Machine Learning and Cybersecurity

While cyberattacks increase daily, using Machine Learning (ML) becomes a cornerstone of Cybersecurity to protect business integrity. Machine Learning's capabilities to analyze massive amounts of data make it ideal for detecting malicious activities and attack patterns in their premature phase, exhibiting vulnerabilities on the network, and anticipating when and how future cyber attacks will occur. 

Tracking threats is strenuous, but the most significant challenge is identifying the root of everything. Applying useful security methods is so defiant, considering that there are more devices than humans nowadays, and cyber criminals are starting to invent new things. Hence, it's time that you know all about this, including basic concepts, features, applications, uses in real life, etc. 

What is Machine Learning (ML) in Cybersecurity?

Machine Learning and Cybersecurity are the perfect combination, considering the huge amount of concurrent attacks on business security. Firstly, ML is a subfield of Artificial Intelligence that lets systems classify data, find data patterns, make predictions, and discover new knowledge. In other words, Machine Learning models are mathematical models that learn from data, either internal or external data sources, through a training process that continuously boosts accuracy.

ML is a term that was born in the United States created by Arthur Samuel in 1959. He defined it as "the field of study that allows computers to learn without being explicitly programmed." Plus, he successfully developed one of the first ML programs in the world, the Samuel Checkers Program, which used to play checkers better than the program's author. 

On the other hand, Cybersecurity is protecting Internet-connected systems, including hardware, software, and data, from cyberattacks. It's a practice that aims to protect networks, systems, and digital assets from attacks that seek to obtain access, modify, or destroy confidential data to disrupt normal business operations. For this reason, it's necessary to protect the systems so that they can overcome and manage those threats.

History of Machine Learning in Cybersecurity

Artificial Intelligence (AI) and Machine Learning use in Cybersecurity started decades ago. The first efforts focused on systems based on rules for detecting abnormal activities in the mid-to-late 1980s, specifically the boom of Big Data after the 2000s. As that technology became more sophisticated, ML algorithms were born as one powerful tool for threat detection.

In the late 2000s, Supervised Learning algorithms paved the path for detecting and preventing, more precisely, threats and abnormal activities, a way that Unsupervised Learning algorithms followed, allowing abnormal behavioral patterns and unknown threat recognition. Deep Learning's peaks in the 2010s revolutionized Cybersecurity and its capabilities to process vast amounts of data and discover complex patterns. Natural Language Processing (NLP) techniques also gained relevance, allowing a better analysis of textual data and detecting persistent threats. 

In this way, Artificial Intelligence and Machine Learning become increasingly popular within the field of Cybersecurity, continuously evolving to face threats and shape a more secure digital future. AI and ML techniques leverage the large amount of data generated by networks and digital systems to identify patterns, anomalies, and potential threats with greater accuracy and efficiency, allowing proactive prevention and real-time network traffic analysis. Big Data, AI, and ML is a perfect trilogy that has improved cybersecurity defenses by enabling organizations to analyze and respond to security incidents effectively, mitigating risks, and adapting to evolving cyber threats. 

Types of Machine Learning

Three types of Machine Learning exist: Supervised, Unsupervised, and Reinforcement Learning, which are highly useful in the context of Cybersecurity. 

1. Supervised Learning: It implies training an algorithm with tagged data so it learns to organize data based on the relationships between inputs and outputs. Generally, a human operator must manage algorithms during the training. ML algorithms use Supervised Learning to classify data as neutral or harmful, identifying threats such as denial of service attacks and predicting future cyberattacks. 

2. Unsupervised Learning: It refers to a trained algorithm with unlabeled or raw data, tagging and classifying malware samples without a human guide. Security teams depend on Unsupervised Learning to train more complex algorithms that detect new cyberattacks, especially as hackers develop different techniques to infiltrate business defenses. And there's another type, Semi-Supervised Learning, which combines Unsupervised and Supervised Learning.

3. Reinforcement Learning: It's a test approach that involves trial and error in which an algorithm learns new tasks by being punished for incorrect actions and rewarded for correct ones. In Cybersecurity, ML algorithms use this technique to enhance their capacity for detecting a wider range of cyberattacks. Team members can also use Reinforcement Learning to automate repetitive tasks, resulting in more effective and secure IT processes. 

Machine Learning Uses Cases in Cybersecurity

The common approach is the regression technique for predicting threats. Defenders can use existing data to detect fraud and malware with this approach, trained with previous data, and perform stable and accurate detection.

Another way to solve cyberattacks is using User and Entity Behavior Analytics (UEBA) systems, which use analytics techniques such as Adversarial Machine Learning, Statistics Analysis, Deep Learning algorithms, etc., to boost cyber defense and intrusion detection systems by identifying abnormal behaviors.

UEBA solutions create profiles that model the standard behavior of users and entities in an IT environment, such as users, data repositories, servers, etc. These solutions establish a baseline and use it to compare and identify anomalous behaviors.

