Building a secure website is time-consuming, expensive and challenging task for web developers. Researchers to identify webpage sinks to address security efforts, as it helps to reduce time and money to secure web application, are introducing different web vulnerabilities prediction models. Some of the well-known web vulnerabilities are SQL Injection, Cross Site Scripting (XSS) and Cross Site Request Forgery (CSRF). Different machine learning methods are being employed by the existing vulnerability prediction models to prevent vulnerable components in web applications. However, majority of these methods cannot challenge all web vulnerabilities. Therefore, this paper proposed a method namely NMPREDICTOR to predict vulnerable files in website for vulnerability prediction as a classification problem by predicting legitimate or vulnerable code. In addition, it is an effort to employ the classification on different classifier of machine learning algorithms to judge elimination of vulnerable components. Numerous experiments have been conducted in our study to evaluate the performance of our proposed model. Through our proposed method, we have builds 6 classifiers on a training set of labeled files represented by their software metrics and text features. Additionally, we builds a Meta classifier, which combines the six underlying classifiers i.e. J48, Naive Bayes and Random forest. NMPREDICTOR is evaluated on datasets of three web applications, which offers 223 superior quality vulnerabilities found in PHPMyAdmin, Moodle and Drupal. Our proposed method shows a clearly has an advantage over results of existing studies in case of Drupal, PhpMyAdmin and Moodle.

Predicting Web Vulnerabilities in Web Applications Based on Machine Learning

Dermis ailments are disorders that hurt or damage the dermis that has an enormous impact on the everyday life of a person. People’s tight schedule has significantly impacted their accessibility for repetitive examinations, thereby preventing individuals from consulting a medical practitioner. Network-centered medicinal schemes’ popularity is increasingly becoming a model for helping individuals recognize how crucial the level of an ailment is. Acne dermis ailment is one of the extremely well-known dermis sicknesses that troubles the sebaceous glands, thus repetitive diagnosis could assist to avoid blisters. Fuzzy based approach for diagnosing acne skin disease was proposed in this paper. It was suggested that the approach assisted solving the shortcomings of previous expert system methods. Expert machine reasoning is related to literary ambiguity. The proposed system of used fuzzy rules to address inaccuracy in the expert system’s analysis. It was proven that the scheme was 82% accurate, indicating good performance. The Fuzzy expert system built had shown an extreme level of guidance, medical care recommendations and demonstrated the degree of seriousness of acne dermis state in patients.

/pdf/severe-acne-skin-disease-a-fuzzy-based-method-for-diagnosis-392wv8co2o.pdf

Severe Acne Skin Disease: A Fuzzy-Based Method for Diagnosis

In the process of clarifying whether a patient or patients is suffering from a disease or not, diagnosis plays a significant role. The procedure is quite slow and cumbersome, and some patients may not be able to pursue the final test results and diagnosis. The method in this paper comprises many fact-finding and data-mining methods. Artificial Intelligence techniques such as Neural Networks and Fuzzy Logic were fussed together in emerging the Coactive Neuro-Fuzzy Expert System diagnostic tool. The authors conducted oral interviews with the medical practitioners whose knowledge were captured into the knowledge based of the Fuzzy Expert System. Neuro-Fuzzy expert system diagnostic software was implemented with Microsoft Visual C# (C Sharp) programming language and Microsoft SQL Server 2012 to manage the database. Questionnaires were administered to the patients and filled by the medical practitioners on behalf of the patients to capture the prevailing symptoms. The study demonstrated the practical application of neuro-fuzzy method in diagnosis of malaria. The hybrid learning rule has greatly enhanced the proposed system performance when compared with existing systems where only the back-propagation learning rule were used for implementation. It was concluded that the diagnostic expert system developed is as accurate as that of the medical experts in decision making. DIAGMAL is hereby recommended to medical practitioners as a diagnostic tool for malaria.

/pdf/diagmal-a-malaria-coactive-neuro-fuzzy-expert-system-4us7m5wsyh.pdf

Diagmal: A Malaria Coactive Neuro-Fuzzy Expert System

A system vulnerability analysis technique (SVAT) for complex mission critical systems (CMCS) was developed in response to the need to be able to conduct penetration testing on large industrial systems which cannot be taken offline or risk disablement or impairment for conventional penetration testing. SVAT-CMCS facilitates the use of known vulnerability and exploit information, incremental testing of system components and data analysis techniques to identify attack pathways in CMCSs. This data can be utilized for corrective activities or to target controlled manual follow-up testing. This paper presents the SVAT-CMCS paradigm and describes its implementation in a software tool, which was built using the Blackboard Architecture, that can be utilized for attack pathway identification. The performance of this tool is characterized using three example models. In particular, it explores the path generation speed and the impact of link cap restrictions on system operations, under different levels of network size and complexity. Accurate fact-rule processing is also tested using these models. The results show significant decreases in path generation efficiency as the link cap and network complexity increase; however, rule processing accuracy is not impacted.

Development of a System Vulnerability Analysis Tool for Assessment of Complex Mission Critical Systems

Approach towards most web-applications were ad-hoc, thus arose a need to raise security standards as it requires much efforts to maintain in meeting its quality standards. With existing and new technologies like ASP, VB scripts, CGI, it's quite hard enough task to assess the quality of web because of the factors that influence the performance of the website. With data all over the place and with users having no minimal knowledge on how to protect their data, the dark side of technology always tried to devise its own path and is now posing a serious threat to web security with new set of computer security vulnerabilities, SQL injection and cross-site scripting (XSS). Though there were some exploits such as buffer overflow, these SQL-Injections and cross-site scripting vulnerabilities are instances of the broader class of input validation, which are a result of changing business requirements. These input validation-based vulnerabilities therefore require fundamentally new techniques to characterize and mitigate them. This dissert focuses on how efficiently can we deal with these web security vulnerabilities , thus addressing active issues primarily with SQL injection attacks. Further we will bisect the field of web applications to understand vulnerable domains and will focus on the approach which could be followed to address the exploits which could occur due to vulnerabilities and the approach or methodologies which we can use to give proper security to the web applciation.

Security testing on web application

The biggest challenge of Web application is the inestimable losses arising from security flaws. Two approaches were advanced by a number of scholars to provide security to Web space. One of such approach is vulnerability assessment, which is a conscious effort to isolate, identify and recognize potentials vulnerabilities exploited by attackers. The second being the estimation and determination of level of risks/threats posed to Web applications by vulnerabilities obvious to the developer (or tester); this is generally referred to as penetration testing. Recently, there is Vulnerability Assessment and Penetration Testing (VAPT) that combined these two schemes to improve safety and effectively combat the menace of attackers on Web applications. This paper proposed Fuzzy Classifier-based Vulnerability and Assessment Testing (FCVAPT) model to provide security for sensitive data/information in Web applications. Cross Site Scripting (XSS) and Structured Query Language (SQL) injections were selected for evaluation of proposed FCVAPT model. FCVAPT model’s classification performance for MSE, MAPE and RMSE were 33.33, 14.81% and 5.77% respectively. FCVAPT is considerably effective for detecting vulnerability and ascertaining the nature of threats/risks available to Web applications.

A Fuzzy Classifier-Based Penetration Testing for Web Applications

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A Fuzzy Classifier-Based Penetration Testing for Web Applications