A/Prof. Sutharshan Rajasegarar

Quantum Autoencoder Frameworks for Network Anomaly Detection

Moe Hdaib, S Rajasegarar, Lei Pan

(2024), pp. 69-82, ICONIP 2023 : Proceedings of the 30th International Conference on Neural Information Processing, Changsha, China, E1

An Improved Visual Assessment with Data-Dependent Kernel for Stream Clustering

Baojie Zhang, Yang Cao, Ye Zhu, Sutharshan Rajasegarar, Hong Li, Maia Angelova Turkedjieva, Gang Li

(2023), Vol. 13935, pp. 197-209, PAKDD 2023 : Advances in Knowledge Discovery and Data Mining: 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part I, Osaka, Japan, E1

Double Attention-Based Lightweight Network for Plant Pest Recognition

J Sivasubramaniam, T Selvarajah, S Rajasegarar, J Yearwood

(2023), Vol. 1793, pp. 598-611, ICONIP 2022 : Proceedings of the 29th Neural Information Processing Conference, Virtual Event, E1

EnSpeciVAT: Enhanced SpecieVAT for cluster tendency identification in graphs

S Xia, S Rajasegarar, C Leckie, S Erfani, J Chan, Lei Pan

(2023), Vol. 14178, pp. 323-337, ADMA 2023 : Proceedings of the 19th International Conference on Advanced Data Mining and Applications, Shenyang, China, E1

It's PageRank All The Way Down: Simplifying Deep Graph Networks

D Jack, S Erfani, J Chan, S Rajasegarar, C Leckie

(2023), pp. 172-180, SDM 2023 : Proceedings of the SIAM International Conference on Data Mining, Minneapolis, Minnesota, E1

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

Error Spectrum Analysis of Solar Power Prediction for Deakin Microgrid Digital Twin

I Natgunanathan, A Anwar, S Rajasegarar, V Mak-Hau

(2022), Vol. 2022-November, pp. 1-6, APPEEC 2022 : Proceedings of the 14th Asia-Pacific Power and Energy Engineering Conference, Melbourne, Victoria, E1

DμDT: The Deakin University Microgrid Digital Twin

V Mak-Hau, A Henkel, M Abdelrazek, S Rajasegarar, A Anwar, A Fletcher

(2022), Vol. 2022-November, pp. 1-6, APPEEC 2022 : Proceedings of the 14th Asia-Pacific Power and Energy Engineering Conference, Melbourne, Victoria, E1

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

IoT insider attack - survey

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

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

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

Relationship between Angiotensin Converting Enzyme gene and Cardiac Autonomic Neuropathy among Australian population

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

Bayesian maximum entropy and interacting multiple model based automatic sensor drift detection and correction in an IoT environment

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

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

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

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

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

Spatio-temporal estimation with Bayesian maximum entropy and compressive sensing in communication constrained networks

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

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

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