Applications of Machine Learning in Cybersecurity in 2023

New technologies like machine learning and artificial intelligence have invaded many fields. Cybersecurity is no exception to this. Applications of machine learning in cybersecurity have grown over the years due to increasing data threats. According to IBM, the average cost for the data breach was $4.35M compared to $3.86M in 2020.

Cyberattacks have the potential to be much more than a passing fright. They might alter the trajectory of your life. Sensitive data disclosure can seriously affect people, organizations, and authorities. Hackers can access you via the web, Bluetooth, text messages, or the internet platforms you use. They can easily invade systems where the network security key is weak. These attacks can also affect industrial systems where the ot security is weak. 

In cyber security, machine learning algorithms can detect malware, improve network security, and protect sensitive data. This blog will explore four applications of machine learning in cyber security. Let’s get started.

Machine Learning Applications in Cybersecurity

Anomaly Detection in Cyber Security

Finding abnormalities in data that differ from expected standards or trends is known as anomaly detection. Anomaly detection is a technique used in cyber security to spot possible cyber risks. These risks include malware, breaches, or insider threats that differ from typical user behavior or system activity.

ML algorithms can construct a baseline of typical behavior using past data. It can then be used to detect abnormal occurrences as they occur. Following the specific use case, the algorithms can be trained on various forms of data, including system logs or user activity data.

The clustering algorithm is a well-known algorithm for machine learning for anomaly identification. Clustering algorithms aggregate data points together. Finding data points that don’t fit into groups allows the detection of anomalies. Some frequently used machine learning algorithms for anomaly detection include decision trees, neural networks, and support vector machines.

Network Threat Detection

For any organization, network threat is of the highest significance. It can take time to comprehend the topologies of the network security architecture. That could be better, given the data entering and leaving the network. Hackers can easily access where the network security key is weak.

Machine learning is growing in demand in network threat detection since it effectively identifies risks. Real-time network traffic analysis is a one-way machine learning algorithm that detects network threats. These algorithms can immediately identify possible attacks and notify network security personnel to act immediately by tracking traffic flows and spotting trends and anomalies.

Algorithms for unsupervised learning can also be applied to identify network threats. These algorithms operate without labeled data by detecting abnormalities and patterns in traffic data from networks. Unsupervised learning algorithms can identify possible risks in network traffic data and flag them for additional examination by cybersecurity professionals. The focus on ot security can also help in protecting industrial devices and systems from network threats using ML models.

User and Entity Behavior Analytics (UEBA)

UEBA analyses massive volumes of data about user behavior using cutting-edge algorithms and statistical algorithms. Data are collected on login durations, shared data trends, and file transfers to identify unusual behavior that might indicate a cyber threat.

It enables businesses to spot suspected intrusion attempts or other harmful activities that conventional security procedures would have overlooked. UEBA can identify unusual activity and warn cyber security teams to analyze and take measures before a security flaw happens by continuously monitoring user and entity behavior.

It can also be used with other security tools like intrusion detection systems and firewalls to offer a complete network security solution. It can spot suspicious network activity that intrusion detection systems could overlook. At the same time, intrusion detection systems can spot possible network attacks that UEBA might miss.

The capability of UEBA to evolve and change over the period is one of its main advantages. UEBA can enhance its capacity to identify new threats. It can reduce false positives by evaluating past information and gaining insight from previous instances.

Email Filtering

Various organizations rely heavily on email as a means of communication, yet it is also a frequent target of cyberattacks. Phishing links, virus attachments, and other security risks can jeopardize a company’s networks or information when they are included in spam emails.

Machine learning techniques can examine and classify email messages to counteract these dangers. ML models can recognize new or previously unidentified risks, trends, and traits of imminent vulnerabilities.

One popular method of email screening is using a machine learning algorithm to examine the text and metadata of each email to assess its potential risk. For instance, the algorithm might examine the sender’s identity, message body, and body text to spot any possible virus attachments or phishing scams.

Another strategy is to monitor email traffic patterns using a machine learning algorithm to find potential dangers. For instance, an algorithm may examine the regularity of emails from a specific sender or the number of emails with a particular subject line to spot possible spam or phishing. This will increase overall security in case of cyberattacks.

Conclusion

In closing,  applications of machine learning applications cybersecurity will continue to improve and expand in 2023. Machine learning is evolving into a crucial tool for enterprises to recognize and address possible security vulnerabilities due to the increasing quantity and complexity of cyber threats.

Machine learning algorithms are assisting enterprises in keeping ahead of the changing threat landscape and defending against various cyber-attacks. These algorithms can be used for threat, anomaly, and malware detection. They can also improve the ot security of the industrial systems.

ML systems can analyze network traffic where the network security key is not vital. Moreover, user and entity behavior analytics are becoming increasingly crucial for spotting potential threats or other harmful behavior that conventional security procedures could miss.

Machine learning algorithms will become more crucial as organizations gather massive amounts of data about security-related occurrences and events. These algorithms will help analyze and detect possible hazards.

Image Source: Kevin Paster, Burst