Abstract
Nowadays, the homeland security field faces more difficulties in identifying suspicious or abnormal entities in huge datasets. Even though there are numerous technologies available, the objective of finding out the anomalous instances in huge semantic graphs is still a challenging point. This is because the nodes are strongly linked with innumerable links. When a node carries unique or abnormal semantics in the network, it is considered as an abnormal node. In order to understand this idea, the semantic profile of each node is generated by modeling the graph structure using various kinds of nodes and links linked to the node at a particular distance through edges. Here, the relation between the nodes is represented by a certain weight. After framing the graph structure, ternary-based feature level extraction based on assigned weight takes place. Further, the optimal feature selection from extracted rules is deployed where second-order mutual information is assigned as the fitness or the objective function. Here, the abnormal paired nodes are optimally selected using the improved Dragonfly Algorithm (DA) based on the maximum mutual information. Since the weighting factors utilized in DA are based on fitness function, the proposed algorithm is termed as Fitness-Weighed Dragonfly Algorithm (FW-DA). The effectiveness of the proposed algorithm is substantiated by comparing it with the conventional models through various performance analyses.
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Abbreviations
- MRNs:
-
Multi-relational networks
- PSLNs:
-
Probabilistic semantic link networks
- GFA:
-
Graph Filtering Algorithm
- MLP:
-
Machine learning classifier
- DA:
-
Dragonfly Algorithm
- PSO:
-
Particle Swarm Optimization
- FF:
-
FireFly
- GWO:
-
Grey Wolf Optimization
- WOA:
-
Whale Optimization Algorithm
- FPR:
-
False Positive Rate
- FAR:
-
False Alarm Rate
References
Lin S. D. and Chalupsky H. 2008 Discovering and explaining abnormal nodes in semantic graphs; IEEE Transactions on Knowledge and Data Engineering 20(8) 1039–1052
Wu S., Zhang Y. and Cao W. 2017 Network security assessment using a semantic reasoning and graph based approach; Computers & Electrical Engineering 64 96–109
Modica G. D. and Tomarchio O. 2016 Matchmaking semantic security policies in heterogeneous clouds; Future Generation Computer Systems 55 176–185
Vidal J. C., Lama M., Otero-García E. and Bugarín A. 2014 Graph-based semantic annotation for enriching educational content with linked data; Knowledge-Based Systems 55 29–42
Yao Y., Wang Z., Gan C., Kang Q. and Zhang L. 2016 Multi-source alert data understanding for security semantic discovery based on rough set theory; Neurocomputing 208 39–45
Basha S. M. and Rajput D. S. 2018 Parsing based sarcasm detection from literal language in tweets; Recent Patents on Computer Science 11(1) 62–69
AlEroud A. F. and Karabatis G. 2018 Queryable semantics to detect cyber-attacks: a flow-based detection approach; IEEE Transactions on Systems, Man, and Cybernetics: Systems 48(2) 207–223
Das S., Liu Y., Zhang W. and Chandramohan M. 2016 Semantics-based online malware detection: towards efficient real-time protection against malware; IEEE Transactions on Information Forensics and Security 11(2) 289–302
Cadena J., Chen F. and Vullikanti A. 2018 Graph anomaly detection based on Steiner connectivity and density; Proceedings of the IEEE 106(5) 829–845
Thippa Reddy G., Praveen Kumar Reddy M., Kuruva Lakshmanna, Rajput D. S., Rajesh Kaluri and Gautam Srivastava. 2020 Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis; Evolutionary Intelligence 13(2) 185–196
Liu Q., Klucik R., Chen C., Grant G. and Shang L. 2017 Unsupervised detection of contextual anomaly in remotely sensed data; Remote Sensing of Environment 202 75–87
Ahmad S., Lavin A., Purdy S. and Agha Z. 2017 Unsupervised real-time anomaly detection for streaming data; Neurocomputing 262 134–147
Mukund W. B. and Gomathi N. 2018 Quantitative and qualitative correlation analysis of optimal route discovery for vehicular ad-hoc networks; Journal of Central South University 25(7) 1732–1745
Wagh M. B. and Gomathi N. 2019 Optimal route selection for vehicular adhoc networks using lion algorithm; Journal of Engineering Research 7(3)
Xia H., Fang B., Roughan M., Cho K. and Tune P. 2018 A BasisEvolution framework for network traffic anomaly detection; Computer Networks 135 15–31
Fan C., Xiao F., Zhao Y. and Wang J. 2018 Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data; Applied Energy 211 1123–1135
Wang Y., Li X. and Ding X. 2016 Probabilistic framework of visual anomaly detection for unbalanced data; Neurocomputing 201 12–18
Vela A. P., Ruiz M. and Velasco L. 2017 Distributing data analytics for efficient multiple traffic anomalies detection; Computer Communications 107 1–12
Grill M., Pevný T. and Martin Rehak. 2017 Reducing false positives of network anomaly detection by local adaptive multivariate smoothing; Journal of Computer and System Sciences 83(1) 43–57
