Biography summary
Dr. Sutharshan Rajasegarar received his Ph.D. from The University of Melbourne, Australia. He is currently a Senior Lecturer with the School of Information Technology, Deakin University, Burwood, Australia. He previously worked as a Research Fellow at the Department of Electrical and Electronics Engineering, The University of Melbourne, as a researcher in Machine Learning with the National ICT Australia (NICTA/Data61), and as a visiting researcher at University of Surrey, UK. His current research interests include anomaly/outlier detection, distributed machine learning, Artificial Intelligence (AI), signal processing, health analytics, computer vision, wireless communications, cyber securuty, sports analytics and Internet of Things (IoT).
Affiliations
Member, IEEE
Member, ACM
Projects
Anomaly/outlier detection and distributed machine learning.
Emotion detection and profiling using computer vision.
Artificial Intelligence based precision agriculture.
Smart activity detection with wearable sensors
ARC Discover project 2020 (ARC DP): Learning the Focus of Attention to Detect Distributed Coordinated Attacks.
OCSC (Oceania Cyber Secirity Center) project, 2019: Detection of Infected Internet-of-Thing (IoT) Devices to Prevent Distributed Denial of Service (DDoS) Attacks.
EU FP7 (European Union 7th Framework Programme) project, 2013, SOCIOTAL [https://cordis.europa.eu/project/id/609112]
EPSRC (Engineering and Physical Sciences Research Council) UK grant project: REDUCE - Reshaping energy demand of users by communication technology and economic incentives, 2011
Publications
Deep learning-based real-time 3D human pose estimation
X Zhang, Z Zhou, Y Han, H Meng, M Yang, S Rajasegarar
(2023), Vol. 119, pp. 105813-105813, Engineering Applications of Artificial Intelligence, C1
Evolving graph-based video crowd anomaly detection
M Yang, Y Feng, A Rao, S Rajasegarar, S Tian, Z Zhou
(2023), Visual Computer, C1
Deep3DCANN: A Deep 3DCNN-ANN framework for spontaneous micro-expression recognition
S Thuseethan, S Rajasegarar, J Yearwood
(2023), Vol. 630, pp. 341-355, Information Sciences, Amsterdam, The Netherlands, C1
Multi-attention graph neural networks for city-wide bus travel time estimation using limited data
J Ma, J Chan, S Rajasegarar, C Leckie
(2022), Vol. 202, pp. 1-11, Expert Systems with Applications, Amsterdam, The Netherlands, C1
EmoSeC: Emotion recognition from scene context
S Thuseethan, S Rajasegarar, J Yearwood
(2022), Vol. 492, pp. 174-187, Neurocomputing, Amsterdam. The Netherlands, C1
S Janarthan, S Thuseethan, S Rajasegarar, J Yearwood
(2022), Vol. 202, Computers and Electronics in Agriculture, C1
An efficient deep neural model for detecting crowd anomalies in videos
M Yang, S Tian, A Rao, S Rajasegarar, M Palaniswami, Z Zhou
(2022), Applied Intelligence, C1
Identification of Stock Market Manipulation with Deep Learning
Jillian Tallboys, Ye Zhu, Sutharshan Rajasegarar
(2022), Vol. 13087, pp. 408-420, Advanced Data Mining and Applications, Sydney, Australia, E1
EvAnGCN: Evolving Graph Deep Neural Network Based Anomaly Detection in Blockchain
V Patel, V Patel, S Rajasegarar, S Rajasegarar, L Pan, L Pan, J Liu, J Liu, L Zhu, L Zhu
(2022), Vol. 13725, pp. 444-456, ADMA 2022 : Proceedings of the 18th International Conference on Advanced Data Mining and Applications 2022, Brisbane, Qld., E1
Exploiting Redundancy in Network Flow Information for Efficient Security Attack Detection
