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 This email address is being protected from spambots. You need JavaScript enabled to view it..
| F. Zola, L. Segurola-Gil, J.L. Bruse, M. Galar, R. Orduna-Urrutia, Network traffic analysis through node behaviour classification: a graph-based approach with temporal dissection and data-level preprocessing, Computers & Security, Volume 115, 2022, 102632, ISSN 0167-4048, https://doi.org/10.1016/j.cose.2022.102632. |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| F. A. Khan, A. Gumaei, A. Derhab and A. Hussain, "A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection," in IEEE Access, vol. 7, pp. 30373-30385, 2019. |
| S. Khanchi, N. Zincir-Heywood and M. Heywood, "Network Analytics for Streaming Traffic Analysis," 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), 2019, pp. 25-30. |
| G. Apruzzese, M. Colajanni and M. Marchetti, "Evaluating the effectiveness of Adversarial Attacks against Botnet Detectors," 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA), 2019, pp. 1-8, doi: 10.1109/NCA.2019.8935039. |
| Nickolaos Koroniotis, Nour Moustafa, Elena Sitnikova, Benjamin Turnbull, Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset, Future Generation Computer Systems, Volume 100, 2019, Pages 779-796, ISSN 0167-739X, https://doi.org/10.1016/j.future.2019.05.041. |
| Pektaş, A, Acarman, T. A deep learning method to detect network intrusion through flow‐based features. Int J Network Mgmt. 2019; 29:e2050. https://doi.org/10.1002/nem.2050 |
| A. S. Khatouni and N. Zincir-Heywood, "Integrating Machine Learning with Off-the-Shelf Traffic Flow Features for HTTP/HTTPS Traffic Classification," 2019 IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain, 2019, pp. 1-7. |
| A. S. Khatouni, L. Zhang, K. Aziz, I. Zincir and N. Zincir-Heywood, "Exploring NAT Detection and Host Identification Using Machine Learning," 2019 15th International Conference on Network and Service Management (CNSM), Halifax, NS, Canada, 2019, pp. 1-8. |
| R. R. Karn, P. Kudva and I. A. M. Elfadel, "Dynamic Autoselection and Autotuning of Machine Learning Models for Cloud Network Analytics," in IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 5, pp. 1052-1064, 1 May 2019. |
| Andreoni Lopez, M, Mattos, DMF, Duarte, OCMB, Pujolle, G. Toward a monitoring and threat detection system based on stream processing as a virtual network function for big data. Concurrency Computat Pract Exper. 2019; 31:e5344. https://doi.org/10.1002/cpe.5344 |
| D. Zhuang and J. M. Chang, "Enhanced PeerHunter: Detecting Peer-to-Peer Botnets Through Network-Flow Level Community Behavior Analysis," in IEEE Transactions on Information Forensics and Security, vol. 14, no. 6, pp. 1485-1500, June 2019. doi: 10.1109/TIFS.2018.2881657 |
| Osama Faker and Erdogan Dogdu. 2019. Intrusion Detection Using Big Data and Deep Learning Techniques. In Proceedings of the 2019 ACM Southeast Conference (ACM SE '19). ACM, New York, NY, USA, 86-93. DOI: https://doi.org/10.1145/3299815.3314439 |
| P. Mishra, V. Varadharajan, U. Tupakula and E. S. Pilli, "A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection," in IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 686-728, Firstquarter 2019. |
| X. Wu, T. Miskell, Y. Luo, L. Wang and L. Chen, "Edison: Event-driven Distributed System of Network Measurement," 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Arlington, VA, USA, 2019, pp. 545-550. |
| N. Moustafa, B. Turnbull and K. R. Choo, "Towards Automation of Vulnerability and Exploitation Identification in IIoT Networks," 2018 IEEE International Conference on Industrial Internet (ICII), 2018, pp. 139-145, doi: 10.1109/ICII.2018.00023. |
| Marcio Andrey Teixeira, Tara Salman, Maede Zolanvari, Raj Jain, Nader Meskin and Mohammed Samaka. 2018. SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach. Future Internet 2018, 10(8), 76; https://doi.org/10.3390/fi10080076 |
| A. Divekar, M. Parekh, V. Savla, R. Mishra and M. Shirole, "Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives," 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), Kathmandu, 2018, pp. 1-8. |
