International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol. 14, no. 12, 2020 Paper—Smart Online Courses Using Computational Intelligence Smart Online Courses Using Computational Intelligence https://doi.org/10.3991/ijim.v14i12.15601 Irawan Dwi Wahyono (), Djoko Saryono, Khoirudin Asfani, Muhammad Ashar, Sunarti Universitas Negeri Malang, Malang, Indonesia irawan.dwi.ft@um.ac.id Abstract—Computer network security has become an important issue in re- cent decades, the government and several international organizations have in- vested in professional education and training for computer network security. In addition, with the increasing incidence of computer network security crimes, the government and several organizations have taken precautions by providing training to students about computer network security. Some parties develop learning models that are suitable for students and find appropriate learning methods to produce professionals in the field of computer network security that is more effective. The purpose of this study is to design a framework-based Learning system in the form of an Adaptive Online Open Course in Computer System Security Subjects for Information Technology (IT) students. The bene- fits of this framework are to enhance students' skills and abilities in industrial- based computer network security, startup companies and the ability to complete CTF competitions in IT network security. The framework designed is Adaptive in which students learn according to the interests and topics of Computer Net- work Security. Interest-based on students in completing the pretest per topic. Testing in this study is testing the impact and improvement of students' learning abilities and skills on Computer Security and Security System Competence test- ing in a small group consisting of 20 students by seeing the success of complet- ing 3 CTF Topics with each topic totaling 100 computer network security prob- lems in the CTF competition, the average validation result was 83.01% and the CTF exam passing rate was 93%. Keywords—Online course, smart, network security 1 Introduction Computer network security has become an important issue in recent decades, the government and several international organizations have invested in professional education and training for computer system security. In addition, with the increasing incidence of computer network security crime, the government and several organiza- tions have prevented it by learning from students about computer network security. Some parties develop learning models that are appropriate for students and find ap- propriate learning methods to produce professionals in the field of computer network iJIM ‒ Vol. 14, No. 12, 2020 29 https://doi.org/10.3991/ijim.v14i12.15601%0d mailto:irawan.dwi.ft@um.ac.id Paper—Smart Online Courses Using Computational Intelligence security that are more effective[1-5]. Some international organizations concerned with computer network security hold computer network security competitions such as Capture the Flag [6], providing valuable experience for young people, especially stu- dents in learning and implementing skills and competencies about computer network security like the real world [6]. Incorporating computer system security competition in online learning systems, adding value in motivating students and playing a direct role and contributing to computer network security [4-6]. In general, there are many challenges and problems faced in organizing learning in computer network security courses, especially in the selection of models, methods and strategies suitable for online learning participants. In other parts, students must have skills and competencies in accordance with the industrial world such as honing abili- ties in computer network security competition. One way is to take part in online judge competitions such as CTF. Many students who fail cannot even solve a single prob- lem in CTF judge online competition [6]. To combine learning and competition online judge learning for a computer network security course that is interrelated between the material and capabilities desired by the industry and the world of IT network security [6-7]. The main competencies of computer network security courses at the university lev- el may be different from each type of study program, but the basic competencies can be assured of the same. Students may have different interests in the topic of computer network security. Study programs that offer computer network security courses in the curriculum include S1 in Informatics Engineering Education (PTI) and S1 in Infor- matics Engineering (IT), the 2014 curriculum and computer network security compe- titions held by KKNI based on the computer network security industry. The open online course development model must also be adaptive to the topics requested by students to specialize in certain areas of computer network [7-11]. Likewise, compe- tency testing must be adaptive according to the interests and choices of students' cho- sen topics. In this research, the online course will be developed in the form of an adaptive Open Online Course for the Computer System Security Course. It is hoped that this research can make it easier for students to explore computer system security courses and participate in international CTF competitions 2 Method This study uses methods and models to develop materials