S Xia, S Rajasegarar, C Leckie, S Erfani, J Chan
(2022), Vol. 13787 LNCS, pp. 105-119, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), E1
Cyber Attack Detection in IoT Networks with Small Samples: Implementation And Analysis
V Kanthuru, S Rajasegarar, P Rathore, R Doss, L Pan, B Ray, M Chowdhury, C Srimathi, M Durai
(2022), Vol. 13725, pp. 118-130, ADMA 2022 : Proceedings of the Advanced Data Mining and Applications International Conference 2022, Brisbane, Qld., E1
Machine Learning Aided Minimal Sensor based Hand Gesture Character Recognition
N Zaidi, P Kumari, S Rajasegarar, C Karmakar
(2022), DSAA 2022 : Proceedings of the IEEE 9th International Conference on Data Science and Advanced Analytics 2022, Shenzhen, China, E1
Visual Structural Assessment and Anomaly Detection for High-Velocity Data Streams
P Rathore, D Kumar, J Bezdek, S Rajasegarar, M Palaniswami
(2021), Vol. 51, pp. 5979-5992, IEEE Transactions on Cybernetics, United States, C1
LGAttNet: Automatic micro-expression detection using dual-stream local and global attentions
M Takalkar, S Thuseethan, S Rajasegarar, Z Chaczko, M Xu, J Yearwood
(2021), Vol. 212, Knowledge-Based Systems, C1
Effect of Stress on Cardiorespiratory Synchronization of Ironman Athletes
M Angelova, P Holloway, S Shelyag, S Rajasegarar, H Rauch
(2021), Vol. 12, Frontiers in Physiology, Switzerland, C1
Deep learning algorithms for cyber security applications: A survey
G Li, P Sharma, L Pan, S Rajasegarar, C Karmakar, N Patterson
(2021), Vol. 29, pp. 447-471, Journal of Computer Security, C1
A Novel Insider Attack and Machine Learning Based Detection for the Internet of Things
Morshed Chowdhury, Biplob Ray, Sujan Chowdhury, Sutharshan Rajasegarar
(2021), Vol. 2, pp. 1-23, ACM Transactions on Internet of Things, New York, N.Y., C1
Deep Continual Learning for Emerging Emotion Recognition
S Thuseethan, S Rajasegarar, J Yearwood
(2021), pp. 1-14, IEEE Transactions on Multimedia, Piscataway, N.J., C1
Boosting Emotion Recognition in Context using Non-target Subject Information
S Thuseethan, Sutharshan Rajasegarar, John Yearwood
(2021), pp. 1-7, IJCNN 2021 : Proceedings of the International Joint Conference on Neural Networks, Shenzhen, China, E1
ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks
K Hossain, S Kamran, A Tavakoli, Lei Pan, Daniel Ma, S Rajasegarar, Chandan Karmakar
(2021), pp. 50-56, ICMLA 2021 : Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications, Pasadena, Calif., E1
VoterChoice: A ransomware detection honeypot with multiple voting framework
C Keong Ng, S Rajasegarar, L Pan, F Jiang, L Zhang
(2020), Vol. 32, Concurrency and Computation: Practice and Experience, C1
Complex Emotion Profiling: An Incremental Active Learning Based Approach with Sparse Annotations
S Thuseethan, S Rajasegarar, J Yearwood
(2020), Vol. 8, pp. 147711-147727, IEEE Access, C1
Deep metric learning based citrus disease classification with sparse data
S Janarthan, S Thuseethan, S Rajasegarar, Q Lyu, Y Zheng, J Yearwood
(2020), Vol. 8, pp. 162588-162600, IEEE Access, C1
Gathering intelligence on student information behavior using data mining
L Pan, N Patterson, S McKenzie, S Rajasegarar, G Wood-Bradley, J Rough, W Luo, E Lanham, J Coldwell-Neilson
(2020), Vol. 68, pp. 636-658, Library Trends, C1
Robust patient information embedding and retrieval mechanism for ECG signals
I Natgunanathan, C Karmakar, S Rajasegarar, T Zong, A Habib
(2020), Vol. 8, pp. 181233-181245, IEEE Access, C1
