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The era of incredible digital growth is upon us and both individuals and businesses must learn to adapt to new challenges. Most of the aforementioned obstacles are economic or sociological at their core, but the ever-present cyberthreats are also evolving. Luckily, cybersec grows as well and has a new ally in the form of AI.
Over the past few years, machine learning has been making significant advances in numerous fields, including cybersecurity. Now it allows us to take a more dynamic approach to dealing with digital threats. This is true for everyone, ranging from individuals protecting personal data to governments looking out against national security threats.
We’re now finally able to stop playing catch-up with cybercriminals and instead stay a step ahead and be proactive in enhancing our cybersecurity efforts.
The current state of Cybersecurity
As much as the advancement of new technologies has helped cybersecurity develop, it has also burdened it with additional challenges. Malware and phishing are the most common cyber threats we face, but there are also reports of state-sponsored hacking and larger-scale operations that are much harder to detect and deal with.
With so many businesses switching to remote work models, there is now an overall larger number of individuals susceptible to these attacks. Using home networks and being deprived of the robust security that traditional workspaces used to offer both make the risks that much higher. In those cases, educating themselves on the basics of staying safe online while also using proper security tools for protection is the only way to move forward.
All that being said, even the tried and tested cybersecurity measures can sometimes fail. This stems from the fact that they are, more often than not, reactive rather than proactive, only responding to safety risks after a certain amount of damage has already been done.
This is where machine learning comes into play.
Machine Learning in Cybersecurity
By definition, machine learning is a subset of AI that uses advanced algorithms and analytics to allow computers to learn and improve from experience without additional training and programming. In simpler terms, it’s a process that helps make machines smarter over time by allowing them to access and analyze existing data patterns with the end goal of having them make seemingly intelligent decisions.
MMachine learning helps build on traditional security systems by allowing them to be more proactive in responding to threats. It also allows them to identify certain anomalies within datasets which are often indicators of incoming cyber attacks.
- Increased Efficiency: Processing and analyzing data efficiently has always been an issue with traditional cybersecurity systems. It was mostly the human element that slowed the processes down.
- Proactive Threat Detection: Advanced predictive analysis allows AI-powered cybersecurity systems to stop threats before they even happen, rather than dealing with the damage from attacks that have already occurred.
- Adaptability: Staying up to date with the latest cybersecurity threats has always been a challenge to traditional security systems. With machine learning, new patterns of attacks are identified faster than ever before and the protective algorithms can adjust in real time.
Examples of Machine Learning Algorithms in Cybersecurity
Efforts to enhance cybersecurity have reached new heights with the introduction of various algorithms that machine learning has brought to the table. Let’s discuss a few vital ones:
Anomaly Detection
Imagine having a security guard that works 24/7 and never has to rest. That’s advanced anomaly detection. It constantly scans networks for abnormal datasets and deviations from regular patterns. These indicators of potential cybersecurity threats let the entire system know that it should deploy effective countermeasures sooner rather than later.
Multilayer Perceptrons (MLPs)
At their core, MLP algorithms are types of neural networks that are most commonly used in neuroscience for pattern recognition. As you might have already guessed, this very quality makes them ideal candidates for incorporating into cybersecurity networks. Multilayer perceptrons are most effective when deployed with the sole purpose of detecting intrusions and then alerting the system. The machine learning component allows them to learn from each new interaction with the system and become more reliable over time.
Deep Learning
Deep learning algorithms are perhaps the most popular subset of machine learning. While commonly used in different industries, from marketing to healthcare, they have proven to be a formidable asset in battling cyberthreats as well. Deep learning algorithms can detect “smart” malware that disguises itself to bypass traditional cybersecurity systems. This is done by analyzing fragments of the code structure from thousands of different software apps and then pinpointing abnormal ones to inspect more thoroughly.
Each of the aforementioned machine learning algorithms helps digital security measures become more effective in their own way. In essence, we’re only now beginning to comprehend the full potential of deploying machine learning algorithms in the field of cybersecurity and the benefits that come with it.
The future of Machine Learning in Cybersecurity
It’s obvious now that the future of cybersecurity is inseparable from the advancements in machine learning. Sadly, it’s also true that cyber threats are evolving and becoming more complex by the day.
Current developments can allow us to believe AI can continue bolstering our cyber defenses indefinitely as long as it’s handled properly.
Some of the biggest challenges there represent avoiding algorithmic bias and making sure data privacy remains unscathed through the process. There is also the issue of computational resources, or the apparent lack of thereof when compared to some other growing technologies and digital industries.
Summary
The sheer potential of machine learning in cybersecurity cannot be overstated. Increased investments in the field are notable even now and once the industry is completely revolutionized by AI, hackers will have a much harder time endangering our data and privacy.
Until then – and even then – we should keep the basic cybersecurity principles in mind and do our best to stay safe online on our own.
