New Zealand Summer Workshop

10th November 2023

Japan-New Zealand Catalyst Grant

Assoc Professor Ian Welch

ian.welch@vuw.ac.nz
https://www.linkedin.com/in/ianswelch
Te Herenga Waka, Victoria University of Wellington

Who am I?

  • Associate Professor @ Victoria University of Wellington (VUW)
  • PhD and Masters (Newcastle UK), BCom (VUW)
  • Application security policies, malicious and fault tolerant systems, loosely coupled self-adaptive software systems
  • Lead cybersecurity teaching stream at VUW
  • Focus on network applications and people

Owhiti research group

Prior work

Privacy ensuring e-commerce transactions (Ben Palmer) - multipart computation, interactive zero knowledge proofs, formal modeling

Localisation of Attacks, Combating Browser-Based Geo-Information and IP Tracking Attacks (Masood Mansoori) - empirical, hazop for experiments

Early detection of ransomware (Shabbir Abbasi) - evolutionary computing, sequence aware machine learning

One-shot learning for malware detection (Jinting Zhu) - siamese neural networks, adversary machine learning

Automated threat analysis for IoT devices (Junaid Haseeb - unsupervised machine learning, autoencoders, extraction semantic information)

 

Projects soon to finish

  • Detection of advanced persistent attacks
  • (Abdullah Al-Mamoun) - long lasting, slow, few examples - hybrid fusion of data sources, feature selection, evolutionary computing
  • Adaptable network application honeypots (Maryam Van Naseri) - optimisation problem, formal modeling, empirical experiments
  • Understanding and influencing home users cybersecurity
  • (Lisa Patterson) - quantiative and qualitative research, social nudges to improve cybersecurity

New and ongoing projects

  • Explainable AI for cybersecurity (Dedy Hendro, 2027) - xAI, intrusion detection
  • Trusted computing without hardware support (Paul Dagger, parttime, 2028) - multiparty computation
  • Mobile-first Disease detection for native NZ flora (Jamey Hepi, 2026)
  • Safety and security for consumer cyberphysical systems (interdisciplinary)
  • Effective cybersecurity programmes in the Pacific (interdisciplinary)
  • Indigicloud - edge clouds for digital marae - working with Te Tii Marae - applying for grant related to zero knowledge architecture and continuous authentication
  • Intrusion detection for CPS using digital twins (waiting)
  • Improving TOR anonymity (visitor)

Collaboration ideas

  • Malware detection, intrusion detection - feature construction/evolutionary computing (w/ Harith Al-Sahaf).
  • Use of intelligent transport systems as case studies for the new and incoming students working on explanatory AI (w/ Yi Mei, Andrew Lensen) & digital twins (w/ Masood Mansoori, Arman Khouzani).
  • Deployment of honeypots in other organisations (w/ Masood Mansoori)
  • Adversary machine learning (no current students, opportunity to explore with honours students).

 

 

Catalyst grant

Potential avenues.

Cybersecurity for Intelligent Transport Systems.

- cars, pedestrians, (cycles)

- smart city interaction

Two recent survey papers:

- threats from malware

- adverserial machine learning

 

Malware

Inter-vehicle denial-of-service attacks.

Targets vehicles or infrastructure.

Basic - resource exhaustion.

Extended - jam channels.

Distributed denial of services - vehicles and infrastructure.

Solutions (voting to ignore bad actors, blockchain)

 

 

Text

Al-Sabaawi, A., Al-Dulaimi, K., Foo, E., & Alazab, M. (2021). Addressing Malware Attacks on Connected and Autonomous Vehicles: Recent Techniques and Challenges. In M. Stamp, M. Alazab, & A. Shalaginov (Eds.), Malware Analysis Using Artificial Intelligence and Deep Learning (pp. 97–119). Springer International Publishing. https://doi.org/10.1007/978-3-030-62582-5_4
 

Malware

Inter-vehicle other attacks.

GPS spoofing - powergrid solution is spoofing detection).

Masquerading and Sybil attacks - concealment and duplication.

Impersonation - steal identity.

Effects - fake damaging roadway, traffic congestion, crashes.

 

 

 

Malware

Intra-vehicle communication.

- indirect physical access (media system via MP3 files, docking ports, CAN bus via ODB port)

- short-range wireless access (bluetooth, keyless entry, tire pressure)

- long-range wireless (remote telematics, cellular access)

 

 

 

Defences

Inter-vehicle communication.

  • voting to manage VAN membership
  • trust-based sensor networks
  • authentication

Intra-vehicle communication.

  • CAN replaces multiple wires
  • Only once way to access in the past, now multiple
  • Move towards ethernet and more interoperability
  • Intrusion detection
    • CVguard (DDoS prevention
    • Anomaly detection approaches

 

 

 

What's the takeaway?

 

Anomaly detection looks possible area mirroring other systems.

But there are challenges specific to transport

- secure vs safe

- real-time constraints

 

 

 

Adversarial Attacks and Defence

  • High-profile projects by Google, General Motors, Toyota, and Tesla have marked milestones in AV development.
  • Despite advancements, AVs' safety is challenged by cybersecurity threats and adversarial attacks.
  • Driving automation for cars relies upon Deep Learning, used for image recognition and fusion of sensors.

M. Girdhar, J. Hong and J. Moore, "Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models," in IEEE Open Journal of Vehicular Technology, vol. 4, pp. 417-437, 2023, doi: 10.1109/OJVT.2023.3265363.

Adversarial Attacks and Defence

  • AVs use ML in four key areas: perception, prediction, planning, and control.

  • ML algorithms handle tasks like object detection, traffic sign recognition, and decision-making.

  • AVs' ML pipeline links sensory inputs to actuator controls, enabling self-driving capabilities.

  • Intentional threats include attacks exploiting AI weaknesses to impair AV operations.

  • Unintentional risks involve algorithm biases, design flaws, and limitations of AI and ML techniques.

Adversarial Attacks and Defence

  • ML models can be manipulated by adversarial attacks during training or post-training.

  • Adversarial ML poses a risk as attackers can deceive AI systems, causing incorrect predictions.
  • Defences have been proposed in other areas that could be applicable for this context.
  • These include proactive and reactive defences applied in other contexts.

Adversarial Attacks and Defence

  • Proactive defenses attempt to strengthen a neural network's resistance toward adversarial examples in advance of an attack.

  • Reactive defenses aim at detecting adversarial input observations or examples after the neural network models are trained.

  • These techniques can also be broadly categorized into three groups, 1) manipulating data, 2) introducing auxiliary models, and 3) altering models.

What's
the takeaway?

There are many state-of-the-art defences.

Haven't been (necessarily) validated in this context.

Must also meet safety requirements that other context might not have?