The Role of Visual Assessment of Clusters for Big Data Analysis: From Real-World Internet of Things
Marimuthu Palaniswami, Aravinda Rao, Dheeraj Kumar, Punit Rathore, Sutharshan Rajasegarar
(2020), Vol. 6, pp. 45-53, IEEE Systems, Man, and Cybernetics Magazine, Piscataway, N.J., C1
Multiclass anomaly detector: The cs++ support vector machine
A Shilton, S Rajasegarar, M Palaniswami
(2020), Vol. 21, Journal of Machine Learning Research, C1
M Chowdhury, R Doss, B Ray, S Rajasegarar, S Chowdhury
(2020), Vol. 324, pp. 28-41, SGIoT 2019 : Proceedings of the Third European Alliance for Innovation International Conference on Smart Grid and Internet of Things 2019, TaiChung, Taiwan, E1
Graph deep learning based anomaly detection in Ethereum blockchain network
V Patel, L Pan, S Rajasegarar
(2020), Vol. 12570, pp. 132-148, NSS 2020 : Proceedings of the 14th International Conference on Network and System Security, Online from Melbourne, Vic., E1
Multi-attention 3D residual neural network for origin-destination crowd flow prediction
J Ma, J Chan, S Rajasegarar, G Ristanoski, C Leckie
(2020), pp. 1160-1165, ICDM2020 : Proceedings of IEEE's International Conference on Data Mining, Online : Sorrento, Italy, E1
Multimodal Deep Learning Framework for Sentiment Analysis from Text-Image Web Data
Selvarajah Thuseethan, Sivasubramaniam Janarthan, Sutharshan Rajasegarar, Priya Kumari, John Yearwood
(2020), pp. 267-274, WI-IAT 2020 : Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Melbourne, Victoria, E1
A Rapid Hybrid Clustering Algorithm for Large Volumes of High Dimensional Data
P Rathore, D Kumar, J Bezdek, S Rajasegarar, M Palaniswami
(2019), Vol. 31, pp. 641-654, IEEE Transactions on Knowledge and Data Engineering, C1
A Scalable Framework for Trajectory Prediction
P Rathore, D Kumar, S Rajasegarar, M Palaniswami, J Bezdek
(2019), Vol. 20, pp. 3860-3874, IEEE Transactions on Intelligent Transportation Systems, C1
Bus travel time prediction with real-time traffic information
J Ma, J Chan, G Ristanoski, S Rajasegarar, C Leckie
(2019), Vol. 105, pp. 536-549, Transportation research part c: emerging technologies, Amsterdam, The Netherlands, C1
Investigation of Complexity and Regulatory Role of Physiological Activities during a Pacing Exercise
M Angelova, S Shelyag, S Rajasegarar, D Chuckravanen, S Rajbhandari, P Gastin, A St Clair Gibson
(2019), Vol. 7, pp. 152334-152346, IEEE Access, C1
Detection of smoking events from confounding activities of daily living
J Lu, J Wang, X Zheng, C Karmakar, S Rajasegarar
(2019), pp. 1-9, ACSW 2019 : Proceedings of the Australasian Computer Science Week Multiconference, Sydney, N.S.W., E1
Detecting micro-expression intensity changes from videos based on hybrid deep CNN
S Thuseethan, S Rajasegarar, J Yearwood
(2019), Vol. 11441, pp. 387-399, PAKDD 2019 : Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discoveri and Data Mining, Macau, China, E1
Emotion intensity estimation from video frames using deep hybrid convolutional neural networks
S Thuseethan, S Rajasegarar, J Yearwood
(2019), pp. 1-10, IJCNN 2019 : Proceedings of the 2019 International Joint Conference on Neural Networks, Budapest, Hungary, E1
Deep learning and one-class SVM based anomalous crowd detection
M Yang, S Rajasegarar, S Erfani, C Leckie
(2019), Vol. 2019-July, IJCNN 2019 : International Joint Conference on Neural Networks, Budapest, Hungary, E1
Insider attacks on Zigbee based IoT networks by exploiting AT commands
W Piracha, M Chowdhury, B Ray, S Rajasegarar, R Doss
