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Gupta, M.K., Govil, M.C., Singh, G..  2014.  A context-sensitive approach for precise detection of cross-site scripting vulnerabilities. Innovations in Information Technology (INNOVATIONS), 2014 10th International Conference on. :7-12.

Currently, dependence on web applications is increasing rapidly for social communication, health services, financial transactions and many other purposes. Unfortunately, the presence of cross-site scripting vulnerabilities in these applications allows malicious user to steals sensitive information, install malware, and performs various malicious operations. Researchers proposed various approaches and developed tools to detect XSS vulnerability from source code of web applications. However, existing approaches and tools are not free from false positive and false negative results. In this paper, we propose a taint analysis and defensive programming based HTML context-sensitive approach for precise detection of XSS vulnerability from source code of PHP web applications. It also provides automatic suggestions to improve the vulnerable source code. Preliminary experiments and results on test subjects show that proposed approach is more efficient than existing ones.

Gupta, M.K., Govil, M.C., Singh, G..  2014.  Static analysis approaches to detect SQL injection and cross site scripting vulnerabilities in web applications: A survey. Recent Advances and Innovations in Engineering (ICRAIE), 2014. :1-5.

Dependence on web applications is increasing very rapidly in recent time for social communications, health problem, financial transaction and many other purposes. Unfortunately, presence of security weaknesses in web applications allows malicious user's to exploit various security vulnerabilities and become the reason of their failure. Currently, SQL Injection (SQLI) and Cross-Site Scripting (XSS) vulnerabilities are most dangerous security vulnerabilities exploited in various popular web applications i.e. eBay, Google, Facebook, Twitter etc. Research on defensive programming, vulnerability detection and attack prevention techniques has been quite intensive in the past decade. Defensive programming is a set of coding guidelines to develop secure applications. But, mostly developers do not follow security guidelines and repeat same type of programming mistakes in their code. Attack prevention techniques protect the applications from attack during their execution in actual environment. The difficulties associated with accurate detection of SQLI and XSS vulnerabilities in coding phase of software development life cycle. This paper proposes a classification of software security approaches used to develop secure software in various phase of software development life cycle. It also presents a survey of static analysis based approaches to detect SQL Injection and cross-site scripting vulnerabilities in source code of web applications. The aim of these approaches is to identify the weaknesses in source code before their exploitation in actual environment. This paper would help researchers to note down future direction for securing legacy web applications in early phases of software development life cycle.

Yuxi Liu, Hatzinakos, D..  2014.  Earprint: Transient Evoked Otoacoustic Emission for Biometrics. Information Forensics and Security, IEEE Transactions on. 9:2291-2301.

Biometrics is attracting increasing attention in privacy and security concerned issues, such as access control and remote financial transaction. However, advanced forgery and spoofing techniques are threatening the reliability of conventional biometric modalities. This has been motivating our investigation of a novel yet promising modality transient evoked otoacoustic emission (TEOAE), which is an acoustic response generated from cochlea after a click stimulus. Unlike conventional modalities that are easily accessible or captured, TEOAE is naturally immune to replay and falsification attacks as a physiological outcome from human auditory system. In this paper, we resort to wavelet analysis to derive the time-frequency representation of such nonstationary signal, which reveals individual uniqueness and long-term reproducibility. A machine learning technique linear discriminant analysis is subsequently utilized to reduce intrasubject variability and further capture intersubject differentiation features. Considering practical application, we also introduce a complete framework of the biometric system in both verification and identification modes. Comparative experiments on a TEOAE data set of biometric setting show the merits of the proposed method. Performance is further improved with fusion of information from both ears.