Kim-Kwang Raymond Choo received the Ph.D. in Information Security in 2006 from Queensland University of Technology, Australia. He currently holds the Cloud Technology Endowed Professorship at The University of Texas at San Antonio (UTSA). He is an IEEE Computer Society Distinguished Visitor (2021 - 2023), and a Web of Science's Highly Cited Researcher in the field of Cross-Field - 2020. In 2015, he and his team won the Digital Forensics Research Challenge organized by Germany's University of Erlangen-Nuremberg. He is the recipient of the 2019 IEEE Technical Committee on Scalable Computing (TCSC) Award for Excellence in Scalable Computing (Middle Career Researcher), the 2018 UTSA College of Business Col. Jean Piccione and Lt. Col. Philip Piccione Endowed Research Award for Tenured Faculty, the British Computer Society's 2019 Wilkes Award Runner-up, the 2014 Highly Commended Award by the Australia New Zealand Policing Advisory Agency, the Fulbright Scholarship in 2009, the 2008 Australia Day Achievement Medallion, and the British Computer Society's Wilkes Award in 2008. He has also received best paper awards from the IEEE Consumer Electronics Magazine for 2020, EURASIP Journal on Wireless Communications and Networking (JWCN) in 2019, IEEE TrustCom 2018, and ESORICS 2015; the Korea Information Processing Society's Journal of Information Processing Systems (JIPS) Survey Paper Award (Gold) 2019; the IEEE Blockchain 2019 Outstanding Paper Award; and Best Student Paper Awards from Inscrypt 2019 and ACISP 2005. Since receiving his PhD in 2011, Dr. Dehghantanha has made significant contributions to the fast-moving fields of cybersecurity and cyber threat intelligence. He is a Canada Research Chair in Cybersecurity and Threat Intelligence, and an EU Marie-Curie Fellow Alumni in digital forensics. Dr. Dehghantanha has pioneered the use of ML-based systems for threat hunting in IoT/ICS devices using physical characteristics(e.g. power consumption) as opposed to application-level characteristics (e.g. IP addresses). His works have resulted in an Intrusion Detection System (IDS) for IoT networks; and deep learning models for threat hunting in the edge layer of ICS networks. In 2019, with support from the Department of National Defense Canada, he has developed the first multi-view fuzzy machine learning system for cyber threat attribution. He is among few academics contributing to fundamental research in cyber threat intelligence, with most research taking place in industry settings. His work helps define this new discipline while informing practical strategies. He has built a Cyber Kill Chain-based threat intelligence framework for analyzing banking Trojan campaigns which is widely used to model different attack campaigns, including APT groups activities, analyzing crypto-ransomware campaigns, and analyzing Advanced Persistent Threat (APT) groups targeting critical national infrastructure. He is currently the director of Cyber Science Lab at the University of Guelph, Ontario, Canada.

• 1. Big data analytics and forensics: an overview.- 2. Lot privacy, security and forensics challenges: an unmanned aerial vehicle (uav) case study.- 3. Detection of enumeration attacks in cloud environments using infrastructure log data.- 4.- Cyber threat attribution with multi-view heuristic analysis.- 5. Security of industrial cyberspace: fair clustering with linear time approximation.- 6. Adaptive neural trees for attack detection in cyber physical systems.- 7. Evaluating performance of scalable fair clustering machine learning techniques in detecting cyber-attacks in industrial control systems.- 8. Fuzzy bayesian learning for cyber threat hunting in industrial control systems.- 9. Cyber-attack detection in cyber-physical systems using supervised machine learning.- 10. Evaluation of scalable fair clustering machine learning methods for threat hunting in  cyber-physical systems.- 11. Evaluation of supervised and unsupervised machine learning classifiers for mac os malware detection.- 12. Evaluation of machine learning algorithms on internet of things (iot) malware opcodes.- 13. Mac os x malware detection with supervised machine learning algorithms.- 14. Machine learning for osx malware detection.- 15. Hybrid analysis on credit card fraud detection using machine learning techniques.- 16. Mapping ckc model through nlp modelling for apt groups reports.- 17. Ransomware threat detection: a deep learning approach.- 18. Scalable fair clustering algorithm for internet of things malware classification.

