|
|
|
Remotely Monitor and Detect Cybersecurity Anomalies

Remotely Monitor and Detect Cybersecurity Anomalies

Thematic Area: Digitalisation

14) How might we develop lightweight, scalable, and network-agnostic solutions to remotely monitor and detect cybersecurity anomalies, vulnerabilities, events, or incidents?

BACKGROUND

The maritime sector’s increasing adoption of AI, autonomous vehicles and vessels, 5G, and IoT technologies is transforming port and vessel operations.

MPA also intends to strengthen its 5G coverage in Singapore Port waters to support the growing digitalisation of vessels, enabling reliable ship-to-shore communications and seamless connectivity for sensors.

However, these advancements also introduce new risks of cyberattacks across maritime Operations Technology (OT), remote drone operations, and unmanned/electric vessels and vehicles. Conventional IT cybersecurity tools are insufficient for addressing the unique characteristics of maritime OT environments.

SIGNIFICANCE OF PROBLEM

A successful cyberattack against OT systems used in maritime operating environment can cause debilitating impact against efficiency, safety and productivity of Ports of Singapore and potential risks to vessel navigation safety.

Given the multi-protocol, multi-device, and hybrid communication layers in use, detecting cybersecurity threats early is challenging, and limited manpower makes round-the-clock response difficult.

POTENTIAL MARKET SIZE

The global maritime cybersecurity market is estimated to be at $4.2B currently, it’s projected to grow rapidly with smart port developments. Port OT specific cybersecurity and AI-driven Security Operations Centre (SOC) solutions for autonomous systems represent an emerging $1B+ opportunity. Tuas Port alone is a multi-billion-dollar digital infrastructure asset requiring bespoke solutions.

EXISTING EFFORTS

Fragmented endpoint monitoring on shipboard OT and drones. The Port of Singapore has piloted CyberSOC initiatives, but integration with autonomous maritime assets is limited. Most systems rely on signature-based detection, lack contextual awareness, and have limited real-time capabilities across 5G and legacy networks.

Gaps in existing efforts:

  • Anomaly detection across Port OT assets, drones, and USVs are fragmented
  • Solutions are not lightweight and footprint are larger than required
  • Many solutions only work specifically over LTE, satellite comm., RF mesh and not 5G
  • Interoperability with CyberSOC
  • AI-enhanced threat prediction & prioritization