Multi-type relational clustering for enterprise cyber-security networks
•Propose fast novel hard clustering algorithm for multi-type relational data.•Extend the popular NNDSVD method initialisation of our algorithm.•Propose internal performance clustering measure for assessing cluster similarity. Several cyber-security data sources are collected in enterprise networks p...
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| Vydáno v: | Pattern recognition letters Ročník 149; s. 172 - 178 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.09.2021
Elsevier Science Ltd |
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| ISSN: | 0167-8655, 1872-7344 |
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| Abstract | •Propose fast novel hard clustering algorithm for multi-type relational data.•Extend the popular NNDSVD method initialisation of our algorithm.•Propose internal performance clustering measure for assessing cluster similarity.
Several cyber-security data sources are collected in enterprise networks providing relational information between different types of nodes in the network, namely computers, users and ports. This relational data can be expressed as adjacency matrices detailing inter-type relationships corresponding to relations between nodes of different types and intra-type relationships showing relationships between nodes of the same type. In this paper, we propose an extension of Non-Negative Matrix Tri-Factorisation (NMTF) to simultaneously cluster nodes based on their intra and inter-type relationships. Existing NMTF based clustering methods suffer from long computational times due to large matrix multiplications. In our approach, we enforce stricter cluster indicator constraints on the factor matrices to circumvent these issues. Additionally, to make our proposed approach less susceptible to variation in results due to random initialisation, we propose a novel initialisation procedure based on Non-Negative Double Singular Value Decomposition for multi-type relational clustering. Finally, a new performance measure suitable for assessing clustering performance on unlabelled multi-type relational data sets is presented. Our algorithm is assessed on both a simulated and real computer network against standard approaches showing its strong performance. |
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| AbstractList | •Propose fast novel hard clustering algorithm for multi-type relational data.•Extend the popular NNDSVD method initialisation of our algorithm.•Propose internal performance clustering measure for assessing cluster similarity.
Several cyber-security data sources are collected in enterprise networks providing relational information between different types of nodes in the network, namely computers, users and ports. This relational data can be expressed as adjacency matrices detailing inter-type relationships corresponding to relations between nodes of different types and intra-type relationships showing relationships between nodes of the same type. In this paper, we propose an extension of Non-Negative Matrix Tri-Factorisation (NMTF) to simultaneously cluster nodes based on their intra and inter-type relationships. Existing NMTF based clustering methods suffer from long computational times due to large matrix multiplications. In our approach, we enforce stricter cluster indicator constraints on the factor matrices to circumvent these issues. Additionally, to make our proposed approach less susceptible to variation in results due to random initialisation, we propose a novel initialisation procedure based on Non-Negative Double Singular Value Decomposition for multi-type relational clustering. Finally, a new performance measure suitable for assessing clustering performance on unlabelled multi-type relational data sets is presented. Our algorithm is assessed on both a simulated and real computer network against standard approaches showing its strong performance. Several cyber-security data sources are collected in enterprise networks providing relational information between different types of nodes in the network, namely computers, users and ports. This relational data can be expressed as adjacency matrices detailing inter-type relationships corresponding to relations between nodes of different types and intra-type relationships showing relationships between nodes of the same type. In this paper, we propose an extension of Non-Negative Matrix Tri-Factorisation (NMTF) to simultaneously cluster nodes based on their intra and inter-type relationships. Existing NMTF based clustering methods suffer from long computational times due to large matrix multiplications. In our approach, we enforce stricter cluster indicator constraints on the factor matrices to circumvent these issues. Additionally, to make our proposed approach less susceptible to variation in results due to random initialisation, we propose a novel initialisation procedure based on Non-Negative Double Singular Value Decomposition for multi-type relational clustering. Finally, a new performance measure suitable for assessing clustering performance on unlabelled multi-type relational data sets is presented. Our algorithm is assessed on both a simulated and real computer network against standard approaches showing its strong performance. |
| Author | Riddle-Workman, Elizabeth Evangelou, Marina Adams, Niall M. |
| Author_xml | – sequence: 1 givenname: Elizabeth surname: Riddle-Workman fullname: Riddle-Workman, Elizabeth email: elizabeth.riddle-workman13@imperial.ac.uk organization: Department of Mathematics, Imperial College London, London SW7 2AZ, UK – sequence: 2 givenname: Marina surname: Evangelou fullname: Evangelou, Marina organization: Department of Mathematics, Imperial College London, London SW7 2AZ, UK – sequence: 3 givenname: Niall M. surname: Adams fullname: Adams, Niall M. organization: Department of Mathematics, Imperial College London, London SW7 2AZ, UK |
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| Cites_doi | 10.1145/1077464.1077466 10.1016/0378-8733(83)90021-7 10.1007/BF02288367 10.1016/j.patrec.2019.01.016 10.1109/TII.2014.2308433 10.1016/j.patrec.2019.02.018 10.1080/01969727408546059 10.1371/journal.pone.0217994 10.1007/BF01908075 10.1016/j.patcog.2007.09.010 10.1016/j.patrec.2015.05.019 |
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| Keywords | Multi-type relational clustering Cyber-security Network clustering Non-negative matrix factorization 41A10 65D05 65D17 41A05 |
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| SubjectTerms | Algorithms Clustering Computer applications Computer networks Computers Cyber-security Cybersecurity Multi-type relational clustering Network clustering Nodes Non-negative matrix factorization Singular value decomposition |
| Title | Multi-type relational clustering for enterprise cyber-security networks |
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