Kim-Kwang Raymond Choo received the Ph.D. in Information Security in 2006 from Queensland University of Technology, Australia. He currently holds the Cloud Technology Endowed Professorship at The University of Texas at San Antonio (UTSA). He is an IEEE Computer Society Distinguished Visitor (2021 - 2023), and a Web of Science's Highly Cited Researcher in the field of Cross-Field - 2020. In 2015, he and his team won the Digital Forensics Research Challenge organized by Germany's University of Erlangen-Nuremberg. He is the recipient of the 2019 IEEE Technical Committee on Scalable Computing (TCSC) Award for Excellence in Scalable Computing (Middle Career Researcher), the 2018 UTSA College of Business Col. Jean Piccione and Lt. Col. Philip Piccione Endowed Research Award for Tenured Faculty, the British Computer Society's 2019 Wilkes Award Runner-up, the 2014 Highly Commended Award by the Australia New Zealand Policing Advisory Agency, the Fulbright Scholarship in 2009, the 2008 Australia Day Achievement Medallion, and the British Computer Society's Wilkes Award in 2008. He has also received best paper awards from the IEEE Consumer Electronics Magazine for 2020, EURASIP Journal on Wireless Communications and Networking (JWCN) in 2019, IEEE TrustCom 2018, and ESORICS 2015; the Korea Information Processing Society's Journal of Information Processing Systems (JIPS) Survey Paper Award (Gold) 2019; the IEEE Blockchain 2019 Outstanding Paper Award; and Best Student Paper Awards from Inscrypt 2019 and ACISP 2005. Since receiving his PhD in 2011, Dr. Dehghantanha has made significant contributions to the fast-moving fields of cybersecurity and cyber threat intelligence. He is a Canada Research Chair in Cybersecurity and Threat Intelligence, and an EU Marie-Curie Fellow Alumni in digital forensics. Dr. Dehghantanha has pioneered the use of ML-based systems for threat hunting in IoT/ICS devices using physical characteristics(e.g. power consumption) as opposed to application-level characteristics (e.g. IP addresses). His works have resulted in an Intrusion Detection System (IDS) for IoT networks; and deep learning models for threat hunting in the edge layer of ICS networks. In 2019, with support from the Department of National Defense Canada, he has developed the first multi-view fuzzy machine learning system for cyber threat attribution. He is among few academics contributing to fundamental research in cyber threat intelligence, with most research taking place in industry settings. His work helps define this new discipline while informing practical strategies. He has built a Cyber Kill Chain-based threat intelligence framework for analyzing banking Trojan campaigns which is widely used to model different attack campaigns, including APT groups activities, analyzing crypto-ransomware campaigns, and analyzing Advanced Persistent Threat (APT) groups targeting critical national infrastructure. He is currently the director of Cyber Science Lab at the University of Guelph, Ontario, Canada.

• 1. Big data analytics and forensics: an overview.- 2. Lot privacy, security and forensics challenges: an unmanned aerial vehicle (uav) case study.- 3. Detection of enumeration attacks in cloud environments using infrastructure log data.- 4.- Cyber threat attribution with multi-view heuristic analysis.- 5. Security of industrial cyberspace: fair clustering with linear time approximation.- 6. Adaptive neural trees for attack detection in cyber physical systems.- 7. Evaluating performance of scalable fair clustering machine learning techniques in detecting cyber-attacks in industrial control systems.- 8. Fuzzy bayesian learning for cyber threat hunting in industrial control systems.- 9. Cyber-attack detection in cyber-physical systems using supervised machine learning.- 10. Evaluation of scalable fair clustering machine learning methods for threat hunting in  cyber-physical systems.- 11. Evaluation of supervised and unsupervised machine learning classifiers for mac os malware detection.- 12. Evaluation of machine learning algorithms on internet of things (iot) malware opcodes.- 13. Mac os x malware detection with supervised machine learning algorithms.- 14. Machine learning for osx malware detection.- 15. Hybrid analysis on credit card fraud detection using machine learning techniques.- 16. Mapping ckc model through nlp modelling for apt groups reports.- 17. Ransomware threat detection: a deep learning approach.- 18. Scalable fair clustering algorithm for internet of things malware classification.

