Publications
Argus has been used in a large number of network and cyber security research publications, dissertations, theses, books, presentations and blogs. We're proud that we could support so much research and development in computer networking, cyber security, machine learning, and general science with our work.
Maintaining a complete list has been non-trivial, and the list below is the result of keyword web searches, primarily looking at the Google, and the ACM Library for articles since 2010. If you do not see a research paper, dissertaion, book, presentation, reference that you wrote, or you liked, please send us a pointer. Also, if you find that a link on this page is stale, please send us a note to
Standards Organizations
European Union Agency for Cyber Security (ENISA)
Dissertations and Thesis
Research Articles
Research articles include published peer review articles that either use or reference Argus, Argus clients programs or Argus data in the work. Many of the AI/ML research papers use or reference the UNSW NB-15 dataset which is generated using argus data and programs. We are proud to have contributed directly and indirectly to so many research projects and efforts and hope that all the authors are "most excellent" in their efforts.
| Tristan Bilot, Nour El Madhoun, Khaldoun Al Agha, and Anis Zouaoui. 2024. A Survey on Malware Detection with Graph Representation Learning. ACM Comput. Surv. Just Accepted (May 2024). https://doi.org/10.1145/3664649 |
| Na Li, Yiyang Qi, Chaoran Li, and Zhiming Zhao. 2024. Active Learning for Data Quality Control: A Survey. J. Data and Information Quality 16, 2, Article 11 (June 2024), 45 pages. https://doi.org/10.1145/3663369 |
| Noor, U., Kanwal, R. and Rashid, Z., 2024. Enhancing Smart Cities Through Real-Time Insights and Safety: A Comparative Study of Supervised Machine Learning Algorithms for Anomaly Detection in Emerging Urban Landscapes. Technical Journal, 29(01), pp.47-60. |
| Hasan, M., & Malik, T. (2024, June). AI-Enhanced VPN Security Framework: Integrating Open-Source Threat Intelligence and Machine Learning to Secure Digital Networks. In European Conference on Cyber Warfare and Security (Vol. 23, No. 1, pp. 760-768). https://doi.org/10.34190/eccws.23.1.2505 |
| Q. Zeng and Y. Hara-Azumi, "Hardware/Software Codesign of Real-Time Intrusion Detection System for Internet of Things Devices," in IEEE Internet of Things Journal, vol. 11, no. 12, pp. 22351-22363, 15 June15, 2024, doi: 10.1109/JIOT.2024.3380822. |
| Mohammed Ayub, Yasser El-Alfy, and Ayaz H Khan. 2024. Distributed Intrusion Detection Systems Based on Deep Learning Techniques and Boosting Ensemble. In Proceedings of the 7th International Conference on Future Networks and Distributed Systems (ICFNDS '23). Association for Computing Machinery, New York, NY, USA, 180–191. https://doi.org/10.1145/3644713.3644736 |
| Ohtani, T., Yamamoto, R. and Ohzahata, S., 2024. IDAC: Federated Learning-Based Intrusion Detection Using Autonomously Extracted Anomalies in IoT. Sensors, 24(10), p.3218. |
| Yazan Otoum, Navya Gottimukkala, Neeraj Kumar, and Amiya Nayak. 2024. Machine Learning in Metaverse Security: Current Solutions and Future Challenges. ACM Comput. Surv. 56, 8, Article 215 (August 2024), 36 pages. https://doi.org/10.1145/3654663 |
| Shalli Rani, Ankita Sharma, and Muhammad Zohaib. 2024. Study for Integrating IoT-IDS Datasets: Machine and Deep Learning for Secure IoT Network System. In Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering (EASE '24). Association for Computing Machinery, New York, NY, USA, 686–691. https://doi.org/10.1145/3661167.3661286 |