and test adaptive frame- works. The model of making an open online course uses the Dick and Carey model [13] and is carried out by following ten procedural steps that refer to the recommenda- tions of Borg and Gall [14-16]starting from collecting the data needed for assessment to produce the product being tested and disseminated to users. In practice, there are ten steps or steps that must be performed on the R&D method [17-21], including: 1. The research stage of information gathering (research and information gathering); 2. The planning stage (planning); 30 http://www.i-jim.org Paper—Smart Online Courses Using Computational Intelligence 3. The stage of building a product pre-plan (developing an introduction to the prod- uct); 4. The stage of conducting initial field testing; 5. Product revision stage (main product revision); 6. Stage of product testing in the field (main field testing); 7. Operational product revision stage; 8. Stages of conducting operational tests in the field (operational field trials); 9. The final product revision stage; and 10. The stages of dissemination and implementation. The procedure for developing this research material is shown in Figure 1. Fig. 1. Learning material model The design, implementation and testing methods for the adaptive framework use the R&D method as shown in Figure 2. Fig. 2. R&D methods for implementing frameworks 2.1 Formulating problems in the CTF competition and computer network security course material In this first step, the noted all the models of computer network security problems in the CTF competition. The difficulty of the problem on the CTF web as shown in Fig- ure 3. iJIM ‒ Vol. 14, No. 12, 2020 31 Paper—Smart Online Courses Using Computational Intelligence Fig. 3. The CTF web part of the write-up has difficulty 2.2 Data CTF The second step in this research is to collect data on questions and solutions in the CTF competition and then discuss them based on the many solutions that have been solved by the competition participants. An example of a solution to the CTF problem as shown in Figure 4. Fig. 4. CTF problems solved in the web root.me 2.3 Framework smart open online courses The next step in this research is to create a framework for open online courses to solve difficulties in the form of adaptive online courses based on topics to solve ques- tions and interests of CTF users. The design of this system is shown in Figure 5. The course manager is the admin who manages the framework. The instructor is the teach- 32 http://www.i-jim.org Paper—Smart Online Courses Using Computational Intelligence er to guide the user in helping to solve CTF questions. Students are users who only see existing solutions but do not participate in the course. Users are users who take the course. The working principle of this framework is as follows: 1. The user enters, then performs a pre-test to determine the topic to be taken 2. This Adaptive Module evaluates the results of the pre-test to suggest taking the topic of the course. 3. The pre-test assessment is conducted by the instructor then suggests taking certain topics according to the pre-test results. 4. Users attend courses and practice CTF questions 5. Users take the exam on the CTF.org website which is linked to this system, so the system evaluates results based on CTF.org then this system provides solutions to questions that do not pass. Fig. 5. Framework design For hardware specifications required on this system are shown in Table 1. Table 2 for the software specifications used on this system. Table 1. Hardware spesifications Specifications Amount CPU 2 RAM 2 GB Swap 2 GB Storage 40 GB Public IP 1 iJIM ‒ Vol. 14, No. 12, 2020 33 Paper—Smart Online Courses Using Computational Intelligence Table 2. Software specifications Specifications Description Web programming PHP 7 Database SQL server Web framework CodeIgniter Web template Bootstrap Model Contest Moe Contest Environment. Licensed under MIT License 3 Result and Discussion The implementation of this research is in a web-based and mobile-based form as shown in Figure 6. This system contains topics that have been classified based on the CTF competition as shown in Figure 6. Fig. 6. Front view of open framework based online course Fig. 7. Display topics that are classified according to CTF competitions 34 http://www.i-jim.org Paper—Smart Online Courses Using Computational Intelligence Fig. 8. The user interface has solved CTF questions on the CTF.org website Fig. 9. Display 3 Topics that have been resolved by the user Whereas users have completed questions based on suggested topics and then access CTF.org in the system to solve questions based on topics as shown in Figure 8. After completing all the suggested topics from 3 topics, users can see the number of results of completing CTF questions as indicated in Figure 9. In Figure 10, a graphical dis- play in which the user has solved several CTF questions. Fig. 10. Display user hassle the number of courses is taken and CTF questions that have been completed iJIM ‒ Vol. 14, No. 12, 2020 35 Paper—Smart Online Courses Using Computational Intelligence Table 3. Topic 1 Testing Results on users No User Test Result Status 1 user001 85 Pass 2 user002 85 Pass 3 user003 95 Pass 4 user004 85 Pass 5 user005 85 Pass 6 user006 85 Pass 7 user007 70 Pass 8 user008 85 Pass 9 user009 85 Pass 10 user010 85 Pass 11 user011 85 Pass 12 