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An overview of Data Science for Cybersecurity from ML Perspective

rajeshwari

@rajeshwari1

October 18, 2022

Data Science & AI Community

229

Numerous security problems, such as unauthorized access, malware attack, zero-day assault, data breach, denial of service, social engineering or phishing, etc., have increased exponentially in recent years due to our increasing reliance on digitalization and the Internet of Things (IoT). For instance, less than 50 million distinct malware executables were known to the security industry in 2010. According to data from the German AV-TEST institute, by 2012, they had doubled to over 100 million. By 2019, more than 900 million malicious executables were known to the security community. Organizations, as well as individuals, may suffer significant financial losses as a result of cybercrime and attacks.

Data-driven intelligent decision-making from security data for smart cybersecurity solutions is the ultimate goal of cybersecurity data science. CDS represents a partial paradigm change from old, well-known security solutions like firewalls, user authentication, access control, cryptography systems, etc., that might not be successful in light of current needs in the cyber business.

As a result, many sophisticated assaults are developed and propagated across the Internet. While many researchers build cybersecurity models using various data analysis and learning techniques, as is outlined in the section titled "Machine learning tasks in cybersecurity," a thorough security model based on the successful discovery of security insights and the most recent security patterns may be more beneficial. We need to create more adaptable and effective security mechanisms to respond to attacks to handle this problem. We also need to update security policies to mitigate risks wisely and promptly. In order to do this, it is necessary to examine a vast amount of pertinent cybersecurity data produced from multiple sources, including network and system sources, and to find insights or appropriate security measures.

We first have a quick review of the concept and pertinent techniques to understand how cybersecurity data science might be applied to data-driven intelligent decision-making. In order to achieve this, we also examine and briefly explain various machine learning tasks in cybersecurity, outline distinct cybersecurity datasets, and highlight their application in various data-driven cybersecurity applications.

We next go over and highlight many related study topics and future directions in the field of cybersecurity data science, which could aid both academics and businesspeople in advancing research and development in pertinent application areas.

Finally, we present a general multi-layered architecture for the machine learning-based cybersecurity data science model. This framework briefly discusses how the cybersecurity data science model may be utilized to draw insights from security data and make data-driven intelligent decisions to create smart cybersecurity systems.

Cybersecurity data science

The availability of data is a key factor in data science. The foundation of cybersecurity data science is the dataset, which is often a collection of information records made up of various traits or features and related facts. As a result, it's critical to comprehend the nature of cybersecurity data which includes a variety of cyberattack types and pertinent elements. The rationale is that by analyzing the numerous patterns of security incidents or malicious conduct, raw security data gathered from pertinent cyber sources may be used to create a data-driven security model to help us reach our objective. In the field of cybersecurity, numerous datasets are utilized for various tasks, such as intrusion analysis, virus analysis, anomaly analysis, fraud analysis, or spam analysis.

We emphasize their use based on machine learning techniques in diverse cyber applications and review several such datasets, including their various properties and attacks, that are available online. A multi-layered architecture for smart cybersecurity services, which is briefly presented, discusses how to analyze effectively and process these security features, develop a target machine learning-based security model following the criteria, and eventually use data-driven decision-making.

Research Issues And Future Directions

Cybersecurity datasets: The source dataset is the main tool for data scientists working in this field. The vast majority of the datasets now in use are largely outdated and may not be sufficient to comprehend the most recent behavioral patterns of different cyberattacks.

Handling cybersecurity dataset quality issues: Cybersecurity datasets may be cluttered, lacking, inconsequential, unbalanced, or contain inconsistent data points relating to a specific security incident.

Security policy rule generation: Based on the relevant user or user group, service, or application, a user can authorize, limit, and track network traffic using security policy rules that refer to security zones. During the execution, the policy rules, which include both broad and more specialized rules, are compared sequentially to the incoming traffic, and the rule that matches the traffic is applied.

Conclusion

We have examined how cybersecurity data science applies to data-driven intelligent decision-making in smart cybersecurity systems and services in this study, which was motivated by the growing significance of cybersecurity, data science, and machine learning technologies. In terms of deriving knowledge from security incidents and the dataset itself, we have also talked about how it may affect security data.

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