Call for Participation
We welcome you to participate in the Domain Generation Algorithms (DGAs) generated domain name detection and classification shared task track at DMD 2018. The shared task features problem statements in the field of traditional machine learning, deep learning and text analysis in Cyber Security.
The participants will receive an unbalanced dataset for the first subtask, so design your model accordingly. The testing data will be provided one day before the deadline. We require all the participants to submit their trained model and the results obtained on the test data provided by us. All the participants who submit their work are welcome to present their model at DMD 2018.
Note: All the accepted shared task working notes and workshop proceedings will be submitted to CEUR-WS.org for online publication. The extended version of the best working notes will be submitted to the Advanced Sciences and Technologies for Security Applications, Springer.
- The Sub task1 is to identify the DGA generated domain name.
- The Sub task 2 is to detect and categorize the DGA generated domain name to their botnet family.
Apart from the shared task, we welcome scientific papers on topics related to Deep learning for Security Applications as part of ICACCI'18 and SSCC'18.
- Botnet identification and Detection
- Spam and Phishing Detection
- Security in Social Networks
- Learning in Adversarial Environments
- Malware Identification, Analysis and Similarity
- Intrusion Detection and Response
- Representation and Detection of Social Engineering Attacks
- Classification of Sequences of System and Network Events
- Application of Learning to Computer Forensics
- Program Representation
- Web Application
- Security, Privacy, Trust and Safety
- Mobile Computing, Internet of Things (IoT)
- Cloud, Apps and Services, and their Security
- Big Data Architectures for Network Security
- Detecting Data and Information Leakage
Note: Selected papers will be published in the conference proceedings and submitted to IEEE Xplore as well as other Abstracting and Indexing (A&I) databases. The extended version of the best working notes will be submitted to the Advanced Sciences and Technologies for Security Applications, Springer.
Organizing and Technical Program Committee
- Prof. Soman K. P., Prof & Head CEN
- Prof. Bharat Jayaraman, University at Buffalo
- Dr. Sabu M. Thampi, Associate Professor, IIITM-K
- Dr. Mamoun Alazab, Senior member IEEE and Senior Lecturer (Associate Professor in North America)
- Dr MingJian Tang, Data Scientist (Cyber Security), Commonwealth Bank, Australia
- Dr. Rakesh Verma, Professor, University of Houston
- Dr. Lila Ghemri, Associate Professor Texas Southern University, Houston
- Dr. M. Sabarimalai Manikandan, Indian Institute of Technology, Bhubaneswar
- Dr. Prabaharan Poornachandran, Center for Cyber Security Systems and Networks, Amrita Vishwa Vidyapeetham, Kollam, India
- Mr. Pradeep Menon, Chief executive officer, Lakhshya Cyber Security Labs Pvt Ltd, Coimbatore
- Dr. B. B. Gupta, National Institute of Technology Kurukshetra, India
- Mrs. Sowmya V., CEN, Amrita Vishwa Vidyapeetham
- Dr. E. A. Gopalakrishnan, CEN, Amrita Vishwa Vidyapeetham
- Mr. Vijay Krishnan Menon ,CEN, Amrita Vishwa Vidyapeetham
- Dr. Anand Kumar M., CEN, Amrita Vishwa Vidyapeetham, Coimbatore, India
- Dr. Govind D. , CEN, Amrita Vishwa Vidyapeetham
- Dr. Shanmugha Sundaram G. A., CEN, Amrita Vishwa Vidyapeetham
- Dr. Geetha Srikanth , CEN, Amrita Vishwa Vidyapeetham
- Mr. Sajith Variyar V. V., CEN, Amrita Vishwa Vidyapeetham
- A deep-dive on Machine learning for Cybersecurity use cases, Vinayakumar R, Soman KP, Prabaharan Poornachandran and Pradeep Menon [MLCCS 2018 Book chapter under-review]
- S.P.O.O.F Net: Syntactic Patterns for identification of Ominous Online Factors, Vysakh S Mohan, Vinayakumar R, Soman Kp and Prabaharan Poornachandran [BioSTAR 2018]
- Scalable Framework for Cyber Threat Situational Awareness based on Domain Name Systems Data Analysis, Vinayakumar R, Prabaharan Poornachandran and Soman KP [In Press] [Book Chapter -Springer]
- Detecting Malicious Domain Names using Deep Learning Approaches at Scale, Vinayakumar R, Soman KP, and Prabaharan Poornachandran [Journal-IOS Press]
- Evaluating Deep Learning Approaches to Characterize and Classify the DGAs at Scale, Vinayakumar R, Soman KP, Prabaharan Poornachandran and Sachin Kumar S [Journal-IOS Press]