| Sean Peisert, Eli Dart, William Barnett, Edward Balas, James Cuff, Robert L Grossman, Ari Berman, Anurag Shankar, Brian Tierney. 2018. The medical science DMZ: a network design pattern for data-intensive medical science. Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 267–274, https://doi.org/10.1093/jamia/ocx104. |
| Stephanie Ding. 2018. Machine Learning for Cybersecurity: Network-based Botnet Detection Using Time-Limited Flows. Caltech Undergraduate Research Journal, July, 2018. |
| S. Khanchi, N. Zincir-Heywood and M. Heywood, "Streaming Botnet traffic analysis using bio-inspired active learning," NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium, 2018, pp. 1-6, doi: 10.1109/NOMS.2018.8406293. |
| M. Zolanvari, M. A. Teixeira and R. Jain, "Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning," 2018 IEEE International Conference on Intelligence and Security Informatics (ISI), Miami, FL, 2018, pp. 112-117. |
| Verma, A., Ranga, V. Evaluation of Network Intrusion Detection Systems for RPL Based 6LoWPAN Networks in IoT. Wireless Pers Commun 108, 1571–1594 (2019). https://doi.org/10.1007/s11277-019-06485-w |
| Homayoun S., Ahmadzadeh M., Hashemi S., Dehghantanha A., Khayami R. (2018) BoTShark: A Deep Learning Approach for Botnet Traffic Detection. In: Dehghantanha A., Conti M., Dargahi T. (eds) Cyber Threat Intelligence. Advances in Information Security, vol 70. Springer, Cham |
| Atli, B.G., Miche, Y., Kalliola, A. et al. Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space, Cognitive Computing (2018) 10: 848. https://doi.org/10.1007/s12559-018-9564-y. |
| Pektaş, A, Acarman, T. Botnet detection based on network flow summary and deep learning. Int J Network Mgmt. 2018; 28:e2039. https://doi.org/10.1002/nem.2039 |
| Sean Peisert, Eli Dart, William Barnett, Edward Balas, James Cuff, Robert L Grossman, Ari Berman, Anurag Shankar, Brian Tierney. 2018. The medical science DMZ: a network design pattern for data-intensive medical science. Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 267–274, https://doi.org/10.1093/jamia/ocx104 |
| Nascimento, Zuleika and Djamel Fawzi Hadj Sadok. “MODC: A Pareto-Optimal Optimization Approach for Network Traffic Classification Based on the Divide and Conquer Strategy.” Information 9 (2018): 233. |
| Meghdouri, Fares, Tanja Zseby and Félix Iglesias. “Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic.” (2018). |
| B. G. Atli, Y. Miche and A. Jung, "Network Intrusion Detection Using Flow Statistics," 2018 IEEE Statistical Signal Processing Workshop (SSP), Freiburg, 2018, pp. 70-74. doi: 10.1109/SSP.2018.8450709 |
| Meghdouri, Fares; Zseby, Tanja; Iglesias, Félix. 2018. "Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic." Appl. Sci. 8, no. 11: 2196. |
| Koroniotis N., Moustafa N., Sitnikova E., Slay J. (2018) Towards Developing Network Forensic Mechanism for Botnet Activities in the IoT Based on Machine Learning Techniques. In: Hu J., Khalil I., Tari Z., Wen S. (eds) Mobile Networks and Management. MONAMI 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 235. Springer, Cham |
| Y. Wan, J. Chang, R. Chen and S. Wang, "Feature-Selection-Based Ransomware Detection with Machine Learning of Data Analysis," 2018 3rd International Conference on Computer and Communication Systems (ICCCS), Nagoya, 2018, pp. 85-88. doi: 10.1109/CCOMS.2018.8463300 |
| M. J. Vargas-Muñoz, R. Martínez-Peláez, P. Velarde-Alvarado, E. Moreno-García, D. L. Torres-Roman and J. J. Ceballos-Mejía, "Classification of network anomalies in flow level network traffic using Bayesian networks," 2018 International Conference on Electronics, Communications and Computers (CONIELECOMP), Cholula, 2018, pp. 238-243. doi: 10.1109/CONIELECOMP.2018.8327205 |
| Chowdhury, S., Khanzadeh, M., Akula, R. et al. Botnet detection using graph-based feature clustering. Journal of Big Data (2017) 4: 14. https://doi.org/10.1186/s40537-017-0074-7 |
| Cho B., Kim K.J., Kim H. (2018) The Isolation Algorithm of Problem Location with Multi-agent Approach for End-to-End Network Performance Management. In: Kim K., Joukov N. (eds) Mobile and Wireless Technologies 2017. ICMWT 2017. Lecture Notes in Electrical Engineering, vol 425. Springer, Singapore |
| Bhuyan M.H., Bhattacharyya D.K., Kalita J.K. (2017) Practical Tools for Attackers and Defenders. In: Network Traffic Anomaly Detection and Prevention. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-65188-0_6 |