| Paul Kiyambu Mvula, Paula Branco, Guy-Vincent Jourdan, and Herna Lydia Viktor. 2024. A Survey on the Applications of Semi-supervised Learning to Cyber-security. ACM Comput. Surv. 56, 10, Article 253 (October 2024), 41 pages. https://doi.org/10.1145/3657647 |
| Peddle, B., Lu, W., Yu, Q. (2024). Detecting DDoS Attacks in the Internet of Medical Things Through Machine Learning-Based Classification. In: Woungang, I., Dhurandher, S.K. (eds) The 6th International Conference on Wireless, Intelligent and Distributed Environment for Communication. WIDECOM 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 185. Springer, Cham. https://doi.org/10.1007/978-3-031-47126-1_13 |
| Junyi Liu, Yifu Tang, Haimeng Zhao, Xieheng Wang, Fangyu Li, and Jingyi Zhang. 2024. CPS Attack Detection under Limited Local Information in Cyber Security: An Ensemble Multi-Node Multi-Class Classification Approach. ACM Trans. Sen. Netw. 20, 2, Article 33 (March 2024), 27 pages. https://doi.org/10.1145/3585520 |
| Nuñez-Agurto, D., Fuertes, W., Marrone, L., Castillo-Camacho, M., Benavides-Astudillo, E., Perez, F. (2024). Attack Classification Using Machine Learning Techniques in Software-Defined Networking. In: Botto-Tobar, M., Zambrano Vizuete, M., Montes León, S., Torres-Carrión, P., Durakovic, B. (eds) International Conference on Applied Technologies. ICAT 2023. Communications in Computer and Information Science, vol 2050. Springer, Cham. https://doi.org/10.1007/978-3-031-58953-9_19 |
| Ortega-Fernandez, I., Sestelo, M., Burguillo, J.C. et al. Network intrusion detection system for DDoS attacks in ICS using deep autoencoders. Wireless Netw (2023). https://doi.org/10.1007/s11276-022-03214-3 |
| Lisa-Marie Geiginger and Tanja Zseby. 2024. Evading Botnet Detection. In Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing (SAC '24). Association for Computing Machinery, New York, NY, USA, 1331–1340. https://doi.org/10.1145/3605098.3635921 |
| T. Bilot, N. E. Madhoun, K. A. Agha and A. Zouaoui, "Graph Neural Networks for Intrusion Detection: A Survey," in IEEE Access, vol. 11, pp. 49114-49139, 2023, doi: 10.1109/ACCESS.2023.3275789. |
| Kostas, K., Just, M. and Lones, M.A., 2023. IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection. arXiv preprint arXiv:2401.01343. |
| Schumacher, N. et al. (2023). One-Class Models for Intrusion Detection at ISP Customer Networks. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-031-34107-6_3 |
| Chagas de Brito Guimarães, Lucas, DEEP LEARNING-BASED REAL-TIME BOTNET DETECTION FOR EDGE DEVICES. – Rio de Janeiro: UFRJ/COPPE, 2023. XIII, 42 p.: il.; 29, 7cm. |
| Muhammad Asim Mukhtar Bhatti, Muhammad Awais, and Aamna Iqtidar. 2023. Machine Learning based Intrusion Detection System for IoT Applications using Explainable AI. In Proceedings of the 2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics (AIMLR '23). Association for Computing Machinery, New York, NY, USA, Article 11, 1–6. https://doi.org/10.1145/3625343.3625356 |
| Mohamed Amine Merzouk, Frédéric Cuppens, Nora Boulahia-Cuppens, and Reda Yaich. 2023. Parameterizing poisoning attacks in federated learning-based intrusion detection. In Proceedings of the 18th International Conference on Availability, Reliability and Security (ARES '23). Association for Computing Machinery, New York, NY, USA, Article 104, 1–8. https://doi.org/10.1145/3600160.3605090 |
| Hooman Alavizadeh, Julian Jang-Jaccard, Simon Yusuf Enoch, Harith Al-Sahaf, Ian Welch, Seyit A. Camtepe, and Dan Dongseong Kim. 2022. A Survey on Cyber Situation-awareness Systems: Framework, Techniques, and Insights. ACM Comput. Surv. 55, 5, Article 107 (May 2023), 37 pages. https://doi.org/10.1145/3530809 |