(2019), Vol. 1116, pp. 77-91, ATIS 2019 : Proceedings of the 10th Applications and Techniques in Information Security Conference 2019, Tamil Nadul, India, E1
Deep hybrid spatiotemporal networks for continuous pain intensity estimation
S Thuseethan, S Rajasegarar, J Yearwood
(2019), Vol. 11955, pp. 449-461, APNNS 2019 : Proceedings of the 26th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Society 2019, Sydney, N.S.W., E1
Real-time urban microclimate analysis using Internet of Things
P Rathore, A Rao, S Rajasegarar, E Vanz, J Gubbi, M Palaniswami
(2018), Vol. 5, pp. 500-511, IEEE internet of things journal, Piscataway, N.J., C1
Ensemble fuzzy clustering using cumulative aggregation on random projections
P Rathore, J Bezdek, S Erfani, S Rajasegarar, M Palaniswami
(2018), Vol. 26, pp. 1510-1524, IEEE transactions on fuzzy systems, Piscataway, N.J., C1
Efficient unsupervised parameter estimation for one-class support vector machines
Z Ghafoori, S Erfani, S Rajasegarar, J Bezdek, S Karunasekera, C Leckie
(2018), Vol. 29, pp. 5057-5070, IEEE transactions on neural networks and learning systems, Piscataway, N.J., C1
User Activity Pattern Analysis in Telecare Data
M Angelova, J Ellman, H Gibson, P Oman, S Rajasegarar, Y Zhu
(2018), Vol. 6, pp. 33306-33317, IEEE Access, C1
Fast and scalable big data trajectory clustering for understanding urban mobility
D Kumar, H Wu, S Rajasegarar, C Leckie, S Krishnaswamy, M Palaniswami
(2018), Vol. 19, pp. 3709-3722, IEEE transactions on intelligent transportation systems, Piscataway, N.J., C1
Breast cancer recurrence prediction using random forest model
T Al-Quraishi, J Abawajy, M Chowdhury, S Rajasegarar, A Abdalrada
(2018), Vol. 700, pp. 318-329, SCDM 2018 : Concise and informative : Proceedings of the 3rd International Conference on Soft Computing and Data Mining, Johor, Malaysia, E1
A Abdalrada, J Abawajy, M Chowdhury, S Rajasegarar, T Al-Quraishi, H Jelinek
(2018), Vol. 700, pp. 135-146, SCDM 2018 : Proceedings of the 3rd International Conference on Soft Computing and Data Mining 2018, Johor, Malaysia, E1
Distributed detection of zero-day network traffic flows
Y Miao, L Pan, S Rajasegarar, J Zhang, C Leckie, Y Xiang
(2018), Vol. 845, pp. 173-191, AusDM 2017 : Proceedings of the 15th Australasian Data Mining Conference 2017, Melbourne, Vic., E1
P Rathore, D Kumar, S Rajasegarar, M Palaniswami
(2018), pp. 598-603, IEEE WF-IoT 2018 : Proceedings of 4th IEEE World Forum on Internet of Things, Singapore, E1
Graph stream mining based anomalous event analysis
M Yang, L Rashidi, S Rajasegarar, C Leckie
(2018), Vol. 11012, pp. 891-903, PRICAI 2018: Proceedings of the Pacific Rim International Conference on Artifical Intelligence: Trends in Artificial Intelligence, Nanjing, China, E1
Approximate cluster heat maps of large high-dimensional data
P Rathore, J Bezdek, D Kumar, S Rajasegarar, M Palaniswami
(2018), pp. 195-200, ICPR 2018 : Proceedings of the 24th International Conference on Pattern Recognition, Beijing, China, E1
Crowd activity change point detection in videos via graph stream mining
M Yang, L Rashidi, S Rajasegarar, C Leckie, A Rao, M Palaniswami
(2018), pp. 328-336, CVPRW 2018 : Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, Ut., E1
Scalable bottom-up subspace clustering using FP-trees for high dimensional data
M Doan, J Qi, S Rajasegarar, C Leckie
(2018), pp. 106-111, Big Data : Proceedings of the 2018 IEEE International Conference on Big Data, Seattle, Wash., E1
