INSERT INTO `citations` (`citation_id`, `submission_id`, `raw_citation`, `seq`) VALUES
(2438, 479, 'Beaumont-Gay, M. (2007). A Comparison of Syn Flood Detection Algorithms. Second International Conference on Internet Monitoring and Protection (ICIMP 2007), 9. https://doi.org/10.1109/icimp.2007.1', 9),
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(2442, 479, 'Carbone, P. (2015). Apache Flink: Stream and Batch Processing in a Single Engine. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 36, 4.', 13),
(2443, 479, 'Cárdenas, A. A., Mcdaniel, P., Smith, S. W., Manadhata, P. K., Hp, |, Sreeranga, L., & Rajan, P. (2013). Big Data Analytics for Security. (December), 74–76.', 14),
(2444, 479, 'Ceron, J. M., Steding-Jessen, K., Hoepers, C., Granville, L. Z., & Margi, C. B. (2019). Improving iot botnet investigation using an adaptive network layer. Sensors (Switzerland), 19(3), 1–16. https://doi.org/10.3390/s19030727', 15),
(2445, 479, 'Chakrabarti, S., & Singhal, M. (2007). Preventing Offline Dictionary Attacks. 40(6), 68–74. https://doi.org/10.1109/mc.2007.216', 16),
(2446, 479, 'Chebotko, A. (2015). A Big Data Modeling Methodology for Apache Cassandra. IEEE International Congress on Big Data, 238–245. IEEE.', 17),
(2447, 479, 'Cimpanu, C. (2018). SirenJack Attack Lets Hackers Take Control Over Emergency Alert Sirens. Retrieved September 19, 2020, from https://www.bleepingcomputer.com/news/security/sirenjack-attack-lets-hackers-take-control-over-emergency-alert-sirens/', 18),
(2448, 479, 'Dawoud, A., Shahristani, S., & Raun, C. (2018). Deep learning and software-defined networks: Towards secure IoT architecture. Internet of Things, 3–4, 82–89. https://doi.org/10.1016/j.iot.2018.09.003', 19),
(2449, 479, 'Elsaeidy, A., Elgendi, I., Munasinghe, K. S., Sharma, D., & Jamalipour, A. (2017). A smart city cyber security platform for narrowband networks. 2017 27th International Telecommunication Networks and Applications Conference, ITNAC 2017, 2017-Janua, 1–6. https://doi.org/10.1109/ATNAC.2017.8215388', 20),
(2450, 479, 'Gajek, S., Jensen, M., Liao, L., & Schwenk, J. (2009). Analysis of signature wrapping attacks and countermeasures. 2009 IEEE International Conference on Web Services, ICWS 2009, 575–582. https://doi.org/10.1109/ICWS.2009.12', 21),
(2451, 479, 'Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007', 22),
(2452, 479, 'Gao, W., Morris, T., Reaves, B., & Richey, D. (2010). On SCADA control system command and response injection and intrusion detection. General Members Meeting and ECrime Researchers Summit, ECrime 2010, 1–9. https://doi.org/10.1109/ecrime.2010.5706699', 23),
(2453, 479, 'Gruschka, N., & Jensen, M. (2010). Attack surfaces: A taxonomy for attacks on cloud services. Proceedings - 2010 IEEE 3rd International Conference on Cloud Computing, CLOUD 2010, 276–279. https://doi.org/10.1109/CLOUD.2010.23', 24),
(2454, 479, 'Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27–48. https://doi.org/10.1016/j.neucom.2015.09.116', 25),
(2455, 479, 'Gupta, A., Thakur, H. K., Shrivastava, R., Kumar, P., & Nag, S. (2017). A Big Data Analysis Framework Using Apache Spark and Deep Learning. IEEE International Conference on Data Mining Workshops, ICDMW, 2017-Novem(1), 9–16. https://doi.org/10.1109/ICDMW.2017.9', 26),
(2456, 479, 'HaddadPajouh, H., Dehghantanha, A., Khayami, R., & Choo, K. K. R. (2018). A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting. Future Generation Computer Systems, 85, 88–96. https://doi.org/10.1016/j.future.2018.03.007', 27),
(2457, 479, 'He, Y., Mendis, G. J., & Wei, J. (2017). Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism. IEEE Transactions on Smart Grid, 8(5), 2505–2516. https://doi.org/10.1109/TSG.2017.2703842', 28),
(2458, 479, 'Herley, C., & Florêncio, D. (2008). Protecting financial institutions from brute-force attacks. IFIP International Federation for Information Processing, 278, 681–685. https://doi.org/10.1007/978-0-387-09699-5_45', 29),
(2459, 479, 'Homayoun, S., Dehghantanha, A., Ahmadzadeh, M., Hashemi, S., Khayami, R., Choo, K. K. R., & Newton, D. E. (2019). DRTHIS: Deep ransomware threat hunting and intelligence system at the fog layer. Future Generation Computer Systems, 90, 94–104. https://doi.org/10.1016/j.future.2018.07.045', 30),
(2460, 479, 'Hossain, M. M. (2015). Towards an Analysis of Security Issues, Challenges, and Open Problems in The Internet of Things. 015 IEEE World Congress on Services, 21–28.', 31),