| Shruti Sureshan and Debasis Das. 2023. Few-Shot Learning based Anomaly Detection in Security Applications. In Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD) (CODS-COMAD '23). Association for Computing Machinery, New York, NY, USA, 295–296. https://doi.org/10.1145/3570991.3571040 |
| Lewandowski B. Guidelines and a Framework to Improve the Delivery of Network Intrusion Detection Datasets. In SECRYPT 2023 (pp. 649-658). |
| Ezeh, D.A. and de Oliveira, J., 2023. An SDN controller-based framework for anomaly detection using a GAN ensemble algorithm. INFOCOMMUNICATIONS JOURNAL: A PUBLICATION OF THE SCIENTIFIC ASSOCIATION FOR INFOCOMMUNICATIONS (HTE), 15(2), pp.29-36. |
| Abeer Alalmaie, Priyadarsi Nanda, and Xiangjian He. 2023. Zero Trust Network Intrusion Detection System (NIDS) using Auto Encoder for Attention-based CNN-BiLSTM. In Proceedings of the 2023 Australasian Computer Science Week (ACSW '23). Association for Computing Machinery, New York, NY, USA, 1–9. https://doi.org/10.1145/3579375.3579376 |
| Ilhan Firat Kilincer, Fatih Ertam, Abdulkadir Sengur, Ru-San Tan, U. Rajendra Acharya, Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization, Biocybernetics and Biomedical Engineering, Volume 43, Issue 1, 2023, Pages 30-41, ISSN 0208-5216, https://doi.org/10.1016/j.bbe.2022.11.005. (https://www.sciencedirect.com/science/article/pii/S0208521622001012) |
| Wen Fei, Hiroyuki Ohno, and Srinivas Sampalli. 2023. A Systematic Review of IoT Security: Research Potential, Challenges, and Future Directions. ACM Comput. Surv. 56, 5, Article 111 (May 2024), 40 pages. https://doi.org/10.1145/3625094 |
| Weixi Wu. 2023. Traffic anomaly detection method based on bidirectional autoencoder generative adversarial network. In Proceedings of the 2023 International Conference on Communication Network and Machine Learning (CNML '23). Association for Computing Machinery, New York, NY, USA, 337–340. https://doi.org/10.1145/3640912.3640979 |
| Andrea Venturi, Matteo Ferrari, Mirco Marchetti, and Michele Colajanni. 2023. ARGANIDS: a novel Network Intrusion Detection System based on adversarially Regularized Graph Autoencoder. In Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23). Association for Computing Machinery, New York, NY, USA, 1540–1548. https://doi.org/10.1145/3555776.3577651 |
| Zengri Zeng, Baokang Zhao, Han-Chieh Chao, Ilsun You, Kuo-Hui Yeh, and Weizhi Meng. 2023. Towards Intelligent Attack Detection Using DNA Computing. ACM Trans. Multimedia Comput. Commun. Appl. 19, 3s, Article 126 (June 2023), 27 pages. https://doi.org/10.1145/3561057 |
| Cole, Robert G., Fustos, Jacob, Hart, Brian, Hill, Brennan, Wade, Susan, Cooper, Alexis, Cardona, Daniel, Sabbaghi, Arman, and Bullard, Carter. Emulation Modeling for Development of Cyber-Defense Capabilities for Satellite Systems. United States: N. p., 2022. Web. doi:10.2172/1894014. |
| Almaraz-Rivera, J.G., Perez-Diaz, J.A. and Cantoral-Ceballos, J.A., 2022. Transport and application layer DDoS attacks detection to IoT devices by using machine learning and deep learning models. Sensors, 22(9), p.3367. |
| J. G. Almaraz-Rivera, J. A. Perez-Diaz, J. A. Cantoral-Ceballos, J. F. Botero and L. A. Trejo, "Toward the Protection of IoT Networks: Introducing the LATAM-DDoS-IoT Dataset," in IEEE Access, vol. 10, pp. 106909-106920, 2022, doi: 10.1109/ACCESS.2022.3211513. |
| Zhiyan Chen, Jinxin Liu, Yu Shen, Murat Simsek, Burak Kantarci, Hussein T. Mouftah, and Petar Djukic. 2022. Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats. ACM Comput. Surv. 55, 5, Article 105 (May 2023), 37 pages. https://doi.org/10.1145/3530812 |
