Mehmet Ali Yalçınkaya; Ecir Uğur Küçüksille
Abstract
The widespread use of web applications and running on sensitive data has made them one of the most significant targets of cyber attackers. One of the most crucial security measures that can be taken is the detection and closure of vulnerabilities on web applications before attackers. In this study, a ...
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The widespread use of web applications and running on sensitive data has made them one of the most significant targets of cyber attackers. One of the most crucial security measures that can be taken is the detection and closure of vulnerabilities on web applications before attackers. In this study, a web application vulnerability scanner was developed based on dynamic analysis and artificial intelligence, which could test web applications using GET and POST methods and had test classes for 21 different vulnerability types. The developed vulnerability scanner was tested on a web application test laboratory, which was created within the scope of this study and had 262 different web applications. A data set was created from the results of the tests performed using the developed vulnerability scanner. In this study, as a first stage, web page classification was made using the mentioned data set. The highest success rate in the page classification process was determined by 95.39% using the Random Forest Algorithm. The second operation performed using the dataset was the association analysis between vulnerabilities. The proposed model saved the 21% time than the standard scanning model. The page classification process was also used in the crawling of the web application in this study.
Hayyan Salman Hasan; Hasan Muhammad Deeb; Behrouz Tork Ladani
Abstract
Sensitive methods are those that are commonly used by Android malware to perform malicious behavior. These methods may be either evasion or malicious payload methods. Although there are several approaches to handle these methods for performing effective dynamic malware analysis, but generally most of ...
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Sensitive methods are those that are commonly used by Android malware to perform malicious behavior. These methods may be either evasion or malicious payload methods. Although there are several approaches to handle these methods for performing effective dynamic malware analysis, but generally most of them are based on a manually created list. However, the performance shown by the selected approaches is dependent on completeness of the manually created list that is not almost a complete and up-to-date one. Missing some sensitive methods causes to degrade the overall performance and affects the effectiveness of analyzing Android malware.In this paper, we propose a machine learning approach to predict new sensitive methods that might be used in Android malware. We use a manually collected training dataset to train two classifiers: a classifier for detecting the sensitivity nature of the Android methods, and another classifier to categorize the detected sensitive methods into predefined categories. We applied the proposed approach to a large number of methods extracted from Android API 27. The proposed approach is able to predict hundreds of sensitive methods with accuracy of 90.5% for the first classifier and 87.4% for the second classifier. To evaluate the proposed approach, we built a new list of the detected sensitive methods and used it in a number of tools to perform dynamic malware analysis. The proposed model found various sensitive methods that were not considered before by any other tools. Hence, the effectiveness of these tools in performing dynamic analysis are increased.
Ghada Al-Hudhud; Abeer Al-Humamidi
Abstract
A Chatbot is a smart software that responds to natural language input and attempts to hold a conversation in a way that simulates humans. Chatbots have the potential to save any individual’s time, hassle, and tedium by automating mundane tasks. The idea of this research is that to investigate how ...
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A Chatbot is a smart software that responds to natural language input and attempts to hold a conversation in a way that simulates humans. Chatbots have the potential to save any individual’s time, hassle, and tedium by automating mundane tasks. The idea of this research is that to investigate how to help the user efficiently interact with the robot receptionist through an Intelligent Assistant dialogue. Chatbots are an effective way to improve services with their 24 /7 uptime, and their cost efficiency, and their multi-user quality. Despite the chatbots reduce human errors and give more answers that are accurate. Successful implementation of a chatbot requires correct analysis of the user’s query by the bot and ensures the correct response that should be given to the user. This research develops a chatbot for the Airports, which provides the visitors to the SWE chatbot Relevant information about the department. Throughout our extensive search since the very begin- ning of our project, we have been through multiple re- sources and endured a strenuous vetting process.
Saad Ali Alahmari
Abstract
The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. ...
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The increasing volatility in pricing and growing potential for profit in digital currency have made predicting the price of cryptocurrency a very attractive research topic. Several studies have already been conducted using various machine-learning models to predict crypto currency prices. This study presented in this paper applied a classic Autoregressive Integrated Moving Average(ARIMA) model to predict the prices of the three major cryptocurrencies âAT Bitcoin, XRP and Ethereum âAT using daily, weekly and monthly time series. The results demonstrated that ARIMA outperforms most other methods in predicting cryptocurrency prices on a daily time series basis in terms of mean absolute error (MAE), mean squared error (MSE) and root mean squared error(RMSE).
J. Hajian Nezhad; Majid Vafaei Jahan; M. Tayarani-N; Z. Sadrnezhad
Abstract
Recent improvements in web standards and technologies enable the attackers to hide and obfuscate infectious codes with new methods and thus escaping the security filters. In this paper, we study the application of machine learning techniques in detecting malicious web pages. In order to detect malicious ...
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Recent improvements in web standards and technologies enable the attackers to hide and obfuscate infectious codes with new methods and thus escaping the security filters. In this paper, we study the application of machine learning techniques in detecting malicious web pages. In order to detect malicious web pages, we propose and analyze a novel set of features including HTML, JavaScript (jQuery library) and XSS attacks. The proposed features are evaluated on a data set that is gathered by a crawler from malicious web domains, IP and address black lists. For the purpose of evaluation, we use a number of machine learning algorithms. Experimental results show that using the proposed set of features, the C4.5-Tree algorithm offers the best performance with 97.61% accuracy, and F1-measure has 96.75% accuracy. We also rank the quality of the features. Experimental results suggest that nine of the proposed features are among the twenty best discriminative features.