analytics to enhance CSRF mitigation strategies. We propose several RL-based approaches, including CSRF token generation, CSRF detection, request validation, user behavior analysis, and security policy optimization. In these approaches, RL agents are trained to generate secure tokens, detect CSRF attacks, validate request authenticity, model user behavior, and optimize security policies based on observed attack patterns and system performance. The agents learn through simulated attack scenarios, real-world web traffic data, and continuous feedback, adapting to new CSRF techniques and balancing security effectiveness with user experience. Additionally, we investigate predictive analytics techniques for CSRF mitigation, such as anomaly detection, risk scoring, user behavior analysis, predictive token generation, and adaptive security policies. These techniques leverage machine learning algorithms to identify anomalous requests, assign risk scores, classify user behavior, generate secure tokens, and dynamically adjust security measures based on predicted risk levels. The research demonstrates the applications of RL and predictive analytics in enhancing CSRF mitigation strategies. These approaches offer promising solutions to strengthen web application security by proactively detecting and preventing CSRF attacks, adapting to attack patterns, and optimizing security policies. Further research is needed to validate the practicality and scalability of these techniques in real-world deployments and to integrate them with existing CSRF mitigation best practices. This research contributes to the field of web application security by introducing innovative approaches that leverage RL and predictive analytics to mitigate CSRF attacks. The proposed techniques may significantly improve the resilience of web applications against CSRF threats

Effective Detection of Vulnerable and Malicious Browser Extensions

Unsafely coded browser extensions can compromise the security of a browser, making them attractive targets for attackers as a primary vehicle for conducting cyber-attacks. Among others, the three factors making vulnerable extensions a high-risk security threat for browsers include: i) the wide popularity of browser extensions, ii) the similarity of browser extensions with web applications, and iii) the high privilege of browser extension scripts. Furthermore, mechanisms that specifically target to mitigate browser extension-related attacks have received less attention as opposed to solutions that have been deployed for common web security problems (such as SQL injection, XSS, logic flaws, client-side vulnerabilities, drive-by-download, etc.). To address these challenges, recently some techniques have been proposed to defend extension-related attacks. These techniques mainly focus on information flow analysis to capture suspicious data flows, impose privilege restriction on API calls by malicious extensions, apply digital signatures to monitor process and memory level activities, and allow browser users to specify policies in order to restrict the operations of extensions. This article presents a model-based approach to detect vulnerable and malicious browser extensions by widening and complementing the existing techniques. We observe and utilize various common and distinguishing characteristics of benign, vulnerable, and malicious browser extensions. These characteristics are then used to build our detection models, which are based on the Hidden Markov Model constructs. The models are well trained using a set of features extracted from a number of browser extensions together with user supplied specifications. Along the course of this study, one of the main challenges we encountered was the lack of vulnerable and malicious extension samples. To address this issue, based on our previous knowledge on testing web applications and heuristics obtained from available vulnerable and malicious extensions, we have defined rules to generate training samples. The approach is implemented in a prototype tool and evaluated using a number of Mozilla Firefox extensions. Our evaluation indicated that the approach not only detects known vulnerable and malicious extensions, but also identifies previously undetected extensions with a negligible performance overhead

The Transitivity of Trust Problem in the Interaction of Android Applications

Mobile phones have developed into complex platforms with large numbers of installed applications and a wide range of sensitive data. Application security policies limit the permissions of each installed application. As applications may interact, restricting single applications may create a false sense of security for the end users while data may still leave the mobile phone through other applications. Instead, the information flow needs to be policed for the composite system of applications in a transparent and usable manner. In this paper, we propose to employ static analysis based on the software architecture and focused data flow analysis to scalably detect information flows between components. Specifically, we aim to reveal transitivity of trust problems in multi-component mobile platforms. We demonstrate the feasibility of our approach with Android applications, although the generalization of the analysis to similar composition-based architectures, such as Service-oriented Architecture, can also be explored in the future

Building in web application security at the requirements stage : a tool for visualizing and evaluating security trade-offs : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Science in Information Systems at Massey University, Albany, New Zealand

One dimension of Internet security is web application security. The purpose of this Design-science study was to design, build and evaluate a computer-based tool to support security vulnerability and risk assessment in the early stages of web application design. The tool facilitates risk assessment by managers and helps developers to model security requirements using an interactive tree diagram. The tool calculates residual risk for each component of a web application and for the application overall so developers are provided with better information for making decisions about which countermeasures to implement given limited resources tor doing so. The tool supports taking a proactive approach to building in web application security at the requirements stage as opposed to the more common reactive approach of putting countermeasures in place after an attack and loss have been incurred. The primary contribution of the proposed tool is its ability to make known security-related information (e.g. known vulnerabilities, attacks and countermeasures) more accessible to developers who are not security experts and to translate lack of security measures into an understandable measure of relative residual risk. The latter is useful for managers who need to prioritize security spending. Keywords: web application security, security requirements modelling, attack trees, threat trees, risk assessment

AndroShield:automated Android applications vulnerability detection, a hybrid static and dynamic analysis approach

The security of mobile applications has become a major research field which is associated with a lot of challenges. The high rate of developing mobile applications has resulted in less secure applications. This is due to what is called the “rush to release” as defined by Ponemon Institute. Security testing—which is considered one of the main phases of the development life cycle—is either not performed or given minimal time; hence, there is a need for security testing automation. One of the techniques used is Automated Vulnerability Detection. Vulnerability detection is one of the security tests that aims at pinpointing potential security leaks. Fixing those leaks results in protecting smart-phones and tablet mobile device users against attacks. This paper focuses on building a hybrid approach of static and dynamic analysis for detecting the vulnerabilities of Android applications. This approach is capsuled in a usable platform (web application) to make it easy to use for both public users and professional developers. Static analysis, on one hand, performs code analysis. It does not require running the application to detect vulnerabilities. Dynamic analysis, on the other hand, detects the vulnerabilities that are dependent on the run-time behaviour of the application and cannot be detected using static analysis. The model is evaluated against different applications with different security vulnerabilities. Compared with other detection platforms, our model detects information leaks as well as insecure network requests alongside other commonly detected flaws that harm users’ privacy. The code is available through a GitHub repository for public contribution