user012 30 No Pass 13 user013 85 Pass 14 user014 87 Pass 15 user015 85 Pass 16 user016 88 Pass 17 user017 85 Pass 18 user018 75 Pass 19 user019 85 Pass 20 user020 85 Pass Table 4. Topic 2 Testing Results on users No User Test Result Status 1 user001 88 Pass 2 user002 90 Pass 3 user003 90 Pass 4 user004 90 Pass 5 user005 92 Pass 6 user006 70 Pass 7 user007 90 Pass 8 user008 90 Pass 9 user009 87 Pass 10 user010 80 Pass 11 user011 90 Pass 12 user012 50 No Pass 13 user013 90 Pass 14 user014 90 Pass 15 user015 87 Pass 16 user016 90 Pass 17 user017 90 Pass 18 user018 90 Pass 19 user019 90 Pass 20 user020 90 Pass 20 user020 85 Pass 36 http://www.i-jim.org Paper—Smart Online Courses Using Computational Intelligence Table 5. Topic 3 Testing Results on users No User Test Result Status 1 user001 70 Pass 2 user002 90 Pass 3 user003 88 Pass 4 user004 74 Pass 5 user005 92 Pass 6 user006 70 Pass 7 user007 90 Pass 8 user008 45 No Pass 9 user009 87 Pass 10 user010 80 Pass 11 user011 90 Pass 12 user012 85 Pass 13 user013 90 Pass 14 user014 90 Pass 15 user015 87 Pass 16 user016 45 No Pass 17 user017 86 Pass 18 user018 90 Pass 19 user019 85 Pass 20 user020 88 Pass The testing of this system was carried out in a small group consisting of 20 stu- dents of Informatics Engineering, State University of Malang. Tests on topics 1, 2 and 3. Test results on topic 1 as shown in Table 3. In table 3, 1 user cannot pass. The pass- ing rate for topic 1 is 95%. The test results on topic 2 as shown in Table 4. In table 4, 1 user cannot pass. The passing rate for topic 1 is 95%. The test results on topic 3 as shown in Table 5. In table 5 there are 2 users who did not pass. The passing rate for topic 1 is 90%. So, the average graduation in working on CTF questions is 93%. While the average value for each topic is 81.75 for topic 1, topic 2 is 86.2 and topic 3 is 81.1. The graduation requirements for a topic are 75. The average test score chart for each topic is shown in Figure 11. iJIM ‒ Vol. 14, No. 12, 2020 37 Paper—Smart Online Courses Using Computational Intelligence Fig. 11. Graph of average scores per topic with a total of 20 students of IT students 4 Conclusion Based on the description of the results of the study, the following conclusions can be made an adaptive smart online course system based on a framework for solving CTF problems in the field of computer security networks. This system aims to im- prove the ability and expertise in the field of computer network security with a proven graduation rate and the average grade of students in completing CTF questions. Testing the validation of adaptive smart online courses based on the adaptive framework using the Dick and Carey model and R&D obtained an average passing rate of CTF questions of 93%, and then based on this testing model, then the adaptive online course smart is declared valid. 5 Acknowledgement IsDB funded this research for the 2019 budget in collaboration with PNBP Univer- sitas Negeri Malang (UM) to support PUIPT DLI UM. 6 References [1] Guo, T., Xu, C., He, S., Shi, B., Xu, C., & Tao, D. (2019). Robust student network learn- ing. IEEE transactions on neural networks and learning systems. https://doi.org/10.1109/tnnls.2019.2929114 [2] Randrianasolo, A. S., & Pyeatt, L. D. (2014, December). Q-Learning: From Computer Network Security to Software Security. In 2014 13th International Conference on Machine Learning and Applications (pp. 257-262). IEEE https://doi.org/10.1109/icmla.2014.47 [3] Wirawan, I. M., Herwanto, H. W., & Wahyono, I. D. (2017, October). 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In 2019 Interna- tional Conference on Electrical, Electronics and Information Engineering (ICEEIE) (Vol. 6, pp. 196-200). IEEE. https://doi.org/10.1109/iceeie47180.2019.8981477 7 Authors Irawan Dwi Wahyono is a lecturer in the Informatics Engineering Study Program at the Department of Electrical Engineering at Universitas Negeri Malang, Indonesia.. His research interests include: Intelligent Computing, Mobile Computing and Artifi- cial Intelligence (email: irawan.dwi.ft@um.ac.id). Djoko Saryono is a Full Professor at Universitas Negeri Malang with research in Cultural Studies, Poetry, Arts and Humanities (email: djoko.saryono.fs@um.ac.id). Khoirudin Asfani is a lecturer in the Department of Electrical Engineering at Universitas Negeri Malang, Indonesia. His research interests include: Computer Net- working, Web Programming, Learning and Education (email: khoiru- din.asfani.ft@um.ac.id) Muhammad Ashar is a lecturer in the Department of Electrical Engineering at Universitas Negeri Malang, Indonesia.. His research interests include: Intelligent Computing and Mobile Computing (email: muhammad.ashar.ft@um.ac.id) Sunarti is a lecturer in Department of Letters, Universitas Negeri Malang. Her re- search interests include Literature, Linguistic and Culture (email: sunarti.fs@um.ac.id) Article submitted 2020-04-15. Resubmitted 2020-05-21. Final acceptance 2020-05-22. Final version published as submitted by the authors. 40 http://www.i-jim.org https://doi.org/10.3991/ijim.v11i6.6697 https://doi.org/10.1109/isemantic.2018.8549791 https://doi.org/10.1109/eeccis.2018.8692882 https://doi.org/10.1109/iceeie47180.2019.8981477 mailto:irawan.dwi.ft@um.ac.id djoko.saryono.fs@um.ac.id mailto:khoirudin.asfani.ft@um.ac.id mailto:khoirudin.asfani.ft@um.ac.id muhammad.ashar.ft@um.ac.id sunarti.fs@um.ac.id