Machine Learning: Practical applications for Cybersecurity

cybersecurity

Artificial intelligence has become the buzzword in many industries. It is providing practical solutions for different scenarios.

Some of the beneficiaries are in healthcare, manufacturing, education, and even cybersecurity. One of the most critical components of artificial intelligence is machine learning (ML).

Machine learning is the ability of machines to ‘learn’ while running applications. The technologies observe data or scenarios. It can also draw insights from direct experience or instructions.

Please note, all these happen without any explicit programming. The machines then look for patterns in the data to draw conclusions.

Let’s try to provide some clarity with an example of automation testing. When you learn automation testing, you will notice some things.

Dynamic or developer-based changes can impact the stability testing of mobile and web apps. Any changes to the data upon which the testing depends will have an impact.

The use of non-ML test scripts restricts testers to static scripts. Such scripts cannot adapt.

Machine learning allows testers to understand trends and patterns. It helps with better decision-making and higher efficiency.

Further, ML contains self-healing abilities. What it means is it can detect any changes in the test cycle without human intervention. It will then fix any issues or alert the teams to take the necessary action.

QA teams can also increase the test automation coverage. Further, there are no human errors that could impact the final results.

So how can ML play a role in cybersecurity?

Machine Learning in Cybersecurity

Organizations have significant issues concerning cybersecurity. The sophistication levels of cyber-attacks are on the rise. The very same technologies that can help are also accessible to hackers.  Cybercriminals are taking advantage of emerging technologies like AI and ML.

Lack of preparedness is also a major challenge. Reports show that over 77% of organizations do not have a response plan.

As of 2020, the average data breach cost hit $3.86 million. Identifying and containing breaches on average took 280 days. Industry experts also predict that by 2025, the cost of cybercrime may be as high as $10.5 trillion every year.

There are many steps companies can take to improve cybersecurity. They must invest in training employees in proper online security. The right security measures include using antivirus, antimalware, and anti-ransomware.

Socks5 proxy servers are also excellent for extra security. The proxy features a TCP connection authentication. The Secure Shell Encryption (SSH) tunneling further helps keep your data safe.

And that’s not all; there are tons of other benefits. Such include online anonymity, the ability to bypass geographic restrictions, and web scraping capabilities.

Machine learning has a lot of potentials when looking at cybersecurity. Organizations can use the technology to analyze and respond to threats and attacks. It does it through pattern detection, classification, immigration, and prediction.

Security teams can concentrate on other key areas by automating the menial tasks. Google uses ML to identify and respond to threats on Android mobile endpoints. Microsoft Windows Defender uses many layers of ML to block threats.

Companies are also moving away from signature-based systems to ML applications. It allows for a better interpretation of events and actions over a wide variety of sources. It provides insights into how safe they are.

Let’s dive into some noteworthy applications for ML in cybersecurity.

1. Threat Detection and Prevention

Machine learning provides an effective way to detect and prevent threats. The algorithms can detect potential attacks within seconds. The security teams can then step in to mitigate against such.

Early identification is crucial. It prevents the malware from getting into the entire network or systems.

Advanced analysis using ML can help identify advanced assistant threats (APT). Catching the attack in the initial stages of the threat cycle can help avoid many problems.

Such action helps prevent internal threats and data leakages. The quick action ensures zero disruption to the running of the organization.

Prescriptive analytics comes into play in the unfortunate case of a cyberattack. Though responsive in nature, it is crucial. ML analysis of the attack provides a response on what to do to avoid further losses.

2. Departure from Signature-Based Threat Detection

Cyber and computer security have come a long way. In the past, detecting threats or anomalies were by using statics or heuristic methods. Let us take the example of antivirus software. It works by looking at the characteristics of a virus. It then generates or develops a virus signature database.

The database then becomes the comparison point for detecting and removing viruses. The major advantage of such applications is that they are easy to use and understand. The disadvantage is there is not much flexibility due to the lack of robust features.

Take a scenario where data size and flow rate undergo a significant increase. It would be hard to ensure signature comparison keeps up with the data inflow speed. You also don’t achieve too much efficiency.

Think of how much time it would take to compare each packet of data to the signature in the database. You must also ensure proper synchronization, which would be an uphill task. ML or intelligent applications are helping handle such processes.

3. Network Protection with Machine Learning

Many different elements contribute to the organization’s online network. These include wireless, Ethernet, and virtual networks. It is important to keep such networks safe from hackers.

That is why security-conscious organizations have intrusion detection systems (IDS) in place. But, many of the existing ones still use Signature-based approaches in their applications.

Machine learning is providing a new approach. It uses Network Traffic Analytics (NTA) for a deeper analysis of traffic on every network layer. ML helps with:-

  • Regression or predictions of packet parameters. It then compares such with the normal ones
  • Classification of attacks for easier identification
  • Clustering groups data depending on similarities. It is helpful where there are large volumes of unknown data coming into the network. The ML-based clustering algorithm separates data into clusters for easy handling. Clustering can provide excellent insights for forensic analysis

4. Endpoint Analysis and Protection with Machine Learning

Companies like Google, Apple, and Amazon use ML to improve voice-based experiences. As we have stated above, Google is using ML for threat analysis on mobile endpoints.

The bring-your-own-device (BYOD) culture is taking shape. Such threat analysis will be especially crucial for better mobile device security.

ML-based threat detection covers network, device, and application threats. It then takes automated steps or actions to keep the company data safe.

ML works by identifying the behavior patterns of users. It can then distinguish what is normal or abnormal. But understanding user behavior does have its challenges.

This is not like in the case of malware detection, where you can focus on common attacks. It then allows you to train classifiers to respond as they should.

With user behavior, it is not always very clear what to look for. Coming up with a universal algorithm to respond becomes very tricky. ML can reduce the complexity of the problem. It uses regression testing to detect anomalies depending on the user’s action.

It will, for example, note a change in log-in behavior or details. Peer group analysis is also possible with the classification of data. Clustering separates users into groups, allowing for quick detection of outliers.

Administrators can also analyze huge amounts of data. Such can give insights on new threats allowing for proactive or faster responses.

5. Improved Corporate Security

Companies can improve corporate security with insights from ML. Take the example of Insider threats that continue to be a major challenge. We reiterate, how staff members interact with the IT environment can expose the company to hackers.

Machine learning will generate insights by learning behavior. It then places the patterns within the context of an attack environment. The insights are critical in determining the best course of action around cybersecurity.

ML will also help with routine tasks. Such include monitoring, auditing, and updating security systems. Early detection provides an opportunity for quick action to keep the systems safe.

Final Thoughts

Machine learning has an important role to play concerning cybersecurity. We have highlighted how in our article above. The ability to learn or generate insights from data is crucial. It helps with identifying, preventing, and managing threats.

The security teams can become more proactive instead of reactive. The real-time alerts to cyber threats allow for quick action. You avoid the attack spreading into the whole network.

Automation of some of the menial tasks will free up the security teams to concentrate on other areas.

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