| Khodjaeva, Y., Zincir-Heywood, N. and Zincir, I., Can We Detect Malicious Behaviours in Encrypted DNS Tunnels Using Network Flow Entropy. Journal of Cyber Security and Mobility, Vol. 11_3 (Aug 2022), 461–496. |
| Chaganti, R., Mourade, A., Ravi, V., Vemprala, N., Dua, A. and Bhushan, B., 2022. A particle swarm optimization and deep learning approach for intrusion detection system in internet of medical things. Sustainability, 14(19), p.12828. |
| Y. Feng, J. Luo, C. Ma, T. Li and L. Hui, "I Can Still Observe You: Flow-level Behavior Fingerprinting for Online Social Network," GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 6427-6432, doi: 10.1109/GLOBECOM48099.2022.10001510. |
| Ying Xing, Hui Shu, Fei Kang, Hao Zhao, "Peertrap: An Unstructured P2P Botnet Detection Framework Based on SAW Community Discovery", Wireless Communications and Mobile Computing, vol. 2022, Article ID 9900396, 18 pages, 2022. https://doi.org/10.1155/2022/9900396 |
| Veronica Valeros, Sebastian Garcia, Hornet 40: Network dataset of geographically placed honeypots, Data in Brief, Volume 40, 2022, 107795, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2022.107795. (https://www.sciencedirect.com/science/article/pii/S2352340922000075) |
| Eva Papadogiannaki and Sotiris Ioannidis. 2021. A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures. ACM Comput. Surv. 54, 6, Article 123 (July 2022), 35 pages. https://doi.org/10.1145/3457904 |
| Nguyen P.C. et al. (2022) An Intrusion Detection Approach for Small-Sized Networks. In: Smys S., Balas V.E., Palanisamy R. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 336. Springer, Singapore. https://doi.org/10.1007/978-981-16-6723-7_67 |
| A. N. M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, and Paul Haskell-Dowland. 2022. Anomaly Detection in Cybersecurity Datasets via Cooperative Co-evolution-based Feature Selection. ACM Trans. Manage. Inf. Syst. 13, 3, Article 29 (September 2022), 39 pages. https://doi.org/10.1145/3495165 |
| Arora, Pallavi, Baljeet Kaur, and Marcio Andrey Teixeira. "HOME NETWORK SECURITY INCORPORATING MACHINE LEARNING ALGORITHMS IN INTERNET OF MEDICAL THINGS." networks 7 (2021): 8. |
| Huu-Khoi Bui, Ying-Dar Lin, Ren-Hung Hwang, Po-Ching Lin, Van-Linh Nguyen, Yuan-Cheng Lai, CREME: A toolchain of automatic dataset collection for machine learning in intrusion detection, Journal of Network and Computer Applications, Volume 193, 2021, 103212, ISSN 1084-8045. (https://www.sciencedirect.com/science/article/pii/S1084804521002137) |
| Andrea Venturi, Giovanni Apruzzese, Mauro Andreolini, Michele Colajanni, Mirco Marchetti, DReLAB - Deep REinforcement Learning Adversarial Botnet: A benchmark dataset for adversarial attacks against botnet Intrusion Detection Systems, Data in Brief, Volume 34, 2021, 106631, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2020.106631. |
| Norouzian, M.R., Xu, P., Eckert, C., Zarras, A. (2021). Hybroid: Toward Android Malware Detection and Categorization with Program Code and Network Traffic. In: Liu, J.K., Katsikas, S., Meng, W., Susilo, W., Intan, R. (eds) Information Security. ISC 2021. Lecture Notes in Computer Science(), vol 13118. Springer, Cham. https://doi.org/10.1007/978-3-030-91356-4_14 |
| Nazar Waheed, Xiangjian He, Muhammad Ikram, Muhammad Usman, Saad Sajid Hashmi, and Muhammad Usman. 2020. Security and Privacy in IoT Using Machine Learning and Blockchain: Threats and Countermeasures. ACM Comput. Surv. 53, 6, Article 122 (November 2021), 37 pages. https://doi.org/10.1145/3417987 |
