Machine Learning Applications in the Cybersecurity Space

Machine learning is one of the hottest areas in data science. This subset of artificial intelligence allows a system to learn from data and make accurate predictions, identify anomalies or make recommendations using different techniques.

Machine learning techniques extract information from vast amounts of data and transform it into valuable business knowledge. While most industries use these techniques, they are especially prominent in the finance, marketing, healthcare, retail and cybersecurity sectors.

Machine learning can also address new cyber threats. There are many types of cyberattacks, such as structured query language (SQL) injection, phishing, cross-site scripting attacks, malware, social engineering, man-in-the-middle attacks, distributed denial of service attacks and ransomware. Organizations employ machine learning to constantly evaluate data, find patterns that could result in potential attacks and mitigate them.

Typical Uses for Machine Learning Techniques

Among other things, machine learning is often used to identify anomalies by monitoring network behavior, avoiding accessing harmful websites and detecting previously unknown malware. These methods can also protect data in cloud environments. Intrusion detection, malware classification and network analysis are the main security uses of machine learning.

For those in security spaces, the poor quality of the data used to train the methods — or the lack of data entirely — presents serious challenges. The potentially severe consequences of an error make the accuracy requirements in this industry high.

Below are detailed some of the most relevant machine learning uses for security:

Real-time email monitoring. Machine learning uses natural language processing and anomaly detection techniques to analyze email content and identify phishing attempts.
Fight against bots. Bots produce one-quarter of web traffic, and some can even take control of an application and execute specific malicious activities. The machine learning techniques applied to identify bots can vary, but the most relevant are response rate, message variability and identification of temporal patterns.
Malware detection. Polymorphic malware can adapt to security countermeasures. Different machine-learning techniques are helpful in combating this type of malware, including support vector machines, convolutional neural networks and decision trees.

How Machine Learning Can Improve Security

Here are four ways that machine learning can be used to boost security:

1) Using predictive analytics at the source of potential security threats.

Organizations need overall visibility of possible threats, prioritized by possible impact and potential occurrence. Machine learning algorithms can assist with threat identification and priority threat evaluation at a high level of sophistication.

2) Prevent SQL injection-based breaches by learning how attackers modify code to bypass authentication.

SQL injection is one of the most popular techniques attackers use to enter organizations’ networks. This type of cyberattack inserts malicious code into a SQL server to release information.

SQL injection breaches are challenging to track and remediate. One of the most effective approaches to tackle this type of cybersecurity threat involves implementing an enterprise security framework with specific scenarios that trap, stop and eliminate infected code.

3) Eliminate compromised password attacks with risk-based models that validate user identity based on behavioral pattern analysis.

Machine learning utilizes constraint-based and pattern-matching algorithms, which help detect patterns in how people log in to systems and machines containing sensitive information.

Password attacks are a form of attack where a threat actor steals passwords with various programs and password-cracking tools. There are different types of password attacks, like brute force attacks, dictionary attacks and keylogger attacks. Machine learning makes it possible for technology to learn user behavior through a risk-based model. Preventing other users from impersonating actual users using stolen passwords can sharply cut down on compromised password attacks.

4) Using risk scoring models to maintain enterprise security settings.

Risk scoring models are beneficial in planning and accomplishing growth strategies rapidly in an organization spanning multiple geographic regions. These models can also scale security frameworks quickly, thus assisting with threat assessments across numerous locations.

Machine learning enables IT resources to expedite the development of security tools, such as multi-factor authentication, and then scale them globally.

In conclusion, machine learning is a transformative technology that has revolutionized cybersecurity and holds immense potential for the future. Its ability to analyze vast amounts of data, identify patterns and make accurate predictions has unlocked new possibilities in various fields, such as healthcare, finance, transportation and many more.

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