Cybersecurity Machine Learning Use Cases: Examples

Cybersecurity professionals are increasingly finding cybersecurity machine learning use cases in their work. The reason for this is that cybersecurity has become more complicated and the scale of cybersecurity threats is growing exponentially. Machine learning can help to combat these cybersecurity threats by providing security teams with real-time alerts, but there are many cybersecurity machine learning use cases beyond just cybersecurity. Artificial intelligence (AI) technologies, in particular, machine learning models such as logistic regression, SVM and random forest, etc., and deep neural networks models such as CNN, LSTM, etc., have been widely used to fight against cyberattacks. In this blog post, we will look into how machine learning is being used in cybersecurity and also explore some other cybersecurity machine learning use cases.

Most common machine learning use cases in cybersecurity

Here is the list of the most common machine learning use cases in cybersecurity:

• Malware detection: The malware detection can be cast into a binary classification problem, i.e., malware and non-malware (benign). One example of AI-based malware detection is classifying malwares with Android apps. AI-based malware detection for the Android operating system is one of the most popular problems dealt with the help of AI/machine learning models. Android’s popularity and open nature have attracted a large number of malware developers, which has resulted in Android users being more prone to attacks by thousands of fraudulent applications. Deep learning methods such as LSTM have been successfully applied to cyber security issues. For example, a deep learning method has been proposed to detect the Android malware through training two Long-term Short Memory (LSTM) networks for malware and non-malware apps features (i.e., system calls) respectively. Different LSTM network topologies have been tried in Android malware detection. DREBIN, an Android malware dataset can be used to train and validate the classifiers.
• False-positive reduction: Any cybersecurity system is likely to produce false positives, which are alerts about threats that turn out not to be actual threats. It can be difficult for humans to process all these alerts and determine which are actual threats and which are false positives. However, machine learning can be used to help reduce the number of false positives by training a model to identify certain patterns that indicate a false positive.
• Analyzing network logs: Anomaly detection is a cybersecurity machine learning use case that can also be applied to cybersecurity. By finding anomalies in network traffic, cybersecurity teams are able to identify potentially harmful activities and take action before it’s too late. Anomalies in network traffic can be identified through machine learning by looking for patterns that differ from the norm. By converting network assault data into an image format and applying machine learning, cyber security can be enhanced by applying supervised computer vision and other machine learning techniques to detect malicious specimens. Algorithms such as Light Gradient Boosting Machine, Random Forest Classifier, and Extra Trees Classifier have been found to be very effective in this technique.
• Software (source code) vulnerabilities: Software vulnerabilities found with source code are the main reasons for cyber security attacks on software systems. Vulnerability detection is an essential yet challenging step to identify vulnerabilities in the source codes. Deep learning models based on natural language processing (NLP) (such as LSTMs) and CNNs are being applied in vulnerability detection. Pre-trained language models such as BERT have recently emerged as a significantly trending learning paradigm, offering numerous successful applications in NLP. Pre-trained programming language (PL) models such as CodeBERT have made a significant improvement for tasks such as vulnerability detection.
• Phishing attacks: Phishing attacks are among emerging security issues that have recently drawn significant attention in the cyber security community. It is a type of attack leveraged by cybercriminals which impersonate some legitimate organizations’ websites or URLs to deceive the victims There are numerous existing approaches for phishing URL detection.
• Detecting intrusions: Intrusions are one of the most serious cybersecurity threats, and detecting them as early as possible is critical for preventing damage. Machine learning can be used to detect intrusions by analyzing data from security sensors, which can help security teams identify an intrusion much earlier.
• Analyzing historical security events and identifying trends: Another common cybersecurity machine learning use case involves the analysis of past security-related events in order to find trends and patterns. This information can then be used to improve cybersecurity defenses.
• Preventing spam: Email is a common vector for cybersecurity attacks, and one of the best ways to prevent email-based attacks is to use machine learning to filter out spam emails. Machine learning can be used to develop models that identify certain characteristics of spam emails, which can then be used to filter them out of email traffic. Classification algorithms such as Naïve Bayes and support vector machines (SVM) are commonly used for spam filtering.
• Detecting cybersecurity incidents: Another common cybersecurity machine learning use case involves the detection of cyber-attacks. Machine learning can be used to identify attacks that have already occurred or are in progress, which allows security teams to respond quickly. This is often referred to as “attack incident response” and it requires historical data about previous cybersecurity events so that machine learning can be applied.
• Identifying and containing botnets: Botnets represent a serious cybersecurity threat because they allow hackers to control a large number of devices without the device owner’s knowledge. Machine learning can be used to identify botnets by analyzing data from cybersecurity sensors, which helps security teams contain them faster.
• Preventing account takeover: Account hijacking is yet another cybersecurity threat that machine learning can help to prevent. By training a model with historical examples of accounts that have been hijacked, machine learning can flag any accounts that look similar so cybersecurity teams can take action. Classification algorithms can often be used for this task.
• Identifying cybersecurity threats: The ultimate goal of many cybersecurity efforts is to identify all potential cybersecurity threats so security resources can be allocated appropriately. Machine learning models are often used for this purpose because they’re capable of identifying new types of threats that haven’t been encountered before. Classification algorithms can be used to identify cybersecurity threats by analyzing data about past events and identifying patterns. Classification algorithms such as SVM, Random Forest, etc can be used for cybersecurity threat identification.
• Preventing DDoS attacks: One of the most common cybersecurity machine learning use cases involves preventing Distributed Denial of Service (DDoS) attacks. These types of cyber-attacks involve a hacker flooding a website with traffic in order to deny legitimate users access to the site. Machine learning can be used to identify DDoS attacks before they cause damage by analyzing data from cybersecurity sensors.

Machine learning is one of the most exciting cybersecurity technologies because it can help identify cybersecurity vulnerabilities, analyze data to detect cybersecurity threats, and provide protection against DDoS attacks. The use cases outlined above highlight some of the ways machine learning can be applied in your cybersecurity strategy. Let our team know if you’re interested in improving your cybersecurity practices with artificial intelligence or other cutting-edge technology that will keep you ahead of cybercriminals.