| O. Yavanoglu and M. Aydos, "A review on cyber security datasets for machine learning algorithms," 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, 2017, pp. 2186-2193. |
| Ankit Bansal and Sudipta Mahapatra. 2017. A comparative analysis of machine learning techniques for botnet detection. In Proceedings of the 10th International Conference on Security of Information and Networks (SIN '17). ACM, New York, NY, USA, 91-98. DOI: https://doi.org/10.1145/3136825.3136874 |
| T. Salman, D. Bhamare, A. Erbad, R. Jain and M. Samaka, "Machine Learning for Anomaly Detection and Categorization in Multi-Cloud Environments," 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), New York, NY, 2017, pp. 97-103. |
| Luis Miguel Torres, Eduardo Magaña, Daniel Morató, Santiago Garcia-Jimenez, Mikel Izal, TBDClust: Time-based density clustering to enable free browsing of sites in pay-per-use mobile Internet providers. Journal of Network and Computer Applications Volume 99, 1 December 2017, Pages 17-27. ISSN 1084-8045, https://doi.org/10.1016/j.jnca.2017.10.007 |
| F. Haddadi, D. Phan and A. N. Zincir-Heywood, "How to choose from different botnet detection systems?," NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, Istanbul, 2016, pp. 1079-1084. doi: 10.1109/NOMS.2016.7502964 |
| Christopher R. Harshaw, Robert A. Bridges, Michael D. Iannacone, Joel W. Reed, and John R. Goodall. 2016. GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection. In Proceedings of the 11th Annual Cyber and Information Security Research Conference (CISRC '16). ACM, New York, NY, USA, Article 15, 4 pages. DOI: https://doi.org/10.1145/2897795.2897806 |
| D. Bhamare, T. Salman, M. Samaka, A. Erbad and R. Jain, "Feasibility of Supervised Machine Learning for Cloud Security," 2016 International Conference on Information Science and Security (ICISS), Pattaya, 2016, pp. 1-5. |
| Sebasitan Garcia. 2016. Modelling the network behaviour of malware to block malicious patterns. The Stratosphere Project: A Behavioural IPS. Virus Bulletin, Sept 2015. |
| Nour Moustafa and Jill Slay. 2016. The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Inf. Sec. J.: A Global Perspective 25, 1-3 (April 2016), 18-31. DOI: http://dx.doi.org/10.1080/19393555.2015.1125974 |
| Kayla M. Straub, Avik Sengupta, Joseph M. Ernst, Robert W. McGwier, Merrick Watchorn, Richard Tilley, and Randolph Marchany. 2016. Malware Propagation in Fully Connected Networks: A Netflow-Based Analysis. MILCOM 2016 - 2016 IEEE Military Communications Conference, Baltimore, MD, 2016, pp. 497-502. doi: 10.1109/MILCOM.2016.7795376. |
| Shing-Han Li, Yucheng Kao, Zongcyuan Zhang, Yingping Chuang, David C. Yen. A Network Behavior-Based Botnet Detection Mechanism Using PSO and K-means. ACM Transactions on Management Information Systems, April 2015. Article No.: 3 https://doi.org/10.1145/2676869 |
| Buseung Cho, Kyuil Kim, Hyungwoo Park and Jin-Wook Chung. Oct 2015. Network Flow Awareness System for E-Science Collaborative Application. Indian Journal of Science and Technology, Vol 8(26), DOI: 10.17485/ijst/2015/v8i26/81060. |
| H. Lim, Y. Yamaguchi, H. Shimada and H. Takakura, "Malware classification method based on sequence of traffic flow," 2015 International Conference on Information Systems Security and Privacy (ICISSP), Angers, 2015, pp. 1-8. |
| Serguei A. Mokhov, Michael J. Assels, Joey Paquet, and Mourad Debbabi. 2014. Toward Automated MAC Spoofer Investigations. In Proceedings of the 2014 International C* Conference on Computer Science; Software Engineering (C3S2E '14). ACM, New York, NY, USA, , Article 27 , 6 pages. DOI=10.1145/2641483.2641540 http://doi.acm.org/10.1145/2641483.2641540 |
| N. Hoque, Monowar H. Bhuyan, R.C. Baishya, D.K. Bhattacharyya, J.K. Kalita, Network attacks: Taxonomy, tools and systems, Journal of Network and Computer Applications, Volume 40, 2014, Pages 307-324, ISSN 1084-8045, https://doi.org/10.1016/j.jnca.2013.08.001. |
| S. García, M. Grill, J. Stiborek, and A. Zunino. 2014. An empirical comparison of botnet detection methods. Comput. Secur. 45 (September 2014), 100-123. DOI=10.1016/j.cose.2014.05.011 http://dx.doi.org/10.1016/j.cose.2014.05.011 |
