Algorithm Selection Framework for Cyber Attack Detection

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2

The State of the Art of Automatic Programming

Automaatprogrammeerimine või koodi genereerimine on teatud tüüpi arvutiprogrammide loomisviis, kus kood genereeritakse mõne tööriista abil, mis võimaldab arendajatel koodi kirjutada kõrgemal abstraktsioonitasemel. Selliste programmide rakendamine tarkvaraarenduse protsessis on hea viis programmeerijate produktiivsuse tõstmiseks, võimaldades neil keskenduda pigem käesolevale ülesandele kui implementatsiooni detailidele. Senises teaduskirjanduses on vaadeldud konkreetseid lähenemisi või meetodeid eraldi. Väga vähesed uurimustööd vaatlevad aga kogu valdkonna viimast taset. Käesolevas töös käsitletakse automaatprogrammeerimist olemasoleva kirjanduse süstemaatilise kirjandusülevaate meetodi abil. Töö teeb ülevaate teemaga seonduvatest algoritmidest, probleemidest ning uurmisvaldkonna avatud uurimisküsimustest ning võrdleb valdkonna hetketaset praktika hetketasemega. Vaaldeldud 37 asjakohasest uuringust tegelesid 19 automaatprogrammeerimise üldise määratlemise ja alateemadega. Kolmkümmend uuringut pakkusid välja konkreetse algoritmi või lähenemisviisi. Esitatud tehnikatest rakendati 2 praktikas. Viimasel ajal on automaatprogrammerimise fookus nihkunud programmide sünteesilt induktiivsele programmeerimisele, mille on põhjustanud läbimurded tehisintellekti valdkonnas. Mõistete ja alateemade määratlus on teadlaste vahel ühtne. Õigete spetsifikatsioonide sõnastamine ja piisava teabe andmine automatiseerimiseks on endiselt lahtine uurimisküsimus.Automatic programming or code generation is a type of computer programming where the code is generated using some tools allowing developers to write code at the higher level of abstraction. Implementing these types of programs into the software development process is a good way to boost programmers’ performance by focusing on the task at hand rather than implementation details. Current literature on the subject reviews single approach or method. Very few of them are reviewing state of the art in general. This paper reviews the state of the art of automatic programming by overviewing the existing literature on the topic using systematic literature review method. The paper overviews approaches and algorithms of the topic, examines issues and open questions in the field and compares the state of the art to the state of the practice. Of 37 relevant studies, 19 addressed general definitions and subtopics of automatic programming. 30 presented specific algorithms or approaches. 2 of proposed techniques were implemented in practice. Currently, the focus of automatic programming shifted from program synthesis to inductive programming, caused by a breakthrough in artificial intelligence. Definition of the term and subtopics is consistent between scholars. However, formulating correct specification and providing sufficient information for automation is still an open research question

Categorization of web sites in Turkey with SVM

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2004Includes bibliographical references (leaves: 61-63)Text in English; Abstract: Turkish and Englishix, 70 leavesIn this study of topic .Categorization of Web Sites in Turkey with SVM. after a brief introduction to what the World Wide Web is and a more detailed description of text categorization and web site categorization concepts, categorization of web sites including all prerequisites for classification task takes part. As an information resource the web has an undeniable importance in human life. However the huge structure of the web and its uncontrolled growth led to new information retrieval research areas to be risen in last years. Web mining, the general name of these studies, investigates activities and structures on the web to automatically discover and gather meaningful information from the web documents. It consists of three subfields: .Web Structure Mining., .Web Content Mining. and .Web Usage Mining.. In this project, web content mining concept was applied on the web sites in Turkey during the categorization process. Support Vector Machine, a supervised learning method based on statistics and principle of structural risk minimization is used as the machine learning technique for web site categorization. This thesis is intended to draw a conclusion about web site distributions with respect to thematic categorization based on text. The popular web directory Yahoo.s 12 top level categories were used in this project. Beside of the main purpose, we gathered several statistical descriptive informations about web sites and contents used in html pages. Metatag usage percentages, html design structures and plug-in usage are some of these information. The processes taken through solution, start with employing a web downloader which downloads web page contents and other information such as frame content from each web site. Next, manipulating, parsing and simplifying the downloaded documents takes place. At this point, preperations for categorization task are completed. Then, by applying Support Vector Machine (SVM) package SVMLight developed by Thorsten Joachims, web sites are classified under given categories. The classification results obtained in the last section show that there are some over-lapping categories exist and accuracy and precision values are between 60-80. In addition to categorization results, we saw that almost 17 of web sites utilize html frames and 9367 web sites include metakeywords

Topic Maps as Indexing Tools in the Educational Sphere: Theoretical Foundations, Review of Empirical Research and Future Challenges

Non

Cultivating the under-mined:Cross-case analysis as knowledge mobilization

Abstract: Despite a plethora of case studies in the social sciences, it is the authors ' opinion that case studies remain relatively under-mined sources of expertise. Cross-case analysis is a research method that can mobilize knowledge from individual case studies. The authors propose that mobilization of case knowledge occurs when researchers accumulate case knowledge, compare and contrast cases, and in doing so, produce new knowledge. In this article, the authors present theories of how people can learn from sets of cases. Second, existing techniques for cross-case analysis are discussed. Third, considerations that enable researchers to engage in cross-case analysis are suggested. Finally, the authors introduce a novel online database: the Foresee (4C) database. The purpose of the database is to mobilize case knowledge by helping researchers perform cross-case analysis and by creating an online research community that facilitates dialogue and the mobilization of case knowledge. The design of the 4C database is informed by theories of how people learn from case studies and cross-case analysis techniques. We present evidence from case study research that use of the 4C database helps to mobilize previously dormant case study knowledge to foster greater expertise. Key words: case study, cross-case analysis, computer-assisted analysis, knowledge mobilization, researcher, databas