Understanding cybersecurity from machine learning POV
Cybersecurity has undergone massive changes technologically, driven by data science. Extracting patterns of security incidents or insights from cybersecurity data and creating models based on the data is the key to making a security system automated and intelligent.
Cybersecurity data science is a phenomenon where data and analytics acquired from relevant cybersecurity sources match data-driven models that offer more effective security solutions. The concept of cyber security data science makes the IT process more actionable and intelligent compared to traditional cyber security processes. Therefore, a multi-layered ML-based framework for cybersecurity modeling is sought today.
Today, businesses depend more on digitalization and Internet of Things (IoT) after various security issues such as unauthorized access, malware attack, zero-day attack, data breach, denial of service (DoS), social engineering or phishing have surfaced at a significant level. rate. Cybercrime causes disastrous and sometimes irreversible financial losses that affect both organizations and individuals. A data breach costs $8.19 million in the United States and $3.9 million on average, according to an IBM report. Meanwhile, the annual cost of cybercrime to the global economy is $400 billion.
What is data science in cybersecurity?
Data science has 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 about data. For example, while detecting cyber threats, it analyzes security data in files, logs, network packets or other sources. Generally, security professionals did not use data science to detect cyber threats. Instead, they used file hashes, custom-written rules, and hand-defined heuristics.
While it has its own merits, it requires a lot of manual work 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 information about 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 transitioned to data science with its ability to turn raw data into decision making. To achieve this, several data-driven tasks such as data engineering on practical applications, data volume reduction, which deals with data filtering for further analysis, discovery and detection which focuses on extracting insights from data, automated models that focus on building data An intelligent security model, and targeted security alerts focused on security alerts are some of the resources available to get the system ideal security.
Therefore, cybersecurity data science absorbs methods and techniques from data science, machine learning, and behavioral analysis. It collects huge sets of data which is analyzed with machine learning technologies to detect security risks or attacks. We must keep in mind that cybersecurity data science is not just 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 complex set of rules, methods, or “transfer functions” that are applied to acquire data patterns and to identify or predict behavior. It plays an important role in adhering to a strict cybersecurity protocol.
Deep learning and neural networks
Deep learning is a subset of ML and uses a computational model inspired by the biological neural networks of the human brain. The artificial neural network (ANN) is often used in deep learning, and one of the most popular neural network algorithms is called backpropagation. It operates on a multi-layered neural network consisting of an input layer, one or more hidden layers, and an output layer. Unlike deep learning and traditional machine learning, its performance on the amount of security data increases. Ideally, deep learning performs well with large volumes of data, and machine learning algorithms perform comparatively better on small amounts of data.
Supervised learning is used when goals are set using input, a task-based approach. In ML, the best-known techniques are called classification and regression methods. It owes its popularity to its ability to classify or predict the future of a specific security problem, for example, to predict denial of service attacks or to identify different levels of network attacks such as analysis and identity theft. Meanwhile, to predict continuous or numerical values (total phishing attacks over a certain period or predict network packet parameters), regression techniques are essential. 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.
The main duty of unsupervised learning is to find patterns, structures or knowledge in unlabeled data. In most cases of cyberattacks, the malware remains hidden in several ways, such as by dynamically and autonomously changing its behavior to avoid detection. Clustering techniques fall under unsupervised learning and uncover hidden patterns and structures in datasets, which guides them to identify sophisticated attacks. Meanwhile, clustering techniques can be useful in identifying anomalies and policy violations, detecting and eliminating noisy instances in the data.
How can ML provide an effective security framework?
ML can assess cyber risk and promote inferential techniques to analyze behavior patterns to generate security response alerts and optimize cybersecurity operations. In the following way, we can understand how a multi-layered data processing framework can build a secure cybersecurity system using raw data.
Progressive learning and dynamism
It helps finalize the security model by adding additional information as needed and can be covered further in multiple modules. Attack classification and prediction models that use ML are highly dependent on training data. It is difficult to generalize to other datasets, which may be significant in some cases. To address these limitations, this is used to use domain knowledge in the form of taxonomy or ontology to refine attack correlation in cybersecurity applications. Another important aspect is to extract the latest data-driven security models.
Machine learning-based security
This is one of the most important steps where insights are extracted from data using cybersecurity data science. ML-based modeling can dramatically change the cybersecurity landscape with its security features. Better understanding of data and analytical models based on machine learning using large numbers of cybersecurity data can be effective. Therefore, various tasks can be used in this model to create layered solutions. It transforms raw security data into informative features that represent the underlying security problem in data-driven models.
Security data collection
In order to effectively use ML-based cybersecurity solutions, it is imperative to collect chunks of data, which then form connections between security issues in the cyberinfrastructure. Cyberdata serves as a source for establishing the “truth” of a security model, which affects the performance of the model. The quality and quantity of cyber data can make the solution more effective and efficient. The only concern is how to collect this valuable data to build these models. It can be easily collected and managed from a company’s specific security issues and projects. Further, these data sources are categorized into network, host, and hybrid.
Preparation of safety data
After accumulating the raw safety data, safety data preparation paves the way for building models based on that data. However, not all collected data is used to build the cybersecurity models, as unnecessary data is removed using network sniffers. Also, the collected data can sometimes be noisy, corrupted or have missing files. High quality data is essential to get an accurate data-driven model that maps from input to output. Therefore, it may undergo data cleaning to take care of corrupted data and missing files. Security data characteristics can be continuous, discrete or symbolic.