| Jassim Happa, Ioannis Agrafiotis, Martin Helmhout, Thomas Bashford-Rogers, Michael Goldsmith, and Sadie Creese. 2021. Assessing a Decision Support Tool for SOC Analysts. Digital Threats 2, 3, Article 22 (September 2021), 35 pages. https://doi.org/10.1145/3430753 |
| Siddharth Bhatia, Arjit Jain, Pan Li, Ritesh Kumar, and Bryan Hooi. 2021. MStream: Fast Anomaly Detection in Multi-Aspect Streams. In Proceedings of the Web Conference 2021 (WWW '21). Association for Computing Machinery, New York, NY, USA, 3371–3382. https://doi.org/10.1145/3442381.3450023 |
| E. Tufan, C. Tezcan and C. Acartürk, "Anomaly-Based Intrusion Detection by Machine Learning: A Case Study on Probing Attacks to an Institutional Network," in IEEE Access, vol. 9, pp. 50078-50092, 2021, doi: 10.1109/ACCESS.2021.3068961. |
| Yulduz Khodjaeva and Nur Zincir-Heywood. 2021. Network Flow Entropy for Identifying Malicious Behaviours in DNS Tunnels. In Proceedings of the 16th International Conference on Availability, Reliability and Security (ARES '21). Association for Computing Machinery, New York, NY, USA, Article 72, 1–7. https://doi.org/10.1145/3465481.3470089 |
| J. L. Leevy, J. Hancock, T. M. Khoshgoftaar and J. M. Peterson, "An Easy-to-Classify Approach for the Bot-IoT Dataset," 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), Atlanta, GA, USA, 2021, pp. 172-179, doi: 10.1109/CogMI52975.2021.00031. |
| Idrissi, I., Azizi, M. and Moussaoui, O., 2021. Accelerating the update of a DL-based IDS for IoT using deep transfer learning. Indonesian Journal of Electrical Engineering and Computer Science, 23(2), pp.1059-1067. |
| Prabhat Kumar, Rakesh Tripathi, and Govind P. Gupta. 2021. P2IDF: A Privacy-Preserving based Intrusion Detection Framework for Software Defined Internet of Things-Fog (SDIoT-Fog). In Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking (ICDCN '21). Association for Computing Machinery, New York, NY, USA, 37–42. https://doi.org/10.1145/3427477.3429989 |
| Gautam Srivastava, Thippa Reddy G, N. Deepa, B. Prabadevi, and Praveen Kumar Reddy M. 2021. An ensemble model for intrusion detection in the Internet of Softwarized Things. In Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking (ICDCN '21). Association for Computing Machinery, New York, NY, USA, 25–30. https://doi.org/10.1145/3427477.3429987 |
| Ertza Warraich and Muhammad Shahbaz. 2021. Constructing the face of network data. In Proceedings of the SIGCOMM '21 Poster and Demo Sessions (SIGCOMM '21). Association for Computing Machinery, New York, NY, USA, 21–23. https://doi.org/10.1145/3472716.3472852 |
| Safari Khatouni, A., Seddigh, N., Nandy, B. et al. Machine Learning Based Classification Accuracy of Encrypted Service Channels: Analysis of Various Factors. J Netw Syst Manage 29, 8 (2021). https://doi.org/10.1007/s10922-020-09566-5 |
| Dylan Chou and Meng Jiang. 2021. A Survey on Data-driven Network Intrusion Detection. ACM Comput. Surv. 54, 9, Article 182 (December 2022), 36 pages. https://doi.org/10.1145/3472753 |
| Andrea Corsini, Shanchieh Jay Yang, and Giovanni Apruzzese. 2021. On the Evaluation of Sequential Machine Learning for Network Intrusion Detection. In The 16th International Conference on Availability, Reliability and Security (ARES 2021), August 17–20, 2021, Vienna, Austria. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3465481.3470065 |
| Cheng H., Shen Y., Cheng T., Fang Y., Ling J. (2021) Botnet Detection Based on Multilateral Attribute Graph. In: Lu W., Sun K., Yung M., Liu F. (eds) Science of Cyber Security. SciSec 2021. Lecture Notes in Computer Science, vol 13005. Springer, Cham. https://doi.org/10.1007/978-3-030-89137-4_5 |
