With an increasing reliance on online networks, cloud computing, and online data storage, companies must improve their cybersecurity practices. As the cyber terrain grows, so do the onslaught of cyber threats that put companies at risk of data breaches, loss of sensitive data, and other ever-evolving cyber threats. Organizations must transform their security posture, expand beyond perimeter-based security measures, and embrace new machine learning cybersecurity practices that enhance network security.
Machine learning enhances cybersecurity by predicting threats and automating responses. For example, ML algorithms can identify patterns in network traffic to detect and block malicious activities in real-time. Additionally, companies like IBM and Palo Alto Networks use ML for threat intelligence to preemptively defend against cyberattacks.
Integrating machine learning in cybersecurity has proved crucial in improving a company’s security posture and, specifically, protecting the outer boundaries of a network. It adds an additional layer to security practices which will effectively protect organizations against cyber threats. Understanding the role of machine learning in cybersecurity will support organizations in creating strong, multi-layered security defenses to reduce or eliminate complex digital threats.
According to a report by Cybersecurity Ventures, global spending on cybersecurity is predicted to exceed $1 trillion from 2021 to 2025. Additionally, Gartner predicts that by 2025, 60% of enterprises will use machine learning to enhance their security posture, up from less than 20% in 2021.Let’s break down the benefits of machine learning in cybersecurity!
What is Machine Learning?
Before we really get into machine learning for cybersecurity, let’s clear up what machine learning is. In simple terms, machine learning is a subset of artificial intelligence (AI) that uses datasets to teach machines how to identify patterns, make predictions, and pull valuable insights from data.
Essentially, machines develop algorithms and models without being explicitly programmed. Instead, they’re fed mass amounts of data and from which they identify trends, sort information, or take action based on data, rather than relying on specific programming instructions. Once the machine has learned from data sets, it’s able to make predictions regarding unseen or future data.
Photo by Christopher Burns on Unsplash
Types of machine learning
There are three standard machine learning methods:
Supervised learning: the process of training a model with data that is already labeled, meaning that data scientists know the desired outcomes and can train the machines to reach those same conclusions. After the algorithm learns by studying these examples, the machine can automatically make predictions when presented with new, unlabeled, or unknown data.
Unsupervised learning: the opposite to supervised learning in that the data is unlabeled and the outcomes are unknown. The machine’s task is to discover patterns, trends, and similarities in data and group them together without knowing the desired outcome.
Reinforcement learning: trains an algorithm through giving the machine regular feedback. Positive outcomes are reinforced while undesired outcomes are punished, teaching the algorithm to identify certain trends in data, and adjust its decision-making strategy to enhance its performance over time.
The chosen machine learning method is determined based on individual circumstances, the availability of data, and the desired outcomes.
Source: CyberDB
Applications of machine learning
Machine learning has proven useful in a number of fields. It’s rapidly advancing and has had a profound impact on a variety of industries, including:
Healthcare
Finance
Recommendation services
Computer vision
Robotics
Manufacturing and product fulfillment
Thanks to the availability of large datasets, machine learning has been greatly improved in recent years and continues to be an innovative aspect of artificial intelligence. Familiarizing yourself with machine learning’s applications will prove useful for all tech professionals.
What is Cybersecurity?
Cybersecurity, on the other hand, is a set of ongoing practices and processes that secure information technology systems from malicious actors and digital threats. It’s an all-encompassing term used to describe the variety of securitization practices and strategies utilized by cybersecurity professionals to create a strong network security and protect digital assets.
Key aspects of cybersecurity include:
Confidentiality: cybersecurity professionals must guarantee that sensitive and valuable information can only be accessed by authorized users or systems.
Integrity: this refers to the reliability and accuracy of data and systems. Cybersecurity professionals implement measures to prevent unauthorized tampering or use of data or software.
Availability of data: cybersecurity professionals must protect data against potential disruptions or attacks that may lead to limitations in authorized user access. Ensuring that data is secure and able to be accessed by the appropriate users is a key aspect of cybersecurity.
Authentication: this is the process of verifying the identity of users, devices, or systems who attempt to access an organization's digital assets. Authentication processes are established and maintained by cybersecurity professionals.
Data encryption: this refers to the process of encoding data such that it’s made inaccessible to unauthorized users. Data encryption is utilized both in the transformation of data, and in establishing safe data storage.
Risk management: cybersecurity professionals must be proactive in identifying potential vulnerabilities in software, hardware, and digital systems. Recognizing and reducing potential risks is a huge aspect of cybersecurity work.
Given the growing cyber landscape, the work of cybersecurity professionals is constantly transforming. That said, cybersecurity professionals must stay up-to-date on best practices, emerging threats, and tools that could enhance an organization’s security practices.
What Responsibilities do Cybersecurity Professionals Have?
We’ve touched on key aspects of cybersecurity, but what tools and techniques do cybersecurity professionals utilize in order to fully maintain the confidentiality, integrity, and availability of an organization’s digital assets?
