{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T05:31:47Z","timestamp":1783402307207,"version":"3.54.6"},"reference-count":26,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T00:00:00Z","timestamp":1581379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009226","name":"National Security Agency","doi-asserted-by":"publisher","award":["13000667"],"award-info":[{"award-number":["13000667"]}],"id":[{"id":"10.13039\/100009226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The cyber security toolkit, CyberSecTK, is a simple Python library for preprocessing and feature extraction of cyber-security-related data. As the digital universe expands, more and more data need to be processed using automated approaches. In recent years, cyber security professionals have seen opportunities to use machine learning approaches to help process and analyze their data. The challenge is that cyber security experts do not have necessary trainings to apply machine learning to their problems. The goal of this library is to help bridge this gap. In particular, we propose the development of a toolkit in Python that can process the most common types of cyber security data. This will help cyber experts to implement a basic machine learning pipeline from beginning to end. This proposed research work is our first attempt to achieve this goal. The proposed toolkit is a suite of program modules, data sets, and tutorials supporting research and teaching in cyber security and defense. An example of use cases is presented and discussed. Survey results of students using some of the modules in the library are also presented.<\/jats:p>","DOI":"10.3390\/info11020100","type":"journal-article","created":{"date-parts":[[2020,2,11]],"date-time":"2020-02-11T09:25:21Z","timestamp":1581413121000},"page":"100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Cyber Security Tool Kit (CyberSecTK): A Python Library for Machine Learning and Cyber Security"],"prefix":"10.3390","volume":"11","author":[{"given":"Ricardo A.","family":"Calix","sequence":"first","affiliation":[{"name":"Purdue University Northwest, Hammond, IN 46323, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumendra B.","family":"Singh","sequence":"additional","affiliation":[{"name":"Purdue University Northwest, Hammond, IN 46323, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Purdue University Northwest, Hammond, IN 46323, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingkai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Purdue University Northwest, Hammond, IN 46323, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Tu","sequence":"additional","affiliation":[{"name":"Purdue University Northwest, Hammond, IN 46323, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,11]]},"reference":[{"key":"ref_1","unstructured":"(2019, December 14). IOT Statistics. Available online: https:\/\/ipropertymanagement.com\/iot-statistics."},{"key":"ref_2","unstructured":"(2019, December 14). IDC Forecasts WorldWide Technology Spending on the Internet of Things to Reach $1.2 Trillion in 2022. Available online: https:\/\/www.idc.com\/getdoc.jsp?containerId=prUS43994118."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MC.2017.201","article-title":"DDoS in the IoT: Mirai and other botnets","volume":"50","author":"Kolias","year":"2017","journal-title":"Computer"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Spathoulas, G., Evangelatos, S., Anagnostopoulos, M., Mema, G., and Katsikas, S. (2019, January 20\u201322). Detection of abnormal behavior in smart-home environments. Proceedings of the 2019 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Piraeus, Greece.","DOI":"10.1109\/SEEDA-CECNSM.2019.8908352"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MNET.2018.1700202","article-title":"Learning IoT in edge: Deep learning for the Internet of Things with edge computing","volume":"32","author":"Li","year":"2018","journal-title":"IEEE Network"},{"key":"ref_6","first-page":"93","article-title":"Data Preprocessing and Feature Selection for Machine Learning Intrusion Detection Systems","volume":"13","author":"Tohari","year":"2019","journal-title":"ICIC Express Lett."},{"key":"ref_7","unstructured":"(2020, February 10). Course. Available online: http:\/\/www.ricardocalix.com\/teaching\/teaching.htm."},{"key":"ref_8","unstructured":"(2020, February 10). Keras. Available online: www.keras.io."