(2461, 479, 'Hsieh, C. J., & Chan, T. Y. (2016). Detection DDoS attacks based on neural-network using Apache Spark. 2016 International Conference on Applied System Innovation, IEEE ICASI 2016, 1–4. https://doi.org/10.1109/ICASI.2016.7539833', 32),
(2462, 479, 'Huang, G. (2014). Semi-Supervised and Unsupervised Extreme Learning Machines. IEEE Transactions on Cybernetics, 4(12), 2405–2417.', 33),
(2463, 479, 'Hussain, R., & Abdullah, I. (2018). Review of Different Encryptionand Decryption Techniques Used for Security and Privacy of IoT in Different Applications. 2018 6th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2018, 293–297. https://doi.org/10.1109/SEGE.2018.8499430', 34),
(2464, 479, 'J. Horchert. (2013). Mapping the internet: A hacker’s secret internet census - spiegel online - international. Retrieved from https://www.spiegel.de/international/world/hacker-measures-the-internet-illegally-with-carna-botnet-a-890413.html', 35),
(2465, 479, 'Jim, T., Swamy, N., & Hicks, M. (2007). Defeating script injection attacks with browser-enforced embedded policies. 16th International World Wide Web Conference, WWW2007, 601–610. https://doi.org/10.1145/1242572.1242654', 36),
(2466, 479, 'Kannhavong, B., Nakayama, H., Nemoto, Y., Kato, N., & Jamalipour, A. (2007). A survey of routing attacks in mobile ad hoc networks. IEEE Wireless Communications, 14(5), 85–91. https://doi.org/10.1109/MWC.2007.4396947', 37),
(2467, 479, 'Kara, I., & Aydos, M. (2019). Static and Dynamic Analysis of Third Generation Cerber Ransomware. International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism, IBIGDELFT 2018 - Proceedings, 12–17. https://doi.org/10.1109/IBIGDELFT.2018.8625353', 38),
(2468, 479, 'Katal, A., Wazid, M., & Goudar, R. H. (2013). Big Data: Issues, Challenges, Tools and Good Practices. 2013 6th International Conference on Contemporary Computing, IC3 2013, 404–409. https://doi.org/10.1109/IC3.2013.6612229', 39),
(2469, 479, 'Kc, G. S. (2003). Countering Code-Injection Attacks With Instruction-Set Randomization. Proceedings of the 10th ACM Conference on Computer and Communications Security, 272–280. Retrieved from https://sci-hub.do/10.1145/948109.948146', 40),
(2470, 479, 'Khan, M. A., & Salah, K. (2018). IoT security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems, 82, 395–411. https://doi.org/10.1016/j.future.2017.11.022', 41),
(2471, 479, 'Khattak, H. A., Shah, M. A., Khan, S., Ali, I., & Imran, M. (2019). Perception layer security in Internet of Things. Future Generation Computer Systems, 100, 144–164. https://doi.org/10.1016/j.future.2019.04.038', 42),
(2472, 479, 'Khumoyun, A., Cui, Y., & Hanku, L. (2016). Spark based distributed Deep Learning framework for Big Data applications. 2016 International Conference on Information Science and Communications Technologies, ICISCT 2016, 1–5. https://doi.org/10.1109/ICISCT.2016.7777390', 43),
(2473, 479, 'Kiezun, A., Guo, P. J., Jayaraman, K., & Ernst, M. D. (2009). Automatic creation of SQL injection and cross-site scripting attacks. Proceedings - International Conference on Software Engineering, 199–209. https://doi.org/10.1109/ICSE.2009.5070521', 44),
(2474, 479, 'Kleinman, A. (2017). The most detailed map of the internet was made by breaking the law. Retrieved from https://www.huffpost.com/entry/internet-map_n_2926934', 45),
(2475, 479, 'Komalasari, R. (2020). Manfaat Teknologi Informasi Dan Komunikasi Di Masa Pandemi Covid 19. Tematik, 7(1), 38–50. https://doi.org/10.38204/tematik.v7i1.369', 46),
(2476, 479, 'Kozik, R. (2018). Distributing extreme learning machines with Apache Spark for NetFlow-based malware activity detection. Pattern Recognition Letters, 101, 14–20. https://doi.org/10.1016/j.patrec.2017.11.004', 47),
(2477, 479, 'Liou, C. Y., Huang, J. C., & Yang, W. C. (2008). Modeling word perception using the Elman network. Neurocomputing, 71(16–18), 3150–3157. https://doi.org/10.1016/j.neucom.2008.04.030', 48),
(2478, 479, 'Lippmann, R. P., Fried, D. J., Graf, I., Haines, J. W., Kendall, K. R., McClung, D., … Zissman, M. A. (2000). Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation. Proceedings - DARPA Information Survivability Conference and Exposition, DISCEX 2000, 2, 12–26. https://doi.org/10.1109/DISCEX.2000.821506', 49),
(2479, 479, 'Liu, J., Xiao, Y., & Chen, C. L. P. (2012). Authentication and access control in the Internet of things. Proceedings - 32nd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2012, 588–592. https://doi.org/10.1109/ICDCSW.2012.23', 50),