| Ilievski G, Latkoski P. Network Traffic Classification in an NFV Environment using Supervised ML Algorithms. Journal of Telecommunications and Information Technology. 2021;23–31. |
| Y. Song, W. Luo, J. Li, P. Xu and J. Wei, "SDN-based Industrial Internet Security Gateway," 2021 International Conference on Security, Pattern Analysis, and Cybernetics（SPAC), 2021, pp. 238-243, doi: 10.1109/SPAC53836.2021.9539961. |
| Sirajuddin Qureshi, Saima Tunio, Faheem Akhtar, Ahsan Wajahat, Ahsan Nazir, Faheem Ullah. Network Forensics: A Comprehensive Review of Tools and Techniques. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 5, 2021 |
| Clark, D. and Turnbull, B. Interactive 3D Visualization of Network Traffic in Time for Forensic Analysis. DOI: 10.5220/0008950601770184 In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 3: IVAPP, pages 177-184 ISBN: 978-989-758-402-2; ISSN: 2184-4321 |
| Nour Moustafa and Alireza Jolfaei. 2020. Autonomous detection of malicious events using machine learning models in drone networks. In Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond (DroneCom '20). Association for Computing Machinery, New York, NY, USA, 61–66. https://doi.org/10.1145/3414045.3415951 |
| Jinxin Liu, Burak Kantarci, and Carlisle Adams. 2020. Machine learning-driven intrusion detection for Contiki-NG-based IoT networks exposed to NSL-KDD dataset. In Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning (WiseML '20). Association for Computing Machinery, New York, NY, USA, 25–30. https://doi.org/10.1145/3395352.3402621 |
| Mubarak Albarka Umar, Chen Zhanfang, and Yan Liu. 2020. Network Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection. In Proceedings of the 2020 International Conference on Internet Computing for Science and Engineering (ICICSE '20). Association for Computing Machinery, New York, NY, USA, 5–13. https://doi.org/10.1145/3424311.3424330 |
| Kay Boldt, Kenneth B. Kent, and Rainer Herpers. 2020. Investigation of encrypted and obfuscated network traffic utilizing machine learning. In Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering (CASCON '20). IBM Corp., USA, 43–52. |
| Torgeir Fladby, Hårek Haugerud, Stefano Nichele, Kyrre Begnum, and Anis Yazidi. 2020. Evading a Machine Learning-based Intrusion Detection System through Adversarial Perturbations. In Proceedings of the International Conference on Research in Adaptive and Convergent Systems (RACS '20). Association for Computing Machinery, New York, NY, USA, 161–166. https://doi.org/10.1145/3400286.3418252 |
| A. A. Hady, A. Ghubaish, T. Salman, D. Unal and R. Jain, "Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study," in IEEE Access, vol. 8, pp. 106576-106584, 2020, doi: 10.1109/ACCESS.2020.3000421. |
| Manas Kumar Yogi and KVV Subba Rao, Impact analysis of using ML techniques on imbalanced datasets for leveraging security of industrial IoT. International Journal of Circuit, Computing and Networking 2020; 2(2): 41-46 |
| Chaouki Khammassiab, Saoussen Krichena, "A NSGA2-LR Wrapper Approach for Feature Selection in Network Intrusion Detection". Computer Networks. Volume 172, 8 May 2020, 107183. ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2020.107183 |
| Rajagopal S., Hareesha K.S., Kundapur P.P. (2020) Feature Relevance Analysis and Feature Reduction of UNSW NB-15 Using Neural Networks on MAMLS. In: Pati B., Panigrahi C., Buyya R., Li KC. (eds) Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1082. Springer, Singapore |
