Redefining cybersecurity with a self-learning network

The digital age, with all its innovations, has also ushered in a barrage of cybersecurity threats. But what if we told you that the answer to this complex issue might lie in teaching our networks to learn, adapt, and heal themselves? Let’s introduce you to the world of ‘Self-Learning Networks’ and how BlackDice is reshaping the cybersecurity landscape with it.

Read the full blog from BlackDice CEO Paul Hague, below.

 

 

Cyber’s new dawn: the ‘self-learning network’

When we talk about a self-learning network, we’re discussing a system that’s not stuck in the past. Instead of abiding by a fixed set of rules, these networks study live data, recalibrating their defences in real-time. While many tout the capabilities of AI in cybersecurity, quoting stats like a ‘60% reduction in breach identification time’, the true potential lies in how we harness this technology. This is where BlackDice’s approach stands out – we’re using the power of unsupervised learning models to redefine our defence mechanisms.

Before we delve deeper though, let’s unpack why this approach is transformative.

 

Dataset bias

The industry’s dependence on Large Language Models (LLMs) is a double-edged sword. Yes, they’re robust and can process vast amounts of data, but they’re only as good as the data they’re trained on. When that data is biassed, our defences develop blind spots. The traditional cybersecurity models, though well-intentioned, often suffer from this limitation, leaving them vulnerable to novel threats.

 

“For far too long, we’ve seen systems that rely heavily on predefined rules and patterns, making them inherently rigid. In the rapidly evolving world of cyber threats, such rigidity in using traditional defences is akin to trying to light up a large room with a single candle; it’s simply not sufficient for the task at hand.”

 

Unsupervised learning models

Unsupervised learning models are akin to detectives with an uncanny ability to spot anomalies, even amidst the chaos. Traditional techniques, like those based on predefined signatures or rules (think ‘YARA rules’), have their merits, but they’re playing catch-up. They wait for an incident, analyse it, and then develop a response. This reactive stance is increasingly untenable in today’s landscape where threats are ever-evolving.

An unsupervised model, on the other hand, is proactive. Instead of looking for data that matches an existing rule (which someone has painstakingly written), it identifies patterns, relationships, and anomalies. As attack patterns change, the model adapts. Imagine a security system that evolves with the threat without needing constant rule updates. That’s the promise of unsupervised models.

Moreover, while traditional systems often rely on periodic scans of network or DNS data, essentially analysing events post-factum, unsupervised models operate in real-time. They detect patterns as they form, alerting us in the midst of an attack, not after. This proactive approach means that unsupervised models excel in identifying ‘zero day’ attacks – threats that haven’t occurred before, hence don’t have pre-defined detection rules. Instead of waiting for a rule to be written after the fact, the model identifies anomalies or divergences in behaviour as they happen – in real-time.

Furthermore, in the face of vast datasets, unsupervised models prove to be more scalable than traditional rule-based systems, given equal processing capabilities. They also adapt swiftly to changes in network topology, ensuring that defences remain robust regardless of the network’s evolution.

 

Addressing accuracy and human bias

Let’s face it; human error is a reality. When YARA rules or similar traditional mechanisms are employed, they need human intervention. Rules must be penned down, and that’s where potential logical errors sneak in. A misjudgment can lead to grave oversights. Consider incidents where an anomaly was mistakenly classified as a false positive. In reality these are live attacks, compromising systems in a breaches worth millions. Such blunders aren’t mere mishaps; they’re catastrophes.

In contrast, unsupervised models, by analysing vast datasets, can unearth hidden patterns without human bias. They don’t get swayed by preconceptions or focus narrowly on a single attack pattern. Instead, they consistently survey the horizon, ensuring that no threat, however novel, goes unnoticed.

 

“This is where BlackDice takes charge. Instead of confining itself to the limitations of static datasets, BlackDice’s AI-driven technology draws from the raw, unfiltered pulse of the internet – real, genuine user behaviour and activity data. This approach leads to a system that doesn’t just detect threats but also learns, adapts, and even corrects its vulnerabilities autonomously. This isn’t just a minor shift; it’s an absolute game-changer.”

 

Future scope

Cybersecurity is on the cusp of a revolution, and Self-Learning Networks might just be leading the charge. With threats growing in complexity, solutions that remain stagnant are destined to falter. BlackDice, with its dynamic approach, isn’t just a part of the future – it’s shaping it.

 

“Considering that cyber threats mutate at breakneck speed, clinging to traditional defence mechanisms is not just unwise; it’s actually dangerous.”

 

For us at BlackDice, it’s not just about stopping threats; it’s about staying several steps ahead. In this ever-evolving game of cat and mouse, we’re ensuring the mouse doesn’t stand a chance.