(2480, 479, 'Marir, N. (2018). Distributed Abnormal Behavior Detection Approach Based on Deep Belief Network and Ensemble SVM using Spark. IEEE Access, 59657–59671.', 51),
(2481, 479, 'Marjani, M. (2017). Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges. IEEE Access, 5247–5261.', 52),
(2482, 479, 'Mavridis, I., & Karatza, H. (2017). Performance evaluation of cloud-based log file analysis with Apache Hadoop and Apache Spark. Journal of Systems and Software, 125, 133–151. https://doi.org/10.1016/j.jss.2016.11.037', 53),
(2483, 479, 'Meidan, Y., Bohadana, M., Mathov, Y., Mirsky, Y., Shabtai, A., Breitenbacher, D., & Elovici, Y. (2018). N-BaIoT-Network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervasive Computing, 17(3), 12–22. https://doi.org/10.1109/MPRV.2018.03367731', 54),
(2484, 479, 'Modi, C., Patel, D., Borisaniya, B., Patel, H., Patel, A., & Rajarajan, M. (2013). A survey of intrusion detection techniques in Cloud. Journal of Network and Computer Applications, 36(1), 42–57. https://doi.org/10.1016/j.jnca.2012.05.003', 55),
(2485, 479, 'Mohammadi, M. (2018). Deep Learning for IoT Big Data and Streaming Analytics: A Survey. IEEE Communications Surveys & Tutorials, 20(4), 2923–2960.', 56),
(2486, 479, 'Moustafa, N., Slay, J., & Technology, I. (2015). Unsw-nb15: a comprehensive data set for network intrusion detection systems [157] N. Moustafa, J. Slay, Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). Proceeding Military Communications and Information Systems Conference (MilCIS), 1–6. https://doi.org/10.1109/MilCIS.2015.7348942', 57),
(2487, 479, 'Munawar, Zen and Putri, N. I. (2020). Keamanan Jaringan Komputer Pada Era Big Data. J-SIKA| Jurnal Sistem Informasi Karya Anak Bangsa, 02(01), 14–20.', 58),
(2488, 479, 'Mylavarapu, G. (2015). Real-time Hybrid Intrusion Detection System using Apache Storm. High Performance Computing and Communications IEEE 7th Int. Symp. Cyberspace Safety and Security Conf. Embedded Software and Systems, 1436–1441. https://doi.org/10.1109/HPCC-CSS-ICESS.2015.241', 59),
(2489, 479, 'Nobakht, M., Sivaraman, V., & Boreli, R. (2016). A host-based intrusion detection and mitigation framework for smart home IoT using OpenFlow. Proceedings - 2016 11th International Conference on Availability, Reliability and Security, ARES 2016, 147–156. https://doi.org/10.1109/ARES.2016.64', 60),
(2490, 479, 'Radoglou Grammatikis, P. I., Sarigiannidis, P. G., & Moscholios, I. D. (2019). Securing the Internet of Things: Challenges, threats and solutions. Internet of Things, 5, 41–70. https://doi.org/10.1016/j.iot.2018.11.003', 61),
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(2492, 479, 'Roopak, M., Yun Tian, G., & Chambers, J. (2019). Deep learning models for cyber security in IoT networks. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019, 452–457. https://doi.org/10.1109/CCWC.2019.8666588', 63),
(2493, 479, 'Saxe, J. (2015). Deep Neural Network Based Malware Detection using Two Dimensional Binary Program Features. 2015 10th International Conference on Malicious and Unwanted Software (MALWARE), 11–20. Retrieved from https://dl.acm.org/doi/10.1109/MALWARE.2015.7413680', 64),
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INSERT INTO `citations` (`citation_id`, `submission_id`, `raw_citation`, `seq`) VALUES
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INSERT INTO `citations` (`citation_id`, `submission_id`, `raw_citation`, `seq`) VALUES
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(4453, 689, 'Munawar, Zen. (2014b). Penggunaan Metode Multi Threads untuk Pengelolaan Proses Download di Internet. TEMATIK - Jurnal Teknologi Informasi Dan Komunikasi, 1(1), 47–58. https://doi.org/10.38204/tematik.v1i1.35', 16),
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(4458, 689, 'Munawar, Zen. (2018). Keamanan Pada E-Commerce Usaha Kecil dan Menengah. TEMATIK - Jurnal Teknologi Informasi Dan Komunikasi, 5(1), 1–16. https://doi.org/10.38204/tematik.v5i1.144', 21),
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(4463, 689, 'Munawar, Zen, & Putri, N. I. (2020). Keamanan IoT Dengan Deep Learning dan Teknologi Big Data. TEMATIK - Jurnal Teknologi Informasi Dan Komunikasi, 7(2), 161–185. https://doi.org/10.38204/tematik.v7i2.479', 26),
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