| Abirami M.S., Yash U., Singh S. (2020) Building an Ensemble Learning Based Algorithm for Improving Intrusion Detection System. In: Dash S., Lakshmi C., Das S., Panigrahi B. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 1056. Springer, Singapore |
| Almogren, Ahmad S. Intrusion Detection in Edge-of-Things Computing. Journal of Parallel and Distributed Computing, Volume 137, March 2020, Pages 259-265. |
| Gjorgji ILIEVSKI , Pero LATKOSKI. Efficiency of Supervised Machine Learning Algorithms in Regular and Encrypted VoIP Classification within NFV Environment, RADIOENGINEERING, VOL. 29, NO. 1, APRIL 2020, 243-250. |
| Gupta N., Bedi P., Jindal V. (2020) Effect of Activation Functions on the Performance of Deep Learning Algorithms for Network Intrusion Detection Systems. In: Singh P., Panigrahi B., Suryadevara N., Sharma S., Singh A. (eds) Proceedings of ICETIT 2019. Lecture Notes in Electrical Engineering, vol 605. Springer, Cham |
| Dwivedi, S., Vardhan, M. & Tripathi, S. Incorporating evolutionary computation for securing wireless network against cyberthreats. J Supercomput (2020). https://doi.org/10.1007/s11227-020-03161-w |
| Molina-Coronado, B., Mori, U., Mendiburu, A., & Miguel-Alonso, J. (2020). Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process. ArXiv, abs/2001.09697. |
| Apruzzese, G.; Andreolini, M.; Marchetti, M.; Colacino, V.G.; Russo, G. AppCon: Mitigating Evasion Attacks to ML Cyber Detectors. Symmetry 2020, 12, 653. |
| J. Aiken and S. Scott-Hayward, "Investigating Adversarial Attacks against Network Intrusion Detection Systems in SDNs," 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 2019, pp. 1-7, doi: 10.1109/NFV-SDN47374.2019.9040101. |
| Haney M. (2019) Leveraging Cyber-Physical System Honeypots to Enhance Threat Intelligence. In: Staggs J., Shenoi S. (eds) Critical Infrastructure Protection XIII. ICCIP 2019. IFIP Advances in Information and Communication Technology, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-030-34647-8_11 |
| Guo, H., Fan, X., Cao, A., Outhred, G., & Heidemann, J.S. (2019). Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud. ArXiv, abs/1912.05590. |
| D. C. Le and N. Zincir-Heywood, "Learning From Evolving Network Data for Dependable Botnet Detection," 2019 15th International Conference on Network and Service Management (CNSM), 2019, pp. 1-5, doi: 10.23919/CNSM46954.2019.9012710. |
| M. Zolanvari, M. A. Teixeira, L. Gupta, K. M. Khan and R. Jain, "Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things," in IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6822-6834, Aug. 2019. |
| Robert A. Bridges, Tarrah R Glass-Vanderlan, Michael D Iannacone, Maria S Vincent, Qian (Guenevere) Chen, A Survey of Intrusion Detection Systems Leveraging Host Data. ACM Computing Surveys, November 2019 Article No.: 128 https://doi.org/10.1145/3344382 |
| C. R. Taylor and J. P. Lanson, "Network-based Classification of Authentication Attempts using Machine Learning," 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA, 2019, pp. 669-673 |
| Tuan, T.A., Long, H.V., Son, L.H. et al. Performance evaluation of Botnet DDoS attack detection using machine learning. Evol. Intel. (2019). https://doi.org/10.1007/s12065-019-00310-w |
| Chapaneri R., Shah S. (2019) A Comprehensive Survey of Machine Learning-Based Network Intrusion Detection. In: Satapathy S., Bhateja V., Das S. (eds) Smart Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol 104. Springer, Singapore |
| Fan Zhang, J. Wesley Hines & Jamie B. Coble (2019) A Robust Cybersecurity Solution Platform Architecture for Digital Instrumentation and Control Systems in Nuclear Power Facilities, Nuclear Technology, DOI: 10.1080/00295450.2019.1666599 |