Kim-Kwang Raymond Choo received the Ph.D. in Information Security in 2006 from Queensland University of Technology, Australia. He currently holds the Cloud Technology Endowed Professorship at The University of Texas at San Antonio (UTSA). He is an IEEE Computer Society Distinguished Visitor (2021 - 2023), and a Web of Science's Highly Cited Researcher in the field of Cross-Field - 2020. In 2015, he and his team won the Digital Forensics Research Challenge organized by Germany's University of Erlangen-Nuremberg. He is the recipient of the 2019 IEEE Technical Committee on Scalable Computing (TCSC) Award for Excellence in Scalable Computing (Middle Career Researcher), the 2018 UTSA College of Business Col. Jean Piccione and Lt. Col. Philip Piccione Endowed Research Award for Tenured Faculty, the British Computer Society's 2019 Wilkes Award Runner-up, the 2014 Highly Commended Award by the Australia New Zealand Policing Advisory Agency, the Fulbright Scholarship in 2009, the 2008 Australia Day Achievement Medallion, and the British Computer Society's Wilkes Award in 2008. He has also received best paper awards from the IEEE Consumer Electronics Magazine for 2020, EURASIP Journal on Wireless Communications and Networking (JWCN) in 2019, IEEE TrustCom 2018, and ESORICS 2015; the Korea Information Processing Society's Journal of Information Processing Systems (JIPS) Survey Paper Award (Gold) 2019; the IEEE Blockchain 2019 Outstanding Paper Award; and Best Student Paper Awards from Inscrypt 2019 and ACISP 2005. Since receiving his PhD in 2011, Dr. Dehghantanha has made significant contributions to the fast-moving fields of cybersecurity and cyber threat intelligence. He is a Canada Research Chair in Cybersecurity and Threat Intelligence, and an EU Marie-Curie Fellow Alumni in digital forensics. Dr. Dehghantanha has pioneered the use of ML-based systems for threat hunting in IoT/ICS devices using physical characteristics(e.g. power consumption) as opposed to application-level characteristics (e.g. IP addresses). His works have resulted in an Intrusion Detection System (IDS) for IoT networks; and deep learning models for threat hunting in the edge layer of ICS networks. In 2019, with support from the Department of National Defense Canada, he has developed the first multi-view fuzzy machine learning system for cyber threat attribution. He is among few academics contributing to fundamental research in cyber threat intelligence, with most research taking place in industry settings. His work helps define this new discipline while informing practical strategies. He has built a Cyber Kill Chain-based threat intelligence framework for analyzing banking Trojan campaigns which is widely used to model different attack campaigns, including APT groups activities, analyzing crypto-ransomware campaigns, and analyzing Advanced Persistent Threat (APT) groups targeting critical national infrastructure. He is currently the director of Cyber Science Lab at the University of Guelph, Ontario, Canada.

• 1. Big data analytics and forensics: an overview.- 2. Lot privacy, security and forensics challenges: an unmanned aerial vehicle (uav) case study.- 3. Detection of enumeration attacks in cloud environments using infrastructure log data.- 4.- Cyber threat attribution with multi-view heuristic analysis.- 5. Security of industrial cyberspace: fair clustering with linear time approximation.- 6. Adaptive neural trees for attack detection in cyber physical systems.- 7. Evaluating performance of scalable fair clustering machine learning techniques in detecting cyber-attacks in industrial control systems.- 8. Fuzzy bayesian learning for cyber threat hunting in industrial control systems.- 9. Cyber-attack detection in cyber-physical systems using supervised machine learning.- 10. Evaluation of scalable fair clustering machine learning methods for threat hunting in  cyber-physical systems.- 11. Evaluation of supervised and unsupervised machine learning classifiers for mac os malware detection.- 12. Evaluation of machine learning algorithms on internet of things (iot) malware opcodes.- 13. Mac os x malware detection with supervised machine learning algorithms.- 14. Machine learning for osx malware detection.- 15. Hybrid analysis on credit card fraud detection using machine learning techniques.- 16. Mapping ckc model through nlp modelling for apt groups reports.- 17. Ransomware threat detection: a deep learning approach.- 18. Scalable fair clustering algorithm for internet of things malware classification.