Cluster-based crowd movement behavior detection
M Yang, L Rashidi, A Rao, S Rajasegarar, M Ganji, M Palaniswami, C Leckie
(2018), pp. 1-8, DICTA 2018 : Proceedings of the 2018 Digital Image Computing: Techniques and Applications, Canberra, A.C.T., E1
A visual-numeric approach to clustering and anomaly detection for trajectory data
D Kumar, J Bezdek, S Rajasegarar, C Leckie, M Palaniswami
(2017), Vol. 33, pp. 265-281, Visual computer, Berlin, Germany, C1-1
Maximum entropy-based auto drift correction using high- and low-precision sensors
P Rathore, D Kumar, S Rajasegarar, M Palaniswami
(2017), Vol. 13, ACM Transactions on Sensor Networks, C1
Fog-empowered anomaly detection in IoT using hyperellipsoidal clustering
L Lyu, J Jin, S Rajasegarar, X He, M Palaniswami
(2017), Vol. 4, pp. 1174-1184, IEEE Internet of Things Journal, C1
Clustering aided support vector machines
G Ristanoski, R Soni, S Rajasegarar, J Bailey, C Leckie
(2017), Vol. 10358, pp. 322-334, MLDM 2017 : Proceedings of the 13th International Machine Learning and Data Mining in Pattern Recognition Conference, New York, USA, E1
Orness and cardinality indices for averaging inclusion-exclusion integrals
A Honda, S James, S Rajasegarar
(2017), Vol. 10571, pp. 51-62, MDAI 2017 : Proceedings of the 14th International Modeling Decisions for Artificial Intelligence Conference, Kitakyushu, Japan, E1
Improving load forecasting based on deep learning and K-shape clustering
F Fahiman, S Erfani, S Rajasegarar, M Palaniswami, C Leckie
(2017), pp. 4134-4141, IJCNN 2017 : Proceedings of the International Joint Conference on Neural Networks 2017, Anchorage, Alaska, E1-1
Non-invasive sensor based automated smoking activity detection
B Bhandari, JianChao Lu, Xi Zheng, S Rajasegarar, C Karmakar
(2017), pp. 845-848, EMBC 2017 : Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Seogwipo, South Korea, E1
Acoustic and device feature fusion for load recognition
A Zoha, A Gluhak, M Nati, M Imran, S Rajasegarar
(2016), Vol. 586, pp. 287-300, Novel applications of intelligent systems, Berlin, Germany, B1-1
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
S Erfani, S Rajasegarar, S Karunasekera, C Leckie
(2016), Vol. 58, pp. 121-134, Pattern recognition, Amsterdam, The Netherlands, C1-1
A hybrid approach to clustering in big data
D Kumar, J Bezdek, M Palaniswami, S Rajasegarar, C Leckie, T Havens
(2016), Vol. 46, pp. 2372-2385, IEEE transactions on cybernetics, Piscataway, N.J., C1-1
Adaptive cluster tendency visualization and anomaly detection for streaming data
D Kumar, J Bezdek, S Rajasegarar, M Palaniswami, C Leckie, J Chan, J Gubbi
(2016), Vol. 11, ACM Transactions on Knowledge Discovery from Data, C1-1
Pedestrian behaviour analysis using the microsoft kinect
J Chen, S Rajasegarar, C Leckie, A Gygax
(2016), pp. 1-6, PerCom Workshops 2016: Proceedings of the 13th IEEE International Conference on Pervasive Computing and Communication Workshops, Sydney, N.S.W., E1
Unsupervised parameter estimation for one-class support vector machines
Z Ghafoori, S Rajasegarar, S Erfani, S Karunasekera, C Leckie
(2016), Vol. 9652, pp. 183-195, PAKDD 2016 : Advances in Knowledge Discovery and Data Mining : 20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19-22, 2016 Proceedings, Part II, Auckland, New Zealand, E1
Node re-ordering as a means of anomaly detection in time-evolving graphs
L Rashidi, A Kan, J Bailey, J Chan, C Leckie, W Liu, S Rajasegarar, K Ramamohanarao