Carvalho L. F., Abrão T., Mendes L. S. and Proença M. L. 2018 An ecosystem for anomaly detection and mitigation in software-defined networking; Expert Systems with Applications 104 121–133
Vlietstra W. J., Zielman R., Dongen R. M., Schultes E. A. and Kors J. A. 2017 Automated extraction of potential migraine biomarkers using a semantic graph; Journal of Biomedical Informatics 71 178–189
Wu Z., Liao J., Song W., Mao H. and Mao H. 2018 Semantic hyper-graph-based knowledge representation architecture for complex product development; Computers in Industry 100 43–56
Lugowski A., Kamil S., Buluç A., Williams S. and Gilbert J. R. 2015 Parallel processing of filtered queries in attributed semantic graphs; Journal of Parallel and Distributed Computing 79–80 115–131
Dai C., Chen L., Li B. and Li Y. 2017 Link prediction in multi-relational networks based on relational similarity; Information Sciences 394–395 198–216
Rodriguez M. A. and Shinavier J. 2010 Exposing multi-relational networks to single-relational network analysis algorithms; Journal of Informetrics 4(1) 29–41
Guesmi S., Trabelsi C. and Latiri C. 2016 CoMRing: a framework for community detection based on multi-relational querying exploration; Procedia Computer Science 96 627–636
Reddy G. T., Reddy M. P. K., Lakshmanna K., Kaluri R. and Rajput D. S. 2020 Srivastava G and Thar Baker Analysis of dimensionality reduction techniques on big data; IEEE Access 8 54776–54787
Wang S., Li X., Ye Y., Huang X. and Li Y. 2018 Multi-attribute and relational learning via hypergraph regularized generative model; Neurocomputing 274 115–124
Zhang Z., Li Q., Zeng D. and Gao H. 2013 User community discovery from multi-relational networks; Decision Support Systems 54(2) 870–879
Roy R. G. and Ghoshal D. 2019 Grey wolf optimization-based second order sliding mode control for inchworm robot; Robotica 38(9) 1539–1557
Vidyadhari Ch., Sandhya N. and Premchand P. 2019 A semantic word processing using enhanced cat swarm optimization algorithm for automatic text clustering; Multimedia Research 2(4) 23–32
Quazi M. H. and Kahalekar S. G. 2019 Artifacts removal using dragonfly Levenberg Marquardt-based learning algorithm from electroencephalogram signal; Multimedia Research 2(2) 1–9
Vinusha S. and Abinaya J. S. 2018 Performance analysis of the adaptive cuckoo search rate optimization scheme for the congestion control in the WSN; Journal of Networking and Communication Systems 1(1) 19–27
Preetha N. S. N., Brammya G., Ramya R., Praveena S., Binu D. and Rajakumar B. R. 2018 Grey wolf optimisation-based feature selection and classification for facial emotion recognition; IET Biometrics 7(5) 490–499. https://doi.org/10.1049/iet-bmt.2017.0160
Ravi R. V., Subramaniam K., Roshini T. V., Muthusamy S. P. B. and Venkatesan G. P. 2019 Optimization algorithms, an effective tool for the design of digital filters; a review; Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-019-01431-x
Ravi R. V. and Subramaniam K. 2020 Image compression using optimized wavelet filter derived from grey wolf algorithm; Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02290-7
Jameel A. S. and Ali M. M. 2016 Factors affecting customer loyalty towards Yes company in Malaysia; International Journal of Advanced Research in Engineering & Management 2(1) 1–7
Jameel A. S. 2018 Issues facing citizens in Iraq towards adoption of e-government; Al-Kitab Journal for Human Sciences 1(1). https://doi.org/10.32441/kjhs.01.01.p13
Preetha N. S. N., Brammya G., Ramya R., Praveena S., Binu D. and Rajakumar B. R. 2018 Grey wolf optimisation-based feature selection and classification for facial emotion recognition; IET Biometrics 7(5) 490–499
Mirjalili S. 2016 Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems; Neural Computing and Applications 27(4) 1053–1073
Jadhav A. N. and Gomathi N. 2019 DIGWO: hybridization of dragonfly algorithm with improved grey wolf optimization algorithm for data clustering; Multimedia Research 2(3) 1–11
Wang H., Wang W., Zhou X., Sun H. and Cui Z. 2017 Firefly algorithm with neighborhood attraction; Information Sciences 382–383 374–387
Zhang J. and Xia P. 2017 An improved PSO algorithm for parameter identification of nonlinear dynamic hysteretic models; Journal of Sound and Vibration 389 153–167
Mirjalili S., Mirjalili S. M. and Lewis A. 2014 Grey wolf optimizer; Advances in Engineering Software 69 46–61
Mirjalili S. and Lewis A. 2016 The whale optimization algorithm; Advances in Engineering Software 95 51–67
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Reddy, M.S.K., Rajput, D.S. Ternary-based feature level extraction for anomaly detection in semantic graphs: an optimal feature selection basis. Sādhanā 46, 54 (2021). https://doi.org/10.1007/s12046-021-01570-y
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DOI: https://doi.org/10.1007/s12046-021-01570-y