In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.
See Full PDF See Full PDFDue to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today's cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision.
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European Journal of Technology
In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Due to its distinctive qualities, such as flexibility, scalability, and the capability to quickly adapt to new and unknowable obstacles, machine learning techniques have been used in many scientific fields. Due to notable advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, etc., cyber security is a rapidly expanding sector that requires a lot of attention. Such a broad range of computer security issues have been effectively addres.
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International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Through the use of relevant data to build an algorithm, machine learning primarily aims to automate human help. A subset of artificial intelligence (AI), machine learning (ML) focuses on the development of systems that can learn from past data, recognize patterns, and reach logical conclusions with little to no human involvement. The concept of cyber security involves guarding against hostile attack on digital systems such computers, servers, mobile devices, networks, and the data they are connected to. Accounting for cyber security where machine learning is used and using machine learning to enable cyber security are the two main components of combining cyber security and ML. We may benefit from this union in a number of ways, including by giving machine learning models better security, enhancing the effectiveness of cyber security techniques, and supporting the efficient detection of zero day threats with minimal human involvement. In this review paper, we combine ML and cyber security to talk about two distinct notions. We also talk about the benefits, problems, and difficulties of combining ML and cyber security. In addition, we explore several attacks and present a thorough analysis of various tactics in two different categories. Finally, we offer a few suggestions for future research.
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Journal of Cybersecurity and Privacy
Machine learning is of rising importance in cybersecurity. The primary objective of applying machine learning in cybersecurity is to make the process of malware detection more actionable, scalable and effective than traditional approaches, which require human intervention. The cybersecurity domain involves machine learning challenges that require efficient methodical and theoretical handling. Several machine learning and statistical methods, such as deep learning, support vector machines and Bayesian classification, among others, have proven effective in mitigating cyber-attacks. The detection of hidden trends and insights from network data and building of a corresponding data-driven machine learning model to prevent these attacks is vital to design intelligent security systems. In this survey, the focus is on the machine learning techniques that have been implemented on cybersecurity data to make these systems secure. Existing cybersecurity threats and how machine learning techniqu.
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Cyber crime is proliferating everywhere exploiting every kind of vulnerability to computing environment. Ethical Hackers pay more attention towards assessing vulnerabilities and recommending mitigation methodologies. The development of effective techniques has been an urgent demand in the field of the cybersecurity community. Machine Learning for cybersecurity has become an issue of great importance recently due to th e effectiveness of machine learning and deep learning in cybersecurity issues. Machine learning techniques have been applied for ma jor challenges in cybersecurity issues like intrusion detection, malware classification and detection, spam detection and phishing detection. Although machin e learning cannot automate a complete cybersecurity system, it helps to identify cyber-security threats more efficiently than other softwareoriented methodologies, and thus reduces the burden on security analysts. Ever evolving nature of cyber threats throws challenges continuously on the researchers to e xplore with the ideal combination of deep expertise in cybersecurity and in data science. In this paper, we present the current state of art machine learning applications and their potential for cybersecurity. An analysis of machine learning algorithms for most common types of cybersecurity threats is presented. In a computing context, cybersecurity is going through gigantic movements in innovation and its tasks as of late, and information science is driving the change. Extricating security occurrence examples or experiences from cybersecurity information and building relating information driven model, is the way to make a security framework mechanized and smart. To comprehend and examine the genuine marvels with information, different logical strategies, AI methods, cycles, and frameworks are utilized, which is ordinarily known as information science. In this paper, we zero in and quickly talk about on cybersecurity information science, where the information is being assembled from important cybersecurity sources, and the investigation supplement the most recent information driven examples for giving more powerful security arrangements. The idea of cybersecurity information science permits making the computing cycle more significant and shrewd when contrasted with conventional ones in the area of cybersecurity. We at that point examine and sum up various related examination issues and future bearings. Moreover, we give an AI based multi-layered structure with the end goal of cybersecurity demonstrating. Generally speaking, our objective isn't just to talk about cybersecurity information science and important techniques yet in addition to center the relevance towards information driven savvy dynamic for shielding the frameworks from digital assaults.
