Marzieh Ahmadzadeh

The Canterbury QuestionBank: building a repository of multiple-choice CS1 and CS2 questions

In this paper, we report on an ITiCSE-13 Working Group that developed a set of 654 multiple-choice questions on CS1 and CS2 topics, the Canterbury QuestionBank. We describe the questions, the metadata we investigated, and some preliminary investigations of possible research uses of the QuestionBank. The QuestionBank is publicly available as a repository for computing education instructors and researchers.

Know abnormal, find evil : frequent pattern mining for ransomware threat hunting and intelligence

Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims’ computers and requests a ransom payment to re-instantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately system logs can be mined to hunt abnormalities and stop the evil. In this paper we first setup an environment to collect activity logs of 517 Locky ransomware samples, 535 Cerber ransomware samples and 572 samples of TeslaCrypt ransomware. We utilize Sequential Pattern Mining to find Maximal Frequent Patterns (MFP) of activities within different ransomware families as candidate features for classification using J48, Random Forest, Bagging and MLP algorithms. We could achieve 99 percent accuracy in detecting ransomware instances from goodware samples and 96.5 percent accuracy in detecting family of a given ransomware sample. Our results indicate usefulness and practicality of applying pattern mining techniques in detection of good features for ransomware hunting. Moreover, we showed existence of distinctive frequent patterns within different ransomware families which can be used for identification of a ransomware sample family for building intelligence about threat actors and threat profile of a given target.

The Impact of Improving Debugging Skill on Programming Ability

Abstract This paper reports on a continuing study into teaching programming to adult novice students. As part of the study we aim to find students’ pattern of behavior when they are programming in Java. In a broader perspective, we are interested in improving the students’ ability to write programming. By patterns of behavior, we mean finding the frequently made compiler errors and the pattern of debugging. We claim that incorporating students’ most common errors in a form of debugging exercise will improve their ability to program.

JavaMarker: A Marking System for Java Programs

ABSTRACT In this paper a marking system for Java programming is presented which has been developed as a plug-in for a widely used editor, Eclipse. This system runs student submitted programs against previously defined test cases. Depending on the percentage of correct running code, a proper mark is awarded. Since this program was implemented in order to be used in a principles of programming course, we require students to practice coding with a correct style. Therefore, this system checks the style of the code and produces messages when a better style is expected. In some cases penalty marks are considered for improper code style. For this system to play an educational role, we allow students to submit more than once. With this we aim to help them learn from their mistakes. The number of submissions differs from one exercise to another and is defined dynamically by our system administration. We call this system JavaMarker. General Terms Design, Experimentation, Languages.

Personalized ATMs: Improve ATMs Usability

Using customization in products and services is one of the important methods for obtaining customers’ satisfaction. In this paper, the personalized ATMs have been introduced which as one of the objectives, using personalization and customization methods, improve the efficiency and simplicity of usage of the ATMs and enable the users to benefit from the machine commensurate with their needs. In this system, based on general information about users and the records of customers’ activities in CRM system, the information and screens are displayed which are predicted to be most applicable for the customers. This system is able to provide services based on users’ abilities in order to enable all the customers to acquire their needed services from the system in the shortest time duration and highest efficiency.

BoTShark: A Deep Learning Approach for Botnet Traffic Detection

While botnets have been extensively studied, bot malware is constantly advancing and seeking to exploit new attack vectors and circumvent existing measures. Existing intrusion detection systems are unlikely to be effective countering advanced techniques deployed in recent botnets. This chapter proposes a deep learning-based botnet traffic analyser called Botnet Traffic Shark (BoTShark). BoTShark uses only network transactions and is independent of deep packet inspection technique; thus, avoiding inherent limitations such as the inability to deal with encrypted payloads. This also allows us to identify correlations between original features and extract new features in every layer of an Autoencoder or a Convolutional Neural Networks (CNNs) in a cascading manner. Moreover, we utilise a Softmax classifier as the predictor to detect malicious traffics efficiently.

Pattern of Plagiarism in Novice Students' Generated Programs: An Experimental Approach

In this research we looked for such metrics by an experimental approach that was carried out in three phases. In the first phase we learned the possible ways to plagiarize, which was done by interviewing students. Categorizing the known methods lead us to the most popular plagiarism approach that was applied by the students. Therefore an experiment was designed to simulate the real plagiarism situation. Data that was gathered in this phase were evaluated against real plagiarism data to ensure the results.

A feasibility study on using clustering algorithms in programming education research

Designing an experiment for programming education research, in which collecting and interpreting a large number of qualitative data about programmers is required, needs careful consideration in order to validate the experiment. When it comes to finding a pattern in the programming behaviour of a specific group of programmers (e,g. novice, intermediate or expert programmers), one of the critical issues is the selection of similar participants who can be placed in one group. In this study we were interested in finding a method that could shorten the path to finding participants. Therefore, the use of clustering algorithms to group similar participants was put to test in order to investigate the effectiveness and feasibility of this approach. The clustering algorithms that were used for this study were K-means and DBSCAN. The results showed that the use of these algorithms, for the mentioned purpose, is feasible and that both algorithms can identify similar participants and place them in the same group while participants who are not similar to others, and therefore are not the correct subject of the study, are recognised.

A New Method for Designing a Sitemap

Sitemap is a tool used by web designers to increase the accessibility of their site’s information. The significance of sitemaps lies in providing their users with an overview of the contents of the website. In this article, a new method for designing a sitemap, which is based on different users need, is proposed. The customers of websites are usually at least one or two groups with specific information needs, needing to be provided with more help. In this proposed method, the requirements of these groups are first defined, and scenarios for moving through the site’s pages are also recommended. These scenarios guide users to the right pages in a high level of quality (speed, ease of access and desirability of information). Each scenario leads to a page of website and may mark the other pages in its path. Using our designed sitemap, a proper understanding of the sitemap is expected.