Machine Learning and Cybersecurity
This article will share reasons why ML has risen to such importance in cybersecurity, share some of the challenges of this particular application of the technology and describe the future that machine learning enables.
Machine learning (ML) has become a ubiquitous term across nearly every IT sector. Traditionally used to make sense of big data, enhance business performance, streamline processes, and assist in predictions, ML has also proven invaluable in cybersecurity. This article will explore why ML has become crucial in cybersecurity, outline the challenges unique to this application, and discuss the future that machine learning heralds.
The Growing Importance of Machine Learning in Cybersecurity
The necessity of machine learning in cybersecurity stems from increasing complexity. Many organizations today have numerous Internet of Things (IoT) devices, often unmanaged by IT. Additionally, with data and applications spread across hybrid and multicloud environments and a workforce increasingly working remotely, traditional security methods fall short.
In the past, enterprises relied on signature-based malware detection, static firewall rules, and access control lists (ACLs) to define security policies. However, with more devices in more locations, these methods cannot keep up with the scale and complexity of modern threats.
Machine learning trains models to learn automatically from vast amounts of data. These models can then identify trends, spot anomalies, make recommendations, and ultimately take actions. To address new security challenges, there is a clear need for machine learning. It can scale up security solutions, detect unknown attacks, and identify advanced threats such as polymorphic malware, which can change forms to evade traditional detection methods. ML is thus essential in combating these sophisticated attacks.
Unique Challenges of Applying Machine Learning to Cybersecurity
While machine learning is well-understood and widely used in areas like image processing and natural language processing (NLP), its application in cybersecurity presents unique challenges.
Unique Challenges for Applying ML to Cybersecurity
High Accuracy Requirements: In image processing, mistaking a dog for a cat may be annoying but not critical. However, in cybersecurity, misclassifying a fraudulent data packet as legitimate can lead to severe consequences, such as an attack on a hospital. Organizations process large volumes of data packets daily, and even a 0.1% error rate can block significant amounts of normal traffic, impacting business operations. Early concerns about ML accuracy compared to human researchers have been mitigated by the scalability of ML and its ability to detect unknown attacks by identifying abnormal behaviors.
Access to Large Amounts of Training Data: Machine learning models require extensive data for accurate predictions. Acquiring malware samples is more challenging than collecting data for image processing and NLP due to the scarcity of attack data and privacy concerns surrounding sensitive security information.
Ground Truth: Unlike images, the ground truth in cybersecurity is dynamic and constantly changing. No single malware database can cover all malware, and new malware is continuously generated. Determining the ground truth to measure accuracy is thus challenging.
Common ML Challenges Intensified in Cybersecurity
Explainability of Machine Learning Models: Understanding ML results is crucial for taking appropriate actions in cybersecurity.
Talent Scarcity: Effective ML in cybersecurity requires combining domain knowledge with ML expertise. Finding experts skilled in both areas is difficult. Collaboration between ML data scientists and security researchers is essential, despite their different methodologies and perspectives.
ML Security: Ensuring the security of ML models and data is critical due to the pivotal role cybersecurity plays in business operations. Research and industry efforts are ongoing to secure ML models, with organisations like Palo Alto Networks leading innovations.
The goal of machine learning in cybersecurity is to enhance efficiency and scalability, saving labor and preventing unknown attacks. Manual efforts cannot scale to billions of devices, but ML can. This scalability is essential for protecting organisations in an increasingly complex threat landscape. ML is also crucial for detecting unknown attacks in critical infrastructures, where even a single attack can have life-or-death consequences.
The Future of Cybersecurity Enabled by Machine Learning
Machine learning supports modern cybersecurity solutions in several ways. Each is valuable individually, and together they are transformative for maintaining a strong security posture in a dynamic threat environment.
Identification and Profiling: ML can identify and profile devices on a network, determining their features and behaviors as new devices connect to enterprise networks.
Automated Anomaly Detection: After profiling devices and understanding normal activities, ML can rapidly identify known bad behaviors, enhancing security.
Zero-day Detection: Traditional security requires seeing a bad action at least once for identification. ML can intelligently identify previously unknown forms of malware and attacks, protecting against potential zero-day attacks.
Insights at Scale: ML can analyze trends across large volumes of devices and locations, providing insights and enabling automation at a scale impossible for humans.
Policy Recommendations: ML can help create security policies by understanding device presence and normal behaviors, providing specific recommendations that automate and streamline the process.
As more devices and threats emerge daily and human security resources remain scarce, machine learning is essential for managing complex scenarios at scale, enabling organisations to meet current and future cybersecurity challenges.
