Shared task on Detecting Malicious Domain names (DMD 2018)

a workshop co-located with ICACCI'18 and SSCC'18

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. While registering make sure that the name of the team is kept as title and a short description of your approach is also provided as abstract through the Google form provided. The shared task features problem statements in the field of traditional machine learning, deep learning and text analysis in Cyber Security. Participants are advised to register as soon as possible in order to receive the training and testing datasets as per the schedule.

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.

All the accepted Shared Task and Workshop Proceedings will be submitted to CEUR-WS.org. Extended version of the best working notes and workshop papers will be submitted to the book.This book will be published in Advanced Sciences and Technologies for Security Applications, Springer

Tasks

  1. The Sub task1 is to identify the DGA generated domain name.
  2. The Sub task 2 is to detect and categorize the DGA generated domain name to their botnet family.

Corpus

We have provided a few examples of the benign and DGA generated domain name

Benign domain samples

Top ranked domain names

DGA DOMAIN NAME SAMPLES

Domain names are generated using DGA

Registration

The registration link is HERE

We will post the details for the training corpus on June 30th, 2018. Stay tuned!

For any questions, please contact the workshop organizers at: dmd2018[at]cb.amrita.edu

Call for papers (as part of ICACCI'18 and SSCC'18)

Deep learning for Security Applications (Topics of interest include (but are not limited to)):
  • 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
Selected papers will be published in the conference proceedings and submitted to IEEE Xplore as well as other Abstracting and Indexing (A&I) databases. An extended version of the best papers will be submitted to the book. This book will be published in Advanced Sciences and Technologies for Security Applications, Springer.

Participants can submit their papers through EDAS. The paper submission guidelines avilable here.

Important Dates - Shared task

Event Date
Registration due June 15th (Registration started)
Training data release June 30th
Test data release July 15th
Model and Results Submission July 17th
Results declared July 30th
Working notes due Aug 15th
Conference Sep 19-22

Important Dates - Call for papers

Event Date
Papers Due May 31, 2018
Acceptance Notification June 30, 2018
Final Paper Deadline August 20, 2018

Results

COMMITTEES

organizing and Technical Program committee

Prof Soman KP, 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. Stavros Ntalampiras,, Assistant Professor, Department of Computer Science of the University of Milan.
Dr. Yassine Maleh, Hassan 1st University, Morocco
Dr. M. Sabarimalai Manikandan, Indian Institute of Technology, Bhubaneswar
Dr. B. B. Gupta, National Institute of Technology Kurukshetra, India
Dr. Sandeep K. Shukla,, , Professor, Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur
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

Advisory committee

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

References

A deep-dive on Machine learning for Cybersecurity use cases, Vinayakumar R, Soman KP, Prabaharan Poornachandran and Pradeep Menon [MLCCS 2018 Book chapter Accepted]

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]