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International Journal of Data Science and Big Data Analytics
In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, I have briefly described the data science its evolution its applications in cloud security and how cybersecurity data science came in existence what kind of advantages are given by Cybersecurity Data Science (CSDS) and its steps like, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. After that I have described the various upcoming challenges that can emerge after the frequent applications of CSDS, how machine learning and deep learning are applicable in it and types of algorithms that can be applicable in it. So, the overall paper is not only focuses on the origins of Data Science but it also describes its modern uses for the relevant cybersecurity field and data driven intelligent decision making system can protect our system from known and unknown cyber attacks.
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International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This literature review paper aims to examine and analyze the existing research on prediction models for different types of cyber-attacks. Four key research papers have been selected as the base for this review: "A Prediction Model of DoS Attack's Distribution Discrete Probability," "Apriori Viterbi Model for Prior Detection of Socio-Technical Attacks," "Cyber Attacks Prediction Model Based on Bayesian Network," and "Applying Data Science to Cybersecurity Network Attacks & Events.” An overview of the value of prediction models in cybersecurity and their function in reducing potential threats come first in the review. The methodology section outlines the search strategy used to identify relevant literature and the selection criteria for the base papers. The subsequent sections provide an overview of the field, highlighting the historical development and key theories or frameworks related to cyber-attack prediction. The themes or subtopics identified in the literature are discussed, focusing on the discrete probability distribution model of DoS attacks, the Apriori Viterbi model for detecting socio-technical attacks, the Bayesian network-based prediction model, and the application of data science in analyzing network attacks and events. The review critically evaluates the selected papers, analyzing their methodologies, findings, and limitations. It identifies gaps, controversies, and conflicting findings in the literature, paving the way for further research in the field. The synthesis and interpretation section integrates the findings from the different studies, compares various perspectives, and discusses the implications and significance of the literature for cyber-attack prediction. In conclusion, this literature review paper provides a comprehensive analysis of prediction models for cyber-attacks, based on the selected base papers. It highlights the strengths and weaknesses of existing approaches, identifies research gaps, and offers recommendations for future studies. This review contributes to the advancement of knowledge in the field of cybersecurity and aids in the development of more effective prediction models to combat evolving cyber threats.
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In the age of digital transformation with adoption of Cloud and mobile computing and ever-increasing Internet of Things(IoT) devices, the cybersecurity risks and threat levels are increasing at a rapid pace. The data is spread across systems, devices and cloud leading to growing attack surface and increased frequency of the security attacks. IoT is extended to drones, driver-less cars, industrial equipment, smart buildings, consumer goods, home appliances leaving us with more vulnerable attack points. Organizations needs to have effective information security management system (ISMS) in place to proactively detect, react to security threats with reduced time to discover any potential breach. This paper highlights how Artificial Intelligence(AI) and Machine Learning(ML) can redefine cybersecurity to detect, prevent organizations from security threats and data breaches.
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International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Machine learning (ML) is transforming cybersecurity by enabling advanced detection, prevention and response mechanisms. This paper provides a comprehensive review of ML's role in cybersecurity, examining both theoretical frameworks and practical implementations. It outlines the emerging threats targeting ML models, such as adversarial attacks, data poisoning and model inversion attacks and discusses state-of-the-art defense strategies, including adversarial training, robust architectures and differential privacy. Additionally, the paper explores various ML applications in cybersecurity from intrusion detection to malware classification, highlighting their impact on enhancing security measures. An anomaly inference algorithm is proposed for the early detection of cyber-intrusions at the substations. Cybersecurity has become a vital research area. The paper concludes with a discussion on the key research directions and best practices for creating secure and resilient ML systems in a data-driven world. This paper delves into how Machine Learning (ML) revolutionizes cybersecurity, empowering advanced detection, prevention, and response mechanisms. It offers a thorough exploration of ML's pivotal role in cybersecurity, encompassing theoretical frameworks and practical applications. It addresses emerging threats like adversarial attacks and data poisoning, alongside cutting-edge defense strategies such as adversarial training and robust architectures.
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