| Pratik Narang, Abhishek Thakur, and Chittaranjan Hota. 2014. Hades: a Hadoop-based framework for detection of peer-to-peer botnets. In Proceedings of the 20th International Conference on Management of Data (COMAD '14). Computer Society of India, Mumbai, India, India, 121-124. |
| N. Hoque, Monowar H. Bhuyan, R. C. Baishya, D. K. Bhattacharyya, and J. K. Kalita. 2014. Review: Network attacks: Taxonomy, tools and systems. J. Netw. Comput. Appl. 40 (April 2014), 307-324. DOI=10.1016/j.jnca.2013.08.001 http://dx.doi.org/10.1016/j.jnca.2013.08.001 |
| Serguei A. Mokhov, Michael J. Assels, Joey Paquet, and Mourad Debbabi. 2014. Automating MAC Spoofer Evidence Gathering and Encoding for Investigations. In Foundations and Practice of Security: 7th International Symposium, FPS 2014, Montreal, QC, Canada, November 3-5, 2014. ISBN 3319170406, 9783319170404 |
| Serguei A. Mokhov, Michael J. Assels, Joey Paquet, and Mourad Debbabi. 2014. Toward Automated MAC Spoofer Investigations. In Proceedings of the 2014 International C* Conference on Computer Science & Software Engineering (C3S2E '14). ACM, New York, NY, USA, Article 27, 6 pages. DOI: https://doi.org/10.1145/2641483.2641540 |
| García, S. , Zunino, A. and Campo, M. (2014), Survey on network‐based botnet detection methods. Security Comm. Networks, 7: 878-903. doi:10.1002/sec.800 |
| Skrzewski M. (2013) Monitoring System’s Network Activity for Rootkit Malware Detection. In: Kwiecień A., Gaj P., Stera P. (eds) Computer Networks. CN 2013. Communications in Computer and Information Science, vol 370. Springer, Berlin, Heidelberg |
| Jin, Hongying & Li, Linhao. (2013). Dynamic Network Traffic Flow Prediction Model based on Modified Quantum-Behaved Particle Swarm Optimization. Journal of Networks. 8. 2332-2339. 10.4304/jnw.8.10.2332-2339. |
| Mansour Alsaleh, Abdullah Alqahtani, Abdulrahman Alarifi, and AbdulMalik Al-Salman. 2013. Visualizing PHPIDS log files for better understanding of web server attacks. In Proceedings of the Tenth Workshop on Visualization for Cyber Security (VizSec '13), John Goodall, Kwan-Liu Ma, Sophie Engle, and Fabian Fischer (Eds.). ACM, New York, NY, USA, 1-8. DOI=10.1145/2517957.2517958 http://doi.acm.org/10.1145/2517957.2517958 |
| P. Celeda, P. Velan, M. Rabek, R. Hofstede, and A. Pras, Large-scale geolocation for NetFlow. Proceedings of IM. 2013, 1015-1020. |
| Nichole Boscia. 2012. Flow Analysis Tool Whitepaper. https://www.nas.nasa.gov/assets/pdf/papers/boscia_n_flow_analysis_tools_2012.pdf |
| R. Hunt, "New developments in network forensics — Tools and techniques," 2012 18th IEEE International Conference on Networks (ICON), Singapore, 2012, pp. 376-381. doi: 10.1109/ICON.2012.6506587 |
| Amit Kumar Tyagi and Sadique Nayeem. Article: Detecting HTTP Botnet using Artificial Immune System (AIS). International Journal of Applied Information Systems 2(6):34-37, May 2012. Published by Foundation of Computer Science, New York, USA. |
| H. Li, G. Hu, J. Yuan and H. Lai, "P2P Botnet Detection Based on Irregular Phased Similarity," 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control, Harbin, 2012, pp. 79-82. doi: 10.1109/IMCCC.2012.25 |
| Yeonhee Lee and Youngseok Lee. 2012. Toward scalable internet traffic measurement and analysis with Hadoop. SIGCOMM Comput. Commun. Rev. 43, 1 (January 2012), 5-13. DOI=10.1145/2427036.2427038 http://doi.acm.org/10.1145/2427036.2427038 |
| Rodrigo M. P. Silva and Ronaldo M. Salles. 2012. Methodology for detection and restraint of p2p applications in the network. In Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part IV (ICCSA'12), Beniamino Murgante, Osvaldo Gervasi, Sanjay Misra, Nadia Nedjah, and Ana C. Rocha (Eds.), Vol. Part IV. Springer-Verlag, Berlin, Heidelberg, 326-339. DOI=10.1007/978-3-642-31128-4_24 http://dx.doi.org/10.1007/978-3-642-31128-4_24 |
| Monowar H. Bhuyan, D.K. Bhattacharyya, J.K. Kalita, Surveying Port Scans and Their Detection Methodologies, The Computer Journal, Volume 54, Issue 10, October 2011, Pages 1565–1581, https://doi.org/10.1093/comjnl/bxr035 |
| Skrzewski M. (2011) Analyzing Outbound Network Traffic. In: Kwiecień A., Gaj P., Stera P. (eds) Computer Networks. CN 2011. Communications in Computer and Information Science, vol 160. Springer, Berlin, Heidelberg |
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