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Submission Deadline Oct 1, 2019
Notification Due Mar 31, 2020
Final Version Due Apr 15, 2020
Categories    information security   cyber security   network security   machine learning

Call For Papers



Submission Deadline: October 1, 2019

Dear Colleagues:

We cordially invite you to share your latest research results by
submitting your manuscript to the IEEE Transactions on Dependable
and Secure Computing Special Issue on "AI/ML for Secure Computing".


Artificial Intelligence (AI) and machine learning (including deep
learning) have been widely studied in both academia and industry
to achieve enhanced security and privacy of service computing
(including cloud computing, Internet services, and Internet-of-Things).
For instance, AI and machine learning algorithms can be deployed
to detect sophisticated attacks, e.g., online abuse, which cannot
be easily detected by traditional detection approaches like rule-based
detection. It is important to investigate how they can be implemented
to achieve a good trade-off between detection accuracy and learning
cost. Meanwhile, AI and machine learning themselves are vulnerable
to security and privacy concerns: for example, data used by them may
leak sensitive information, thus compromising users’ privacy.
Thus, in some application scenarios, how to leverage the capability
of AI and machine learning algorithms for enhanced secure service
computing while protecting the user’s privacy remains a challenging
problem. Recent research has demonstrated the negative impact of
other adversarial behavior: adversarial noise injected during the
training phase (“poisoning”) of AI and machine learning algorithms
result in incorrect models; adversaries can construct “adversarial
examples” that cause properly trained models to infer incorrect
results. The security and robustness of AI and machine learning
algorithms also have a strong impact on security and privacy of
service computing.

The scope of this special issue is addressing the challenges of
applying AI and machine learning algorithms to secure computing.
In order to implement them in practice, a big obstacle for research
is to have enhanced security while not impacting users’ privacy.


In this special issue, we are seeking novel approaches and
unpublished work related to AI and machine learning for
enhanced security and privacy protection of service computing.
In particular, we would like to focus on recent trends in
adversarial machine learning, reinforcement learning,
and privacy-preserving machine learning to improve the security.
We solicit experimental, conceptual, and theoretical contributions
on the following topics related to AI and machine learning for
enhanced security and privacy of service computing:

• Attacks on machine learning and defense
• Generative Adversarial Networks (GAN) for attacks and defenses
• Deep learning for enhanced security and privacy
• Enhanced security of service computing with reinforcement learning
• Adversarial machine learning for security and privacy of computing
• Adversarial examples: attacks and defenses
• Robust learning for enhanced security and privacy in service computing
• Learning for malware analysis and detection
• Learning for anomaly and intrusion detection
• Learning for critical infrastructure security
• Learning for cryptanalysis
• Learning for spam detection
• Learning for secure online social networks


Papers submitted to this special issue for possible publication must
be original and must not be under consideration for publication in
any other journal or conference. TDSC requires meaningful technical
novelty in submissions that extend previously published conference
papers. Extension beyond the conference version(s) is not simply
a matter of length. Thus, expanded motivation, expanded discussion
of related work, variants of previously reported algorithms,
incremental additional experiments/simulations, may provide additional
length but will fall below the line for proceeding with review.
Submissions must be directly submitted via the IEEE TDSC submission
website at


• Manuscript Submission Deadline: October 1, 2019
• First Round of Reviews: December 15, 2019
• Revised Papers Due: January 31, 2019
• Final Notification: March 31, 2020
• Final Manuscript Due: April 15, 2020


• N. Asokan, Aalto University, Finland,
• Pan Hui, University of Helsinki, Finland & Hong Kong University of Science and Technology, Hong Kong,
• Qi Li, Tsinghua University, China,
• Ravi Sandhu, The University of Texas at San Antonio,

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