Ms Rizka Purwanto

Ms Rizka Purwanto

Adjunct Associate Lecturer
UNSW Canberra
School of Eng & Tech

Rizka is currently an Adjunct Associate Lecturer at University of New South Wales and an Assistant Professor to the Master of Cybersecurity with Monash University, Indonesia. Prior to this role, she was a postdoctoral researcher at UNSW Canberra Space, with past experiences working as a software engineer in Australia and Indonesia.

Her research interests are mainly in the application of artificial intelligence in various areas. Her current research interests include scams and phishing attacks, specifically on improving cybersecurity awareness, and developing AI based methods that could automatically help detect phishing attacks and scams. Besides AI for cybersecurity, she is also currently working in the area of AI for space, particularly on the use of federated learning on miniaturised satellite constellations, and space object characterisation using lightcurve data with deep learning.

Rizka received the bachelor’s degree in electrical engineering from Institut Teknologi Bandung (ITB), Indonesia, in 2013, and the master’s degree from the University of New South Wales (UNSW) in 2018, concentrating on artificial intelligence and internetworking. She completed her PhD in 2022, from the School of Computer Science and Engineering at UNSW. Her PhD was funded by the University International Postgraduate Award (UIPA) and Cyber Security Cooperative Research Centre scholarships.

 

  • Journal articles | 2022
    Alibasa MJ; Purwanto RW; Priyadi Y; Riskiana RR, 2022, 'Towards Generating Unit Test Codes Using Generative Adversarial Networks', Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 6, pp. 305 - 314, http://dx.doi.org/10.29207/resti.v6i2.3940
    Journal articles | 2022
    Alibasa MJ; Purwanto RW; Yacef K; Glozier N; Calvo RA, 2022, 'Doing and Feeling: Relationships Between Moods, Productivity and Task-Switching', IEEE Transactions on Affective Computing, 13, pp. 1140 - 1154, http://dx.doi.org/10.1109/TAFFC.2020.3029440
    Journal articles | 2022
    Purwanto RW; Pal A; Blair A; Jha S, 2022, 'PhishSim: Aiding Phishing Website Detection With a Feature-Free Tool', IEEE Transactions on Information Forensics and Security, 17, pp. 1497 - 1512, http://dx.doi.org/10.1109/TIFS.2022.3164212
    Journal articles | 2021
    Purwanto R; Pal A; Blair A; Jha S, 2021, 'Man versus Machine: AutoML and Human Experts' Role in Phishing Detection', , http://arxiv.org/abs/2108.12193v1
  • Preprints | 2024
    Suleiman B; Alibasa MJ; Purwanto RW; Jeffries L; Anaissi A; Song J, 2024, Optimisation of federated learning settings under statistical heterogeneity variations, , http://arxiv.org/abs/2406.06340v1
    Conference Papers | 2022
    Brown M; Boyce R; Peters E; Gehly S; Boland S; Jeffreson R; Kremor A; Bateman T; Capon C; Smith B; Bowden G; Glina L; Qiao LL; Balage S; Gupta K; Purwanto R; Bessell T; Reddell T; Bennett J; Lachut M; McLaughlin T; Lambert A, 2022, 'Formation Flying and Change Detection for the UNSW Canberra Space ‘M2’ Low Earth Orbit Formation Flying CubeSat Mission', in AMOS 2022 Proceedings, Hawaii, presented at Advanced Maui Optical and Space Surveillance Technologies Conference, Hawaii, 19 September 2022 - 22 September 2022, http://dx.doi.org/10.26190/unsworks/28567
    Preprints | 2022
    Purwanto R; Pal A; Blair A; Jha S, 2022, PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool, , http://dx.doi.org/10.1109/TIFS.2022.3164212.
    Preprints | 2021
    Purwanto R; Pal A; Blair A; Jha S, 2021, Man versus Machine: AutoML and Human Experts' Role in Phishing Detection, , http://dx.doi.org/10.48550/arxiv.2108.12193
    Preprints | 2020
    Purwanto R; Pal A; Blair A; Jha S, 2020, PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites, , http://dx.doi.org/10.48550/arxiv.2007.11955
    Conference Papers | 2020
    Purwanto R; Paly A; Blair A; Jha S, 2020, 'PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites', in 2020 IEEE Conference on Communications and Network Security, CNS 2020, http://dx.doi.org/10.1109/CNS48642.2020.9162211