Signal processing for network forensics and security

EURASIP Journal on Information Security welcomes submissions to the thematic series on 'Signal processing for network forensics and security'.

Networks are by far the dominating paradigm to exchange information across spatially dispersed individuals and entities. Therefore, network safeguard emerges distinctly as one of the fundamental challenges of modern times: Whether we are talking of website attacks, power network sabotages, or even of terrorist attacks, protecting our “interconnected” lives is of paramount importance.
While it is true that clever interaction among the individual network agents brings steadily new possibilities to share information, such an enhancement is not for free. New doors are concurrently opened to the attacker, multiplying the challenges at the defender’s side. Therefore, it is not surprising that networks have become the preferential attacker’s habitat to hide and launch a rich variety of threats. For instance, a dangerous attack to a powerful target site (e.g., a big e-commerce portal) is often launched through a series of apparently innocuous attacks to some powerless, but most vulnerable, sites (e.g., some personal computers).

Different kinds of networks (e.g., communications/social/sensor networks, distributed big data depositories, power grids) are more and more exposed to an increasingly large variety of threats. Correspondingly, the design of useful defense strategies might require fruitful interaction among several disciplines and communities, including: signal processing, networking, machine learning, optimization, statistics, physics, economics, computer, and social sciences. In particular, signal processing is called to play a primary role in the realm of network security and forensics. To mention a few related examples: decision making in the presence of adversaries that can corrupt the data; universal algorithms for tracing information flows across the network; sparsity-aware algorithms for unveiling traffic volume anomalies.

At a more general level, signal processing can offer principled approaches, theories and algorithms necessary to: (a) disclosing significant relationships hidden in the network data, in order to enable a fast and reliable threat identification; (b) ensuring proper performance guarantees, also in terms of robustness, versatility and adaptation; c) envisaging descriptive indicators with a clear physical interpretation, a crucial requirement to make the output of the inference process usable to forensic purposes.

Potential topics include but are not limited to:

  • Traffic analysis, timing analysis
  • Detection of clandestine information flows
  • Network tomography, inference of causal relationships
  • Anomaly detection, network intrusion, Byzantine attacks
  • Adversarial signal processing, game-theoretic network security
  • Distributed denial of service attacks
  • Machine learning techniques for security applications
  • Large-scale methods (e.g., sparse representations, big data analytics, distributed optimization) for security applications
  • Distributed detection, estimation and filtering under security/privacy constraints
  • Adaptive strategies and consensus strategies under security/privacy constraints
  • Security applications for biological, economic, smart grids, and social networks

Submission Instructions

Before submitting your manuscript, please ensure you have carefully read the submission guidelines for EURASIP Journal on Information Security. The complete manuscript should be submitted through the EURASIP Journal on Information Security submission system. To ensure that you submit to the correct thematic series please select the appropriate thematic series in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the thematic series on 'Signal processing for network forensics and security'. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.

Deadline for submissions: 01 June, 2017

Lead Guest Editor

Vincenzo Matta, University of Salerno, Italy

Guest Editors

Ting He, Pennsylvania State University, USA

Gonzalo Mateos, University of Rochester, USA


Submissions will also benefit from the usual advantages of open access publication:

  • Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient
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  • No space constraints: Publishing online means unlimited space for figures, extensive data and video footage
  • Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed

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