@article{LorenzClemensSchroetteretal.2022,
  author    = {Lorenz, Claas and Clemens, Vera Elisabeth and Schr{\"o}tter, Max and Schnor, Bettina},
  title     = {Continuous verification of network security compliance},
  series = {IEEE transactions on network and service management},
  volume    = {19},
  journal   = {IEEE transactions on network and service management},
  number    = {2},
  publisher = {Institute of Electrical and Electronics Engineers},
  address   = {New York},
  issn      = {1932-4537},
  doi       = {10.1109/TNSM.2021.3130290},
  pages     = {1729 -- 1745},
  year      = {2022},
  abstract  = {Continuous verification of network security compliance is an accepted need. Especially, the analysis of stateful packet filters plays a central role for network security in practice. But the few existing tools which support the analysis of stateful packet filters are based on general applicable formal methods like Satifiability Modulo Theories (SMT) or theorem prover and show runtimes in the order of minutes to hours making them unsuitable for continuous compliance verification. In this work, we address these challenges and present the concept of state shell interweaving to transform a stateful firewall rule set into a stateless rule set. This allows us to reuse any fast domain specific engine from the field of data plane verification tools leveraging smart, very fast, and domain specialized data structures and algorithms including Header Space Analysis (HSA). First, we introduce the formal language FPL that enables a high-level human-understandable specification of the desired state of network security. Second, we demonstrate the instantiation of a compliance process using a verification framework that analyzes the configuration of complex networks and devices - including stateful firewalls - for compliance with FPL policies. Our evaluation results show the scalability of the presented approach for the well known Internet2 and Stanford benchmarks as well as for large firewall rule sets where it outscales state-of-the-art tools by a factor of over 41.},
  language  = {en}
}
@misc{OmranianNikoloskiGrimm2022,
  author    = {Omranian, Sara and Nikoloski, Zoran and Grimm, Dominik G.},
  title     = {Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward},
  series = {Computational and structural biotechnology journal},
  volume    = {20},
  journal   = {Computational and structural biotechnology journal},
  publisher = {Research Network of Computational and Structural Biotechnology (RNCSB)},
  address   = {Gotenburg},
  issn      = {2001-0370},
  doi       = {10.1016/j.csbj.2022.05.049},
  pages     = {2699 -- 2712},
  year      = {2022},
  abstract  = {Physically interacting proteins form macromolecule complexes that drive diverse cellular processes. Advances in experimental techniques that capture interactions between proteins provide us with protein-protein interaction (PPI) networks from several model organisms. These datasets have enabled the prediction and other computational analyses of protein complexes. Here we provide a systematic review of the state-of-the-art algorithms for protein complex prediction from PPI networks proposed in the past two decades. The existing approaches that solve this problem are categorized into three groups, including: cluster-quality-based, node affinity-based, and network embedding-based approaches, and we compare and contrast the advantages and disadvantages. We further include a comparative analysis by computing the performance of eighteen methods based on twelve well-established performance measures on four widely used benchmark protein-protein interaction networks. Finally, the limitations and drawbacks of both, current data and approaches, along with the potential solutions in this field are discussed, with emphasis on the points that pave the way for future research efforts in this field. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).},
  language  = {en}
}
@article{Pikovskij2021,
  author    = {Pikovskij, Arkadij},
  title     = {Chimeras on a social-type network},
  series = {Mathematical modelling of natural phenomena : MMNP},
  volume    = {16},
  journal   = {Mathematical modelling of natural phenomena : MMNP},
  publisher = {EDP Sciences},
  address   = {Les Ulis},
  issn      = {0973-5348},
  doi       = {10.1051/mmnp/2021012},
  pages     = {9},
  year      = {2021},
  abstract  = {We consider a social-type network of coupled phase oscillators. Such a network consists of an active core of mutually interacting elements, and of a flock of passive units, which follow the driving from the active elements, but otherwise are not interacting. We consider a ring geometry with a long-range coupling, where active oscillators form a fluctuating chimera pattern. We show that the passive elements are strongly correlated. This is explained by negative transversal Lyapunov exponents.},
  language  = {en}
}