},{"key":"ref_9","unstructured":"(2020, February 10). Tensorflow. Available online: www.tensorflow.org."},{"key":"ref_10","unstructured":"Pedregosa (2011). Scikit-learn: Machine Learning in Python. JMLR, 12, 2825\u20132830."},{"key":"ref_11","unstructured":"(2020, February 10). Pandas. Available online: www.pandas.pydata.org."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Loper, E., and Bird, S. (2002, January 17). NLTK: the Natural Language Toolkit. Proceedings of the ETMTNLP \u201802 Proceedings of the ACL-02, Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics-Volume 1, Stroudsburg, PA, USA.","DOI":"10.3115\/1118108.1118117"},{"key":"ref_13","unstructured":"Calix, R.A., and Sankaran, R. (2013, January 22\u201324). Feature ranking and support vector machines classification analysis of the NSL-KDD intrusion detection corpus. Proceedings of the The Twenty-Sixth International FLAIRS Conference, St. Pete Beach, FL, USA."},{"key":"ref_14","unstructured":"Cabrera, A., and Calix, R.A. (November, January 31). On the Anatomy of the Dynamic Behavior of Polymorphic Viruses. Proceedings of the International Conference on Collaboration Technologies and Systems (CTS), Orlando, FL, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Prasad, B. (2008). Detection of Phishing Attacks: A Machine Learning Approach. Soft Computing Applications in Industry. Studies in Fuzziness and Soft Computing, Springer.","DOI":"10.1007\/978-3-540-77465-5"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Amigud, A., Arnedo-Moreno, J., Daradoumis, T., and Guerrero-Roldan, A. (2016, January 7\u20139). A Behavioral Biometrics Based and Machine Learning Aided Framework for Academic Integrity in E-Assessment. Proceedings of the 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS), Ostrawva, Czech Republic.","DOI":"10.1109\/INCoS.2016.16"},{"key":"ref_17","unstructured":"Calix, R. (2017). Getting Started with Deep Learning: Programming and Methodologies using Python, CreateSpace Independent Publishing Platform. [1st ed.]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Iqbal, I.M., and Calix, R.A. (November, January 31). Analysis of a Payload-based Network Intrusion Detection System Using Pattern Recognition Processors. Proceedings of the 2016 International Conference on Collaboration Technologies and Systems (CTS), Orlando, FL, USA.","DOI":"10.1109\/CTS.2016.0077"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hamed, T., Ernst, J.B., and Kremer, S.C. (2018). A survey and taxonomy on data and pre-processing techniques of intrusion detection systems. Computer and Network Security Essentials, Springer.","DOI":"10.1007\/978-3-319-58424-9_7"},{"key":"ref_20","first-page":"8887","article-title":"Overview of malware analysis and detection","volume":"975","author":"Aziz","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Firdausi, I., Erwin, A., and Nugroho, A.S. (2010, January 2\u20133). Analysis of machine learning techniques used in behavior-based malware detection. Proceedings of the 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies, Jakarta, Indonesia.","DOI":"10.1109\/ACT.2010.33"},{"key":"ref_22","unstructured":"(2020, February 10). CyberSecTK. Available online: https:\/\/github.com\/sumendrabsingh\/CyberSecTK-Library."},{"key":"ref_23","unstructured":"(2019, October 10). Scapy. Available online: https:\/\/scapy.net."},{"key":"ref_24","unstructured":"Rajib, R.M., Sandra, S., Ragv, S., and Nils, O.T. (2017). Link-Layer Device Type Classification on Encrypted Wireless Traffic with COTS Radios. European Symposium on Research in Computer Security, Springer. Part II."},{"key":"ref_25","unstructured":"(2019, October 10). Aircrack-ng. Available online: https:\/\/www.aircrack-ng.org."},{"key":"ref_26","first-page":"73","article-title":"Factors that Influence Usage of Knowledge Management by Information Technology Professionals at Institutions of Higher Education","volume":"3","author":"Calix","year":"2010","journal-title":"J. Manag. Eng. Integr."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:56:54Z","timestamp":1760173014000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,11]]},"references-count":26,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["info11020100"],"URL":"https:\/\/doi.org\/10.3390\/info11020100","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,11]]}}}