Data Security

How Machine Learning Is Transforming Cybersecurity

February 15, 2024

How Machine Learning Is Transforming Cybersecurity

Introduction

Machine learning is revolutionizing cybersecurity. As a cybersecurity professional, I have seen firsthand how machine learning algorithms are enabling breakthroughs in defending against increasingly sophisticated cyber attacks. In this article, I will provide an in-depth look at how machine learning is transforming the field of cybersecurity.

How Machine Learning Works

Before diving into the cybersecurity applications, it’s important to understand what machine learning is at a high level. Machine learning is a subset of artificial intelligence that enables computers to learn and improve from experience without being explicitly programmed. There are three main types of machine learning:

Supervised learning – Algorithms are trained using labeled example data, enabling the algorithm to learn the patterns that connect the input data to the output. Common supervised learning algorithms include logistic regression, support vector machines, random forests, and neural networks.
Unsupervised learning – Algorithms are provided with unlabeled data and tasked with finding patterns and structure within the data. Common unsupervised learning techniques include clustering algorithms like k-means and principal component analysis.
Reinforcement learning – Algorithms interact with a dynamic environment, receiving feedback on actions in the form of rewards or penalties. The goal is to maximize rewards over time through trial and error.

The power of machine learning stems from its ability to detect patterns and features in vast amounts of data that would be impossible for humans to uncover. As more data is fed into a machine learning algorithm, its models continue to improve, enabling it to deliver increasingly accurate predictions and decisions.

Cyber Threat Detection

One of the most important uses of machine learning in cybersecurity is in threat detection. Traditional rule-based detection systems are ineffective at identifying new types of attacks or threats disguised to look like normal traffic. Machine learning algorithms can analyze large volumes of data from the network and endpoints to detect anomalies and recognize emerging threats.

Specific ways machine learning is improving threat detection include:

Network traffic analysis – Algorithms are trained on patterns of normal network activity compared to malicious activity. New connections and traffic can then be monitored in real-time and analyzed for signs of attacks.
Malware detection – Machine learning algorithms study malware samples to extract key characteristics. These models can then be used to analyze new files and detect even novel malware variants.
User behavior analytics – By learning the typical behavior patterns of users, unexpected activity and potential insider threats can be rapidly identified.

With machine learning, false positives can also be reduced compared to traditional rules-based systems. This allows security teams to focus on the most critical threats. According to a recent ESG report, 63% of cybersecurity professionals say machine learning has improved monitoring and detection within their organization.

Automating Cybersecurity Processes

Another benefit of machine learning is enabling greater automation of tedious, repetitive tasks that used to require extensive human effort. Analysts’ time is freed up to work on higher value activities and quickly respond to critical threats. Cybersecurity processes being automated with machine learning include:

Identity and access management – Machine learning algorithms can analyze access patterns to enhance identity and access management. Multi-factor authentication requests and access privilege escalations can be selectively auto-approved or flagged for review based on user behavior profiles.
Malware triage – The high volume of malware samples can overwhelm analysts. By automatically classifying samples by family and severity, machine learning models allow analysts to focus their reverse engineering efforts on only the most malicious and unique variants.
Vulnerability management – Machine learning algorithms help prioritize vulnerabilities by analyzing attributes like exploitability, malware connections and real-world attack observations. Patching efforts can then focus on fixing the vulnerabilities posing the greatest risk first.

According to a recent Socionext survey, 58% of organizations are using machine learning to automate cybersecurity processes, enabling them to operate more efficiently and consistently.

Extracting Insights from Security Data

The massive amount of data generated from security tools, systems and sensors can easily overwhelm security teams. Machine learning enables extracting valuable insights from this sea of data to improve cyber defense and reporting. Specifically, machine learning can help uncover:

Attack attribution – By reviewing inbound network traffic and hacking tools and techniques, algorithms can help classify the likely source of attacks and establish threat actor attribution.
Trends and anomalies – Machine learning statistical techniques detect changes in attack trends across time, uncovering periods of increased activity or seasonality.
Similar entities – Connections between related entities like malware samples, suspicious IPs and user accounts can be automatically surfaced to identify broader attack campaigns.

With these machine learning-powered capabilities, I can gain critical insights into the threats targeting my organization vs chasing thousands of data points manually. 62% of IT security professionals in an IdRaaS survey identified using machine learning for analysis and reporting as a top 3 priority for improving their cyber programs.

Looking Ahead

The impact of machine learning on elevating the efficiency and effectiveness of cybersecurity defenses is already clear. But looking at the future, I expect machine learning to become even more integral in this field by combining with related technologies:

Automation – Machine learning will enable more automated response and remediation actions, instead of just alerts.
Explainable AI – Algorithms that can explain their output will improve ML acceptance and identify potential biases.
Multi-layered learning – Cybersecurity will benefit from multi-layered neural networks capable of learning complex features.
Reinforcement learning – Dynamic environments like hacker behaviour can be modelled to maximize defence strategy rewards.

Though attackers will continue evolving, machine learning algorithms have demonstrated the ability to detect novel threats missed by other security methods. By augmenting the capabilities of cybersecurity teams, machine learning is proving to be a critically enabling technology against increasingly sophisticated cyber attacks.