From Noise to Clarity

One of the biggest challenges I’ve seen in Perimeter Intrusion Detection Systems (PIDS) is finding the right balance between a high Probability of Detection (POD) and a low Nuisance Alarm Rate (NAR). It’s a pain point that keeps coming up in conversations with security professionals across the globe. The trade-off has long plagued the industry: how do you build a system that is sensitive enough to detect real intrusions without inundating operators with false alarms?

In my experience, high-sensitivity systems can be a double-edged sword. They detect even minor disturbances like wildlife, wind, or vibrations, triggering alarms and causing “alarm fatigue.” When operators lose trust in the system, it can lead to delayed responses, missed intrusions, or even systems being switched off altogether, an outcome that’s as dangerous as it sounds.

This issue is particularly critical in industries like corrections, energy and oil and gas, where a secure perimeter is essential. Whether it’s keeping a maximum-security prison secure or safeguarding oil pipelines from potential sabotage, the need for reliable PIDS is non-negotiable. Yet, nuisance alarms continue to be a thorn in the side of operators. That’s where I believe the game-changing combination of fiber-optic Distributed Acoustic Sensors (DAS) and Deep Learning comes in to rewrite the rulebook on perimeter security.

Fiber-optic DAS: The Backbone of Modern PIDS

Fiber-optic DAS has emerged as a preferred choice for PIDS. The technology uses fiber-optic cables to detect vibrations and acoustic signals, providing advantages such as immunity to electromagnetic interference, passive operation with no field power requirements, and precise detection capabilities.

While fiber-optic PIDS systems have been commercially available since the 1990s, the advancements in signal processing and optical design in recent years have made it more viable than ever. Today, DAS is deployed across extensive perimeters and challenging environments. Some standout examples include:

• Corrections: In the U.S., DAS systems are used along high-security prison perimeters to monitor breakout attempts or breaches, providing real-time alerts that distinguish between real threats and benign activities like wildlife or weather.
• Oil and Gas: In pipeline security, DAS technology detects potential threats such as unauthorized digging, vehicle vibrations, and even seismic disturbances that could indicate sabotage attempts. With the vast network of pipelines crisscrossing the United States, DAS has become a critical tool in protecting energy infrastructure.

However, while DAS systems have proven effective, their high sensitivity has historically come at the cost of higher NAR. This is where Deep Learning truly shines, bridging the gap in ways that weren’t possible before.

Deep Learning: The Intelligence Behind Modern PIDS

Deep Learning (DL), a subset of Artificial Intelligence (AI), is a buzzword we’ve all heard, but its impact on PIDS is genuinely transformative. By using neural networks to process massive amounts of data, DL systems can identify patterns and classify events with a level of accuracy that’s almost impossible for traditional methods to match. This technology doesn’t just follow pre-set rules; it learns from raw data and gets better over time.

In PIDS, this capability is a gamechanger. By leveraging DL,, DAS systems can differentiate between genuine intrusions and nuisance events. For instance:

• In a high-security prison, environmental factors like wind or nearby construction can trigger arms. A DL enabled system learns to ignore these distractions while still maintaining high sensitivity to tunnelling, climbing or fence-cutting attempts.
• Similarly, for oil pipelines, the system can distinguish between harmless ground vibrations caused by passing vehicles and those from unauthorized digging, a critical capability in protecting national energy assets.

FFT’s Aura Ai-X: A Proven Solution

Future Fibre Technologies (FFT) has been at the forefront of integrating DL into fiber-optic DAS systems. FFT’s flagship product, Aura Ai-X, demonstrates the practical benefits of this approach. FFT has amassed an extensive library of data from diverse environments, corrections facilities, oil refineries, airports, and more, allowing the DL models to adapt to specific conditions. These models are securely deployed to Aura Ai-X systems, where they process real-time data from the fiber sensors.

The results? High POD, minimal NAR, and, most importantly, restored operator confidence. Some examples include:

• At a U.S. correctional facility, Aura Ai-X reduced nuisance alarms by over 91%, allowing security teams to focus on real threats without distraction.
• In the oil and gas sector, FFT’s system has been deployed along pipelines in Texas, where it successfully identified and classified potential threats with near-perfect accuracy, preventing costly disruptions and potential damage.

Real-World Testing: Proof of Concept

These aren’t just theoretical benefits. FFT recently conducted a test at a major oil refinery on the Gulf Coast, where the system faced challenges such as vibrations from nearby highways, heavy machinery, and extreme weather conditions. Traditional signal processing methods struggled to filter out nuisance alarms. However, Aura Ai-X, equipped with a DL engine, reduced false alarms to near zero, while maintaining high sensitivity.

Another trial at a Midwestern correctional facility further highlighted the system’s capabilities. The DAS system, combined with DL, accurately detected multiple intrusion attempts while ignoring benign events like animals lading on the perimeter or wind disturbances.

Trust and Reliability: The Human Factor

I truly believe that trust is at the heart of any successful security system. Operators need to rely on their tools without second guessing them. A PIDS plagued by false alarms quickly loses credibility. DL enhanced PIDS, like Aura Ai-X, rebuilds that trust by delivering accurate, actionable alerts when they matter most.

The Future of PIDS

Looking ahead, I’m confident that Deep Learning will continue to push the boundaries of what PIDS can achieve. For industries like corrections and critical infrastructure, this represents a huge step forward in protecting high value assets. With systems like Aura Ai-X, we’re moving into an era where nuisance alarms are a thing of the past, replaced by intelligent, reliable, and trustworthy solutions.

Security teams can focus on what truly matters.  protecting people, assets, and infrastructure without unnecessary distractions. Deep Learning isn’t just a tool for improving PIDS; it’s the foundation for a smarter, safer future.

Andrew Holysz
Director of North America, Critical Infrastructure
Future Fibre Technologies