How Machine Learning Is Revolutionizing Cyber Security

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The role of AI and machine learning (ML) in modern business needs little introduction. A 2021 report by NewVantage Partners found that 99% of Fortune 1000 companies are actively investing in both AI and Big Data.

 

AI and ML enable businesses to scale processes to new heights, unlocking optimization and driving ROI. Among the many applications of AI and ML is cybersecurity, a business-critical industry engaged in a constant arms race with increasingly sophisticated hackers and cybercriminals.

 

Cybercrime is forecast to rise by 15% by 2025, costing businesses a combined $10.5 trillion. Both large enterprise-level corporations and smaller businesses face risks, too, with smaller businesses suffering a combined 10,000 attacks per day in the UK alone.

 

Machine learning is helping fight modern cybercrime – here’s how.

ML in Cybersecurity: How Does it Work?

Machine learning applies machine-based decision-making to complex real-world scenarios. ML models convert data into decisions.

 

There are three main ways in which ML assists cybersecurity:

Learning

Firstly, it’s crucial to learn what fraudulent payments look like and their shared characteristics, if any. Learning about cybercrime better-positions businesses to deal with it.

 

Once fraudulent payments can be accurately identified, preventing them becomes much simpler. The problem is, there are some 108 million daily credit card transactions processed in the US alone, which makes identifying fraudulent payments more challenging than finding the proverbial needle in a haystack.

 

Machine learning algorithms are adept at classifying fraudulent payments by sifting through enormously large datasets. In addition, unsupervised algorithms can cluster payments based on shared characteristics, potentially identifying shared features between payments later labeled as fraudulent.

 

ML can analyze patterns in fraudulent payments, e.g., what device the fraudster is using, their browser, location, payment type, etc. This equips organizations with the intelligence they need to recognize cybercrime.

Reasoning

Cybersecurity teams are currently inundated by fraud alerts, thus causing “alert fatigue,” which lessens a human team’s capabilities. In other words, there are often too many alerts to deal with manually.

 

By accurately triaging alerts, ML algorithms can grade the risk associated with a payment. For example, an ML model can look at the payment amount, DNS, session IDs, digital fingerprints, user behavior, IP address, location data, and other factors to gauge risk. Human teams can then prioritize the highest-risk cases.

Augmenting human teams

ML-enabled cybersecurity tools augment human teams rather than replacing them entirely. In essence, machine learning scales up human teams, enabling them to take control of the ever-increasing volume of digital payments.

 

“Security tools must support security teams in doing their jobs better, from the people side, the process side, and the technology side” – Allie Mellen, 2021; Forrester.

Use Cases of ML in Cybersecurity

ML can assist cybersecurity in many ways that vary from industry to industry and department to department. Here are three key use case examples of ML in cybersecurity:

1: Zero-Day Exploits

Zero-day exploits are software weaknesses or vulnerabilities that enable hackers to penetrate and attack internal systems. These weaknesses are either unknown or yet to be patched by the developers. ML software testing helps developers discover such exploits.

 

For example, an ML model can analyze code to discover exploits, testing various vulnerabilities to discover whether an attack is possible. This is the same technique used by hackers to find exploits – ML enables developers to locate exploits before hackers do.

2: Network Protection

By measuring traffic across a network, ML models can discover the early makings of brute force attacks, DDoS attacks, and other attempted hacking attempts.

 

In addition, ML is quick and works with vast volumes of data, enabling cybersecurity teams to quickly home in on attacks and stop them in their tracks.

3: Anomaly Detection

Hackers frequently spoof IP addresses, session IDs, location data, device types, and other information, which allows them to penetrate systems. ML models can find data leaks and anomalies that indicate illicit behavior or hacking attempts, freezing them out of the system until human teams can take a look.

The Benefits of ML in Cybersecurity

ML’s benefits mostly pertain to its speed and scalability. For example, TowardsDataScience estimates that some 83% of fraud investigation cases are still conducted manually.

 

ML transforms manual investigations, enhancing the capabilities of human teams.

 

The main benefits of ML in cybersecurity are:

 

  • Enhanced fraud definitions.
  • Quick, accurate classification of fraudulent activity.
  • Root cause analysis to discover where an attack originated.
  • Predictive analysis to predict when an attack is most likely.
  • Anomaly detection to respond to attacks already in progress.
  • Learning from past attacks to implement new strategies.
  • Network-wide coverage.
  • Augmentation of human teams.
  • Hackers are already using AI and ML – organizations need to match their capabilities.

Learning From the Past and Present

Fraud techniques have advanced over the years, and hackers are now deploying their own bots and AIs. ML models can help charter the evolution of malware and hacking tools, providing businesses and organizations with a rich account of what hackers are doing.

 

Cybersecurity companies are even scraping data from the darknet to learn about what hackers are planning, their tools, techniques, etc. One of the most prolific examples of this is the international takedown of EncroChat, led by a collaboration between over 16 countries.

 

Additionally, cybersecurity firms use ML to analyze botnets, malware, and other hacking software to bring cybercriminal networks down from the inside.

 

Understanding data from the past and present is critical to developing robust cybersecurity software – machine learning equips cybersecurity developers with the means to analyze networks, attacks, and malware.

How to Adopt ML in Cybersecurity

Many cybersecurity vendors offer ML capabilities, both at the consumer and corporate levels. Some of these vendors offer managed services, allowing businesses to equip themselves with cutting-edge cybersecurity without costly on-premises installations.

 

The road to adopting partially automated cybersecurity is still a long one. As noted, the vast majority of organizations still rely on manual techniques. Businesses that face constant cybersecurity risks should seriously consider the benefits of investing in modern cybersecurity platforms that offer AI and ML as part of their value proposition.

 

 

 

 

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