Threat detection: identifying and assessing potential threats is crucial in maintaining a secure digital environment. Cybersecurity professionals must track network traffic, analyze system logs, and immediately address security alerts. They use a number of tools in order to detect suspicious activity.
Vulnerability assessment: involves the ongoing work of evaluating potential risks and determining where to direct organizational resources in reducing the organization’s potential attack surface.
Configuring security measures: cybersecurity professionals implement and maintain security measures such as firewalls, intrusion detection systems, and antivirus software.
Building organizational awareness: this is a crucial aspect of securing digital assets because your organization’s cybersecurity practices only work insofar as individual employees partake in securing network systems. Educating employees on their role in the organization’s cybersecurity reduces the potential for human error in securitization practices.
Incident response: in the case of a security breach, cybersecurity professionals must be prepared to respond in a timely fashion, investigating the incident, working to resolve it, and initiating next steps in order to prevent future incidents.
Compliance: as the digital realm evolves, so do the laws and standards that govern it. Cybersecurity professionals must develop, update, and enforce security protocols that align with industry standards, while also ensuring that securitization practices are in line with government regulations.
These are just a few of the many responsibilities of cybersecurity professionals. Their work continues to evolve as the industry changes and cyber threats take new forms. Cybersecurity professionals must have foresight, work well under pressure, and learn from incidents such that cybersecurity systems continue to improve.
Key Uses of Machine Learning in Cybersecurity
Machine learning has proven helpful in data analysis in a variety of industries, from finance to healthcare to robotics and quality control. It’s a field that’s rapidly advancing because of its wide range of applications. That said, if properly understood and utilized, machine learning can help cybersecurity professionals build a robust defense against evolving security risks and vulnerabilities.
Machine learning moves cybersecurity practices beyond traditional rule-based cybersecurity approaches and signature-based detection systems, leading to an organization’s stronger security posture. Here are a few ways that artificial intelligence and machine learning applied to cybersecurity can enhance an organization’s network security:
Anomaly detection: machine learning models are able to analyze massive amounts of data including network traffic, system logs, and user behavior patterns, from which anomalies can be identified and potential threats are detected. Machine learning models build an understanding of what “normal” behavior is in a given network or system. Through that knowledge, they are able to flag abnormal behavior or potential security threats.
Malware detection: machine learning can aid in identifying new and transforming malware strains. Machine learning analyzes file characteristics and code behavior in order to detect malicious software that is often overlooked by other antivirus tools.
Phishing detection: machine learning models analyze email content, URLs, and user behavior, and learn patterns, through which ML can recognize and address phishing attempts.
Behavioral analysis: ML continuously monitors and analyzes user behavior in order to identify suspicious or unauthorized activity. This is often referred to as user and entity behavior analytics, or “UEBA,” which is a machine learning technique that helps organizations detect insider threats and compromised accounts.
Threat hunting: cybersecurity professionals must learn to fully utilize machine learning models in order to take on advanced threat hunting. Through proper training and implementing machine learning threat detection, organizations can detect sophisticated threats early on, identify zero-day attacks, and address advanced persistent threats (APTs).
Reduce false positives: ML is more efficient than the standard rule-based systems, ensures a reduction in false positives, and thus, frees up time for security teams to investigate and address genuine threats.
Scalability: machine learning is designed to adapt to changing network landscapes and to scale in relation to greater quantities of data. This is crucial in modern network systems and in dynamic environments where traditional perimeter defenses may not be sufficient.
Cloud security: with companies becoming more and more reliant upon cloud computing, integrating machine learning securitization practices into cloud environments will help identify and address digital threats that standard perimeter based practices fail to fully address.
Photo by Possessed Photography on Unsplash
These are just a few of the benefits of integrating machine learning into an organization's cybersecurity efforts. Through fully engaging the power of machine learning, cybersecurity professionals can close security gaps, reduce cyber risks, and improve operational efficiency. Knowing this, machine learning should be embraced as one aspect of a holistic security strategy that includes perimeter defenses, access controls, data encryption, and cybersecurity training for all employees.
In all, if your business operates in the digital sphere, cybersecurity and machine learning must be understood as two crucial aspects of your work. Machine learning and cybersecurity are key in protecting your company’s digital assets, work that is crucial in maintaining a reputation as a reliable and trustworthy organization.
How to Implement Machine Learning in Your Cybersecurity Strategy
Identify Security Needs: Determine which areas require enhancement using ML.
Select Appropriate Tools: Choose tools like SIEM systems that integrate ML.
Collect and Prepare Data: Ensure quality data for training ML models.
Develop and Train Models: Build ML models tailored to specific security needs.
Continuously Monitor and Update: Regularly refine ML models based on new threat data.
Interested in a career in cybersecurity?
If you’re looking to jumpstart your career in cybersecurity, then look no further. Ironhack offers bootcamps in cybersecurity that will help you land a job in the growing industry. Check out our bootcamps, financing plans, and vast alumni network today!
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