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| L. Xiao, J. Gerth, P. Hanrahan, Enhancing Visual Analysis of Network Traffic Using a Knowledge Representation, Visual Analytics Science And Technology, 2006 IEEE Symposium On, p 107-114, Oct 31 - Nov 2, 2006, Baltimore, MD, USA [doi>10.1109/VAST.2006.261436] |
| Javier Verdú , Jorge Garcí , Mario Nemirovsky , Mateo Valero, Architectural impact of stateful networking applications, Proceedings of the 2005 ACM symposium on Architecture for networking and communications systems, October 26-28, 2005, Princeton, NJ, USA [doi>10.1145/1095890.1095893] |
| William Yurcik, Visualizing NetFlows for security at line speed: the SIFT tool suite, Proceedings of the 19th conference on Large Installation System Administration Conference, p.16-16, December 04-09, 2005, San Diego, CA |
| Kiran Lakkaraju , William Yurcik , Adam J. Lee, NVisionIP: netflow visualizations of system state for security situational awareness, Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security, October 29-29, 2004, Washington DC, USA [doi>10.1145/1029208.1029219] |
| Dogu Arifler , Gustavo de Veciana , Brian L. Evans, A factor analytic approach to inferring congestion sharing based on flow level measurements, IEEE/ACM Transactions on Networking (TON), v.15 n.1, p.67-79, February 2007 [doi>10.1109/TNET.2006.890103] |
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| Sun-Myung Hwang, "P2P Protocol Analysis and Blocking Algorithm", Computational Science and Its Applications – ICCSA 2005 Lecture Notes in Computer Science Volume 3481, 2005, pp 21-30 |
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Books
| C Sanders, J Smith, Applied Network Security Monitoring: Collection, Detection, and Analysis, Waltham, MA, Syngress, 2014. |
| R. Bejtlich, The Practice of Network Security Monitoring: Understanding Incident Detection and Response, San Francisco: No Starch Press, 2013. |
| Wireless Networking in the Developing World 3rd Edition, http://wndw.net, 2013. |
| S. Davidoff, J. Ham, Network Forensics: Tracking Hackers through Cyberspace, Prentice Hall, 1st Edition, 2012. |
| J. Vacca, Managing Information Secuirty, Syngress Publishing, 2010. |
| R. Marty, Applied Security Visualization, New York:Addison-Wesley Professional, Aug 2008. |
| A. Lockhart, Network Security Hacks 2nd Edition, O'Reilly Media, Inc., Sestaphol, CA, USA 2007. |
| J Babbin, et. al., Security Log Management: Identifying Patterns in the Chaos 2nd Edition, Syngress Publishing, Inc., Rockland, MA, USA 2006. |
| Flickenger R.; Belcher M.; Canessa E.; Zennaro M, How to Accelerate Your Internet: A practical Guide to Bandwidth Management and Optimisation Using Open Source Software Oxford: INASP/ICTP. ISBN: 0-9778093-1-5. 2006. |
| V Oppleman, O Friedrichs and B Watson, Extreme Exploits: Advanced Defenses Against Hardcore Hacks (Hacking Exposed), McGraw-Hill Osborne Media; 1 edition July 18, 2005. |
| R. Bejtlich, Extrusion Detection : Security Monitoring for Internal Intrusions, New York:Addison-Wesley, November 2005. |
| I. Ristic, Apache Security, O'Reilly Media Inc., Sebastopol, CA, USA, 2005. |
| R. Bejtlich, The Tao of Network Security Monitoring: Beyond Intrusion Detection , New York:Addison-Wesley, 2004. |
| D. Farmer, W. Venema, Forensic Discovery, New York:Addison-Wesley, 2004. |
| J. Nazario, Defense and Detection Strategies against Internet Worms, Boston:Artech House, 2004. |
| Eoghan Casey, Digital Evidence and Computer Crime 2nd Edition, Academic Press, Inc., Orlando, FL, 2004. |