It's solely a small preamble to the true use of ML in Cybersecurity. Machine Learning is critical in bolstering the context of Cybersecurity by improving the capabilities of detecting and responding to threats. Here are some of the most relevant use cases. 

User Behavior Modeling 

Some cybernetic threats can attack a particular organization, steal its login credentials from users, and then illegally log into the network. Identifying a normal antivirus is complex, as a user's credentials are authentic, and the cyberattack can occur without anyone noticing. In this case, Machine Learning algorithms can provide support using User Behavior Modeling, integrating your login and logout patterns into ML algorithms. Then, whenever a user behaves outside their normal behavior method, ML algorithms can identify and notify the cybersecurity team that something is out of the ordinary. 

Email Monitoring 

Phishing attacks occur when a cyber criminal sends fraudulent emails to employees and asks for private information, like confidential data related to your work, bank data, credit card, business password, etc. In this case, you can use Cybersecurity Software combined with ML to verify any lurking threats. NLP software can also scan emails and see if there is anything suspicious, like some patterns and phrases that may indicate that the email is a phishing attempt. 

Anomaly Detection 

ML algorithms can learn normal network traffic, User Behavior, or system activity and then mark anything that deviates from this baseline as a malicious sample. ML helps to detect various types of attacks, highlighting internal threats, detection via DNS analyses, detection of phishing websites, and zero-day vulnerabilities. 

Malware Detection and User and Entity Behavior Analytics (UEBA)

ML models can analyze files and executables to identify patterns associated with known malware and even detect previously seen behavioral feature threats. Machine Learning can also analyze User Behavior and identify deviations according to normal application user patterns, which could indicate an insider threat or compromised account. 

Credential Stuffing Detection

Machine Learning Algorithms can enable network intrusion detection systems to suggest there's a Credential Stuffing Attack. Also, ML can help you automate responses to certain types of threats, allowing for a quick and more effective mitigation process. 

Threat Intelligence Analysis

ML can process and analyze large amounts of cyber threat intelligence data to determine patterns and trends, helping companies avoid emerging threats. In addition, Machine Learning can help you to assess more accurate threat detection and fraudulent activities in financial fields, such as credit card fraud or money laundering. 

Between other use cases, you can leverage ML to prioritize vulnerabilities based on their potential impact and likelihood of exploitation, allowing security teams to focus their resources on the most critical issues. In addition, ML helps to improve Security Information and Event Management (SIEM) by enhancing the accuracy of threat detection and reducing false negatives. Finally, Machine Learning allows you to optimize repetitive security tasks and monitor the behavior of Internet of Things (IoT) devices to identify unusual or suspicious activities.

The use cases mentioned above are solely a few examples of Machine Learning solutions for Cybersecurity. This combination continues to evolve as new threats and technologies emerge. Plus, using ML in conjunction with other security measures and practices is the best combination you could implement. 

Machine Learning for Cybersecurity Pros and Cons

Machine Learning offers multiple benefits to IT and Cybersecurity professionals. The first advantage is that ML can learn new functions and improve the performance of existing systems, providing automated workflows without human intervention. IT and security analysts can leave their basic responsibilities to ML systems while focusing their time, resources, and energy on tackling new cyberattacks, fixing faults, and completing advanced tasks. 

It can process and analyze large sets of data quicker than traditional methods, and these algorithms can improve cyber threat detection rates of malicious patterns much faster or alert the development of new cyberattacks. In this same sequence, IT and security teams can immediately act, quelling cyber attacks in their early stages before they spread. By reviewing a company's security infrastructure, ML algorithms can expose weak points, advice recommendations, and help team members prepare for attacks. 

In this form, IT and security teams can tackle threats, even before they happen, by determining procedures and necessary systems to defend against more complex attacks. This technology can determine known and unknown attacks, making security teams more resilient to growing threats by strengthening their security technologies, like antivirus software.

It also provides comprehensive protection 24/7 without getting tired, which may be an impossible task for humans. Also, it can learn from its experiences to enhance the performance of the entire cybersecurity domain. Security teams can trust more operations to highly trained algorithms and reduce human errors. However, not everything is rosy when protecting internet-connected systems from true security incidents; using ML in Cybersecurity has some limitations. For instance, it is especially vulnerable to zero-day attacks and adversarial attacks. Due to this, you must handle ML security systems with precaution. 

Also, a very important point is that not all companies can afford the application of ML in their security processes. At the same time, cyber criminals can access ML and AI to create complex, advanced threats that can avoid detection parameters.  

Conclusion

Machine Learning's role in the Cybersecurity landscape offers solutions with a vital advantage to adapt and become more effective over time against advanced attacks. Threat intelligence by ML not only supports a proactive approach to malware analysis protection but also helps improve security personnel's solutions, response times, and operational decisions.