Cybersecurity has undergone massive shifts technology-wise, led by data science. The extraction of security incident patterns or insights from cybersecurity data and building data-driven models on it is the key to making a security system automated and intelligent.
Cybersecurity data science is a phenomenon where the data and analytics acquired from relevant cybersecurity sources suit the data-driven patterns that give more effective security solutions. The concept of cybersecurity data science makes the computing process more actionable and intelligent when compared to traditional ones in cybersecurity. Therefore, an ML-based multi-layered framework for cybersecurity modelling is sought after today.
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Today, companies depend more on digitalisation and Internet-of-Things (IoT) after various security issues like unauthorised access, malware attack, zero-day attack, data breach, denial of service (DoS), social engineering or phishing surfaced at a significant rate. Cybercrime causes disastrous and sometimes irreversible financial losses that affect both organisations and individuals. A data breach costs $8.19 million in the United States and $3.9 million on an average, according to an IBM report. Meanwhile, the annual cost for the global economy from cybercrime is $400 billion.
What is cybersecurity data science?
Data science brought about a global change in various industries. However, it has become an important segment for the future of robust cybersecurity systems and services. This comes after cybersecurity has become all about data. For example, while detecting cyber threats, it analyses security data in files, logs, network packets, or other sources. Commonly, security professionals did not use data science to detect cyber threats. Instead, they used file hashes, custom-written rules, and manually defined heuristics.
Although it has its own merits, it requires a lot of manual labour to keep up with the ever-changing threat landscape. On the other hand, data science can change the industry with machine learning algorithms that can be used to extract insights of security event patterns from training data for detection and prevention. It can be used to detect malware or suspicious trends and to extract policy rules.
The security industry has moved to data science led by its ability to transform raw data into decision making. In order to pull this off, several data-driven tasks like data engineering on practical applications, reducing data volume, which deals with filtering data for further analysis, discovery and detection that focuses on extracting insights from data, automated models that focus on building data-driven intelligent security model, and targeted security alerts focusing on security alerts are some of the resources available to achieve the ideal security system.
Hence, cybersecurity data science absorbs the methods and techniques of data science, machine learning, and behavioural analytics. It collects huge datasets that are analysed with machine learning technologies for detecting security risks or attacks. We have to keep in mind that cybersecurity data science is not only a collection of machine learning algorithms but a process that guides security professionals to scale and automate their security activities.
How is ML used in cybersecurity
Machine learning models contain a set of rules, methods, or complex “transfer functions” which are applied to acquire data patterns and to identify or predict behaviour. It plays an important role in following a strict cybersecurity protocol.
Deep learning and neural networks
Deep learning is a subset of ML and uses a computational model that is inspired by the biological neural networks in the human brain. Artificial Neural Network (ANN) is often used in deep learning, and one of the most popular neural network algorithms is called backpropagation. It works on a multi-layer neural network consisting of an input layer, one or more hidden layers, and an output layer. In contrast between deep learning and classical machine learning is its performance on the amount of security data increases. Ideally, deep learning works well with large volumes of data, and machine learning algorithms perform comparatively better on small amounts of data.
Supervised learning is used when targets are defined using inputs, a task-driven approach. In ML, the most famous techniques are called classification and regression methods. It owes its popularity due to its ability to classify or predict the future of a specific security problem—for example, to forecast denial-of-service attacks or to identify different grades of network attacks like scanning and spoofing. Meanwhile, to foretell continuous or numeric values (total phishing attacks in a certain period or predicting the network packet parameters), regression techniques are critical. Regression analysis is also used to identify the root causes of cybercrime and fraud. Classification and regression can be differentiated by its output variable, the output is continuous in regression, and the predicted output for classification is discrete.
Unsupervised learning’s main duty is to find patterns, structures, or knowledge in unlabelled data. In most cyberattack cases, the malware remains hidden in several ways, like changing its behaviour dynamically and autonomously to avoid detection. Clustering techniques come under unsupervised learning and uncover the hidden patterns and structures from the datasets, which guides it to identify sophisticated attacks. Meanwhile, clustering techniques can be helpful to identify anomalies and policy violations, detecting and eliminating noisy instances in data.
How can ML provide an effective security framework
ML can assess cyber risks and promotes inferential techniques to analyse behavioural patterns to generate security response alerts and optimises cybersecurity operations. In the following way, we can understand how a multi-layered data processing framework can build a secure cybersecurity system by using raw data.
Gradual learning and dynamism
It helps to finalise the security model by adding additional intelligence as per the needs and can be processed further in several modules. The attack classification and prediction models that use ML heavily depend on training data. It is difficult to generalise to other datasets, which can be significant in some cases. To address such limitations, this is used to utilise domain knowledge in the form of taxonomy or ontology to refine attack correlation in cybersecurity applications. Another significant aspect of this is to extract the latest data-driven security patterns.
Machine learning-based security
This is one of the most important steps where insights are extracted from data by using cybersecurity data science. ML-based modelling can dramatically change the cybersecurity landscape with its security features. A better understanding of data and machine learning-based analytical models utilising a large number of cybersecurity data can be effective. Therefore, various tasks can be used in this model for building layer solutions. It transforms raw security data into informative features that represent the underlying security problem into data-driven models.
Security data collection
In order to use ML-based cybersecurity solutions effectively, it is imperative to collect chunks of data, which later forms links between security problems in cyberinfrastructure. Cyber data serves as a source for setting up the “truth” of a security model, which affects the model performance. The quality and quantity of cyber data can make the solution more effective and efficient. The only concern is how to collect this precious data for building these models. It can be easily collected and managed from the specific security problems and projects of an enterprise. Furthermore, these data sources are classified into network, host, and hybrid.
Security data preparation
After racking up the raw security data, security data preparation paves the way for building models based on this data. However, not all of the collected data is used to build the cybersecurity models, as useless data is removed with the help of network sniffers. In addition, the collected data can sometimes be noisy, corrupted, or have missing files. High-quality data is a must to get an accurate data-driven model which maps from input to output. Therefore, it might undergo data cleaning to take care of the corrupted data and missing files. The security data’s characteristics can be continuous, discrete, or symbolic.