Essay Sample on The Concepts of Machine Learning and Data Analytics and Their Applications to Cybersecurity

Introduction

Machine learning refers to the application of artificial intelligence that enhances the ability of the systems to learn and improve from experience without any explicit programming. Machine learning makes use of data analysis to automate its learning process as well as in building its models. Machine learning technology focuses on the development of computer programs with abilities to access data, analyze the data, and make use of the findings to learn and make predictions for themselves. In machine learning, the learning process begins at the observation stage. At this stage, there is a direct experience where the programs look for patterns in the provided data, analyzes to make decisions on the future basing them on the data provided. The primary aim of machine learning and data analysis is to allow the computers to learn without the intervention of humans and adjust their knowledge base as well as the resulting actions accordingly.

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The past few years have witnessed an increase in the advancements in artificial intelligence and machine learning. Similarly, there has also been a rise in cyber-related threats such as the rise in the ransomware, botnets and other forms of malware attacks. In order to complement the ever-growing skills of the attackers as well as the human analysts for the cybercrimes, organizations across the globe are turning to machine learning to provide a forceful deterrent to the cybercriminals. Incorporation of machine learning and data analytics in cybersecurity to protect organizations from the Advanced Persistent Threats (ATP) is projected to boost other security-related fields such as big data, intelligence, as well as analytics which are key aspects of machine learning. The use of machine learning in cybersecurity has played a critical role in various technologies as well as practices that have been developed to reduce the cyber attack incidents as well as the opportunities that may lead to these incidents while limiting the resulting damage. Machine learning makes use of the two major classification techniques which are the supervised learning and the unsupervised learning to identify the potential threats and undertake the appropriate actions. The supervised learning makes use of the labeled training data sets to identify the differences between the provided data while unsupervised learning makes use of unlabeled data to make analysis and come up classifications of the data sets.

Companies Are Providing Innovative Defensive Cybersecurity Measures Based on Machine Learning and Data Analytics

The increased trends in the cyberspace have forced the existing security companies such as Cisco, Symantec, McAfee, Microsoft, Check Point, and many others to make the appropriate adjustments to their systems to incorporate machine learning and data analysis to provide satisfactory services to their customers. The companies understand the importance of security to the businesses as well as organizations. The incorporation of the machine learning and data analysis in cybersecurity is important during this period where a mass exodus to the cloud-based technologies have been witnessed. The businesses have also been faced with a never-ending race with the cybercriminals with ever-evolving threat sophistication levels. According to Nick Carr (2017), there are various techniques that have been employed by the cybercriminals to push through their agendas. These techniques include the use of ActiveMime files which makes use of the social engineering methods in enticing the victims to enable the macros in their devices. Macros are rules that specify how given input sequence ought to be mapped to the resulting output sequence using a defined procedure. The execution of these macros in the clients' computer prompts the initial file to download multiple payloads from the remote servers. The malicious files can also be delivered through the use of attachments in the spear phishing emails. In the past, cybercriminals have designed multilingual lure documents that have been tailored to specific victims using common file extensions such as .exe, .docx where the ActiveMine is archived with the contaminated texts and images. After these files have been created, they are exported to the web pages which are regularly visited by the target audience and the links shared with the victims. The ActiveMine files also contain OLE files to lure the unsuspecting users into opening them. The files contain fake error messages to trick the users into launching the macros.

In most cases, the error messages encourage the users to enable its content to solve the given error. For this reason, global organizations such as the European Union have come up with regulations such as the General Data Protection Regulation (GDPR) which focuses on the users' data. Thus, to find a permanent solution to the existing cybersecurity challenges, it is advisable for a CTO to acquire the services of any of these Cybersecurity companies since they have the resources as dedicated for research and the required capacity to deal with the ever-evolving technologies used by the cybercriminals in carrying out their activities.

References

A. Epishkina and S. Zapechnikov, "A syllabus on data mining and machine learning with applications to cybersecurity," 2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC), 2016.

Nick Carr, "Cyber Espionage is Alive and Well: APT32 and the Threat to Global Corporations," Cyber Threat!, pp. 1-10, 2017

S. N. Narayanan, A. Ganesan, K. Joshi, T. Oates, A. Joshi, and T. Finin, "Early Detection of Cybersecurity Threats Using Collaborative Cognition," 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC), 2018.

X. Du, "Data Mining and Machine Learning in Cybersecurity," 2011.