With increasing reliance on Internet networks, cloud computing, and online storage, companies must strengthen their cybersecurity practices. As the cyber terrain grows, so does the onslaught of cyber threats that expose businesses to data breaches, loss of sensitive data, and other ever-evolving cyber threats. Organizations must change their security posture, expand beyond perimeter-based security techniques, and adopt new machine learning cybersecurity techniques that improve network security.
A subset of artificial intelligence, machine learning uses algorithms from previous data sets and statistical analysis to make assumptions about computer behavior. The computer can then adapt its actions, even performing functions for which it was not programmed. These capabilities have made machine learning a key cybersecurity tool.
According to a 2023 survey of cybersecurity professionals, approximately half of respondents worldwide see deep learning as the most promising artificial intelligence (AI) or machine learning technique to improve cybersecurity defenses, especially for identifying malware in encrypted traffic. Natural language processing for advanced identity theft detection ranked second according to 30 percent of respondents. Nearly 20 percent of participants saw quantum computing-enhanced AI algorithms as the most promising AI method for improving cybersecurity through sophisticated cryptography.
Understanding machine learning
Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on creating systems that learn — or improve performance — based on the data they use. The phrase artificial intelligence refers to systems or machines that mimic human intelligence. The terms are sometimes used synonymously. AI and machine learning are often discussed together, but they do not mean the same thing. It is important to remember that while all machine learning is artificial intelligence, not all artificial intelligence is machine learning.
There are three standard methods of machine learning:
- Supervised learning: A technique for training models with data that has already been labeled, meaning that data scientists know the desired results and can teach machines to reach the same conclusions. The machine can automatically make predictions when presented with new, unlabeled, or unknown data after the algorithm learns by studying these examples.
- Unsupervised learning: The opposite of supervised learning in that the data is unlabeled and the outcomes unknown. The task of the machine is to discover patterns, trends and similarities in the data and group them without knowing the desired outcome.
- Reinforcement learning: It trains the algorithm by giving the machine regular feedback. Unwanted outcomes are penalized while positive outcomes are reinforced, teaching the algorithm to identify certain trends in the data and adjust its decision-making strategy to improve its performance over time.
The roles of machine learning in predicting and preventing cyber threats
Machine learning is useful in data analysis in a variety of industries, from finance to healthcare to robotics and quality control. It is a rapidly improving field due to its wide range of applications. Additionally, if properly understood and used, machine learning can help cybersecurity professionals build strong defenses against emerging security risks and vulnerabilities.
Machine learning moves cybersecurity practices beyond traditional rules-based cybersecurity techniques and signature-based detection systems, leading to a stronger organization’s security posture. These are the ways machine learning and artificial intelligence put into cybersecurity can improve an organization’s network security.
- Anomaly detection: Machine learning models can analyze vast amounts of data, including network traffic, system logs, and user behavior patterns, from which anomalies can be identified and potential threats detected. Machine learning models build an understanding of what is “normal” behavior in a particular network or system. Through this knowledge, they can flag abnormal behavior or potential security threats.
- Malware detection: Machine learning can help identify and transform new types of malware. Machine learning analyzes file characteristics and code behavior to detect malware that other antivirus tools often miss.
- Identity theft detection: Email content, URLs, user behavior and learning patterns, with which ML can recognize and resolve phishing attempts, can be analyzed with machine learning models.
- Threat hunting: Cybersecurity professionals must fully utilize machine learning models for advanced threat hunting. Through proper education and implementation of machine learning threat detection, organizations can detect emerging threats early, identify zero-day attacks, and address advanced persistent threats (APTs).
- Reduce false positives: ML is more efficient than standard rule-based systems, guarantees a reduction in false positives and thus frees up time for security teams to investigate and address real threats.
- Scalability: Machine learning is built to adapt to changing network landscapes and larger volumes of data. This is significant in modern network systems and in dynamic environments where traditional perimeter defense may not be sufficient.
- Security in the cloud: As organizations increasingly rely on cloud computing, implementing machine learning securitization techniques into cloud environments will help identify and address digital threats that standard perimeter-based practices fail to fully address.
- Behavior analysis: Machine language continuously analyzes and monitors user behavior to identify suspicious or unauthorized activity. This is called user and entity behavior analytics, or “UEBA,” which is a machine learning technique that helps organizations detect insider threats and compromised accounts.
The benefits of using machine learning in cyber security
- Quickly synthesize massive amounts of data: One of the most difficult missions facing analysts is the need to quickly synthesize intelligence collected across their attack surface, which is typically generated faster than their teams can manually process. Machine learning can rapidly analyze vast amounts of historical and dynamic intelligence, enabling teams to operationalize data from disparate sources in near real-time.
- Activate professional intelligence in large numbers: Frequent training cycles allow models to continuously learn from their evolving sample population, which includes detections flagged by analysts or alerts reviewed by the analyst. This stops repetitive false positives and allows models to learn and implement the ground truth generated by experts.
- Automate repetitive manual tasks: Applying machine learning to specific targets can prevent security teams from mundane, repetitive tasks, acting as a force multiplier that allows them to scale responses to incoming alerts and redirect time and resources toward complex strategic projects.
- Increase analyst efficiency: Machine learning can augment analyst insight with the latest real-time intelligence, enabling threat intelligence and security operations analysts to efficiently allocate resources to address their organization’s most critical vulnerabilities and investigate time-sensitive detections alerted by ML.
Limitations of machine learning in cyber security
- Machine learning requires a large amount of training data, data that is inclusive and unbiased and of high quality.
- Machine learning requires more computing power, as well as enough time to learn and develop algorithms.
- Interpreting data can sometimes be difficult. Appropriate algorithms must be selected.
- Imagine training an algorithm with data sets that are not large enough to be inclusive. As a result of a biased training set, you will end up with biased predictions. Machine learning is prone to making mistakes.
Conclusion
Organizations and people alike face a constant battle with the ever-changing cyber threat landscape. While traditional cybersecurity strategies are important, they are becoming increasingly inadequate in the face of rapidly evolving threats. This study aimed to examine the valuable contribution that machine learning can make to improving cybersecurity efforts, with a particular focus on threat detection, prevention, and response.
In this article, we explored various applications of machine learning. These applications include anomaly detection and signature-based detection, as well as behavioral analysis, predictive analytics, and natural language processing. An extraordinary level of precision, speed and adaptability has been demonstrated by these applications in their ability to identify and combat threats.