(2016), Vol. 9852, pp. 162-178, ECML PKDD 2016 : Machine learning and knowledge discovery in databases : Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Riva del Garda, Italy, E1
R1STM: one-class support tensor machine with randomised kernel
S Erfani, M Baktashmotlagh, S Rajasegarar, V Nguyen, C Leckie, J Bailey, K Ramamohanarao
(2016), pp. 198-206, SDM 2016 : Proceedings of 2016 SIAM International Conference on Data Mining, Miami, Fla., E1
Anomalous behavior detection in crowded scenes using clustering and spatio-temporal features
M Yang, S Rajasegarar, A Rao, C Leckie, M Palaniswami
(2016), Vol. 486, pp. 132-141, IFIP 2016 : Proceedings of the 9th Intelligent Information Processing International Conference, Melbourne, Victoria, E1
Anomaly detection in non-stationary data: ensemble based self-adaptive OCSVM
Z Ghafoori, S Erfani, S Rajasegarar, S Karunasekera, C Leckie
(2016), pp. 2476-2483, IJCNN 2016: Proceedings of the IEEE International Joint Conference on Neural Networks, Vancouver, Canada, E1
Geospatial estimation-based auto drift correction in wireless sensor networks
D Kumar, S Rajasegarar, M Palaniswami
(2015), Vol. 11, pp. 1-39, ACM transactions on sensor networks, New York, N. Y., C1-1
An embedding scheme for detecting anomalous block structured graphs
L Rashidi, S Rajasegarar, C Leckie
(2015), Vol. 9078, pp. 215-227, Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015. Proceedings, Part II, Ho Chi Minh City, Vietnam, E1-1
Pattern based anomalous user detection in cognitive radio networks
S Rajasegarar, C Leckie, M Palaniswami
(2015), pp. 5605-5609, ICASSP 2015 : Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Brisbane, Qld., E1-1
DP1SVM: a dynamic planar one-class support vector machine for Internet of Things environment
A Shilton, S Rajasegarar, C Leckie, M Palaniswami
(2015), pp. 1-6, RIoT 2015 : Proceedings of the 2015 International Conference on Recent Advances in Internet of Things, Singapore, Singapore, E1-1
Parking availability prediction for sensor-enabled car parks in smart cities
Y Zheng, S Rajasegarar, C Leckie
(2015), pp. 1-6, ISSNIP 2015 : Proceedings of the 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Singapore, E1-1
Profiling pedestrian distribution and anomaly detection in a dynamic environment
M Doan, S Rajasegarar, M Salehi, M Moshtaghi, C Leckie
(2015), Vol. 19-23-Oct-2015, pp. 1827-1830, CIKM 2015: Proceedings of the Information and Knowledge Management 2015 International Conference, Melbourne, Vic., E1-1
R1SVM: a randomised nonlinear approach to large-scale anomaly detection
S Erfani, M Baktashmotlagh, S Rajasegarar, S Karunasekera, C Leckie
(2015), Vol. 1, pp. 432-438, AAAI 2015: Proceedings of the Artificial Intelligence 2015 Conference, Austin, Texas, E1-1
DPISVM: a dynamic planar one-class support vector machine for internet of things environment
A Shilton, S Rajasegarar, C Leckie, M Palaniswami
(2015), pp. 1-6, RIoT 2015 : Proceedingins of the International Conference on Recent Advances in Internet of Things, Singapore, E1-1
Streaming analysis in wireless sensor networks
M Moshtaghi, J Bezdek, T Havens, C Leckie, S Karunasekera, S Rajasegarar, M Palaniswami
(2014), Vol. 14, pp. 905-921, Wireless communications and mobile computing, Chichester, Eng., C1-1
An adaptive elliptical anomaly detection model for wireless sensor networks
M Moshtaghi, C Leckie, S Karunasekera, S Rajasegarar
(2014), Vol. 64, pp. 195-207, Computer networks, Amsterdam, The Netherlands, C1-1
S Rajasegarar, A Gluhak, M Ali Imran, M Nati, M Moshtaghi, C Leckie, M Palaniswami
(2014), Vol. 47, pp. 2867-2879, Pattern recognition, Chatswood, N.S.W., C1-1
Anomaly detection in wireless sensor networks in a non-stationary environment
C Oreilly, A Gluhak, M Imran, S Rajasegarar
(2014), Vol. 16, pp. 1413-1432, IEEE communications surveys and tutorials, Piscataway, N.J., C1-1
Hyperspherical cluster based distributed anomaly detection in wireless sensor networks
S Rajasegarar, C Leckie, M Palaniswami
(2014), Vol. 74, pp. 1833-1847, Journal of parallel and distributed computing, Amsterdam, The Netherlands, C1-1
High-resolution monitoring of atmospheric pollutants using a system of low-cost sensors
S Rajasegarar, T Havens, S Karunasekera, C Leckie, J Bezdek, M Jamriska, A Gunatilaka, A Skvortsov, M Palaniswami
(2014), Vol. 52, pp. 3823-3832, IEEE transactions on geoscience and remote sensing, Piscataway, N. J., C1-1
Profiling spatial and temporal behaviour in sensor networks: a case study in energy monitoring
L Rashidi, S Rajasegarar, C Leckie, M Nati, A Gluhak, M Imran, M Palaniswami
(2014), pp. 1-7, IEEE ISSNIP 2014 : Proceedings of the 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Singapore, Singapore, E1-1
Smart car parking: temporal clustering and anomaly detection in urban car parking
Y Zheng, S Rajasegarar, C Leckie, M Palaniswami
(2014), pp. 1-6, IEEE ISSNIP 2014 : Proceedings of the 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Singapore, Singapore, E1-1
High resolution spatio-temporal monitoring of air pollutants using wireless sensor networks
S Rajasegarar, P Zhang, Y Zhou, S Karunasekera, C Leckie, M Palaniswami
(2014), pp. 1-6, IEEE ISSNIP 2014 : Proceedings of the 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Singapore, Singapore, E1-1
Detection of anomalous crowd behaviour using hyperspherical clustering
A Rao, J Gubbi, S Rajasegarar, S Marusic, M Palaniswami
(2014), pp. 1-8, DICTA 2014 : Proceedings of the Digital Image Computing : Techniques and Applications International Conference, Wollongong, New South Wales, E1-1
S Rajasegarar, C Leckie, M Palaniswami
(2014), pp. 4536-4541, IEEE ICC 2014 : Communications: the centrepoint of the digital economy : Proceedings of the 2014 IEEE International Conference on Communications, Sydney, N.S.W., E1-1
Combined multiclass classification and anomaly detection for large-scale wireless sensor networks
A Shilton, S Rajasegarar, M Palaniswami
(2013), pp. 491-496, IEEE ISSNIP 2013 : Proceedings of the 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Vic., E1-1
Automatic sensor drift detection and correction using spatial kriging and kalman filtering
D Kumar, S Rajasegarar, M Palaniswami
(2013), pp. 183-190, DCoSS 2013 : Proceedings of the 2013 IEEE International Conference on Distributed Computing in Sensor Systems, Cambridge, Mass., E1-1
ClusiVAT: a mixed visual/numerical clustering algorithm for big data
D Kumar, M Palaniswami, S Rajasegarar, C Leckie, J Bezdek, T Havens
(2013), pp. 112-117, Big Data 2013 : Proceedings of the 2013 IEEE International Conference on Big Data, Silicon Valley, Calif., E1-1
Optimization of an energy harvesting buoy for coral reef monitoring
A Pirisi, F Grimaccia, M Mussetta, R Zich, R Johnstone, M Palaniswami, S Rajasegarar
(2013), pp. 629-634, CEC 2013 : Proceedings of the 2013 IEEE Congress on Evolutionary Computation, Cancun, Mexico, E1-1
Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey.
A Zoha, A Gluhak, M Imran, S Rajasegarar
(2012), Vol. 12, pp. 16838-16866, Sensors, Basel, Switzerland, C1-1
Measures for clustering and anomaly detection in sets of higher dimensional ellipsoids
S Rajasegarar, J Bezdek, M Moshtaghi, C Leckie, T Havens, M Palaniswami
(2012), pp. 1-8, WCCI 2012 : Proceedings of the IEEE World Congress on Computational Intelligence, Brisbane, Queensland, E1-1
Acoustic and device feature fusion for load recognition
A Zoha, A Gluhak, M Nati, M Imran, S Rajasegarar
(2012), pp. 386-392, IS 2012 : Intelligent systems: methodology, systems, applications in emerging technologies : Proceedings of the 2012 6th IEEE International Conference Intelligent Systems, Sofia, Bulgaria, E1-1
Online anomaly rate parameter tracking for anomaly detection in wireless sensor networks
C O'Reilly, A Gluhak, M Imran, S Rajasegarar
(2012), Vol. 1, pp. 191-199, SECON 2012 : Proceedings of the 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, Seoul, South Korea, E1-1
Anomaly detection in environmental monitoring networks
J Bezdek, S Rajasegarar, M Moshtaghi, C Leckie, M Palaniswami, T Havens
(2011), Vol. 6, pp. 52-58, IEEE Computational Intelligence Magazine, Piscataway, N.J., C1-1
Clustering ellipses for anomaly detection
M Moshtaghi, T Havens, J Bezdek, L Park, C Leckie, S Rajasegarar, J Keller, M Palaniswami
(2011), Vol. 44, pp. 55-69, Pattern Recognition, Amsterdam, The Netherlands, C1-1
An efficient hyperellipsoidal clustering algorithm for resource-constrained environments
M Moshtaghi, S Rajasegarar, C Leckie, S Karunasekera
(2011), Vol. 44, pp. 2197-2209, Pattern recognition, Amsterdam, The Netherlands, C1-1
Spatio-temporal modelling-based drift-aware wireless sensor networks
M Takruri, S Rajasegarar, S Challa, C Leckie, M Palaniswami
(2011), Vol. 1, pp. 110-122, IET Wireless Sensor Systems, Stevenage, Eng., C1-1
Incremental elliptical boundary estimation for anomaly detection in wireless sensor networks
M Moshtaghi, C Leckie, S Karunasekera, J Bezdek, S Rajasegarar, M Palaniswami
(2011), pp. 467-476, ICDM 2011 : Proceedings of the 2011 IEEE 11th International Conference on Data Mining, Vancouver, Canada, E1-1
An efficient approach to detecting concept-evolution in network data streams
S Erfani, S Rajasegarar, C Leckie
(2011), pp. 1-7, ATNAC 2011 : Proceedings of the 2011 Australasian Telecommunication Networks and Applications Conference, Melbourne, Vic., E1-1
S Rajasegarar, C Leckie, J Bezdek, M Palaniswami
(2010), Vol. 5, pp. 518-533, IEEE transactions on information forensics and security, Piscataway, N.J., C1-1
Elliptical anomalies in wireless sensor networks
S Rajasegarar, J Bezdek, C Leckie, M Palaniswami
(2009), Vol. 6, pp. 7:1-7:28, ACM transactions on sensor networks, New York, N.Y., C1-1
Anomaly detection in wireless sensor networks
S Rajasegarar, C Leckie, M Palaniswami
(2008), Vol. 15, pp. 34-40, IEEE Wireless Communications, Piscataway, N.J., C1-1
Funded Projects at Deakin
Australian Competitive Grants
Learning the Focus of Attention to Detect Distributed Coordinated Attacks
Prof Christopher Leckie, Prof Ramamohanarao Kotagiri, Dr Sarah Erfani, Dr Sutharshan Rajasegarar, Prof Vipin Kumar
ARC - Discovery Projects
- 2022: $46,500
- 2021: $27,000
- 2020: $27,000
Other Public Sector Funding
Graph-learning for group activity classification.
Dr Sutharshan Rajasegarar, Dr Lei Pan
CSIRO Scholarship - Commonwealth Scientific and Industrial Research Organisation
- 2021: $10,000
Industry and Other Funding
Machine Learning and Optimisation for Adaptive Traffic Vehicle Routing.
Dr Dhananjay Thiruvady, Dr Sutharshan Rajasegarar, Dr Sergey Polyakovskiy
CAT3-1 Premonition.io Pty Ltd
- 2021: $10,098
Supervisions
Thuseethan Selvarajah
Thesis entitled: Deep Emotions: In-depth Emotion Recognition using Deep Learning
Doctor of Philosophy (Information Technology), School of Information Technology
Chee Keong (Allan) Ng
Thesis entitled: VoterChoice: A Ransomware Detection Honeypot with Multiple Voting Concept
Doctor of Philosophy (Information Technology), School of Information Technology