Cybersecurity Data Research Articles

Studi Literatur: Strategi Pengamanan Siber dalam Menghadapi Cyber Crime

In the current digital era, cybersecurity of data and information systems. The threat of attacks, or , is a form of threat that continues to grow along with advances in technology. This research aims to learn about cybersecurity strategies in dealing with . The method used in this research is a literature review. This journal was prepared based on an analysis of research results from various journals accessed through secondary data sources, namely the Google Scholar database, and e-resources from Springer, Cambridge, and Taylor and Francis. The research results show that implementing technology such as encryption, multifactor authentication, and user training, are some of the effective steps that have been proven to reduce risk. However, challenges in implementing this strategy remain, both from a technical and non-technical perspective, which require special attention from researchers and practitioners in the field of cyber security.

Cybersecurity lessons from the Vastaamo psychotherapy data breach for psychiatrists and other mental healthcare providers.

The Vastaamo psychotherapy data breach in Finland is perhaps the largest cybersecurity incident in mental healthcare to date, resulting in significant patient harm. There are specific lessons for mental healthcare providers from an analysis of the incident. Case study of this specific electronic health record data breach, based on detailed media reporting. The issues raised include: the importance of governance of the cybersecurity of sensitive personal patient data, such as compliance with legislative requirements on privacy and data security; specific security measures such as de-identification of data, data protection via passwords, multi-factor authentication, firewalls and encryption; and timely and effective communication, and support of those who have been affected. The implications for mental healthcare providers, including psychiatrists and trainees, are that, within their capability, providers need to assess the efficacy and robustness of cybersecurity of electronic health record systems they use, and carefully consider the information that is recorded to minimise exposures such as in the Vastaamo breach.

Machine Learning Algorithm for Enhanced Cybersecurity: Identification and Mitigation of Emerging Threats

Machine learning (ML) methodologies have significantly transformed cybersecurity by offering sophisticated instruments that not only identify but also avert and alleviate cyber threats. This research study seeks to examine the convergence of machine learning and cybersecurity, focusing on diverse methodologies and their use in enhancing cybersecurity measures. The study examines several machines learning methods, including Graph Neural Networks, Adversarial Learning, Federated Learning, Explainable AI, and Reinforcement Learning. Every algorithm is essential for enhancing the identification and mitigation of cyber assaults. Graph Neural Networks facilitate the modelling of intricate linkages within cybersecurity data. It aids not just in forecasting future events but also in identifying anomalies and analyzing network traffic. Adversarial Learning assists in training machine learning models to address the difficulty of producing misleading input data that can deceive any model, hence enhancing their efficacy. Federated Learning is examined as a method for training machine learning models across decentralized networks while preserving data privacy and enhancing model accuracy. Explainable AI methodologies primarily offer transparency and interpretability in machine learning-driven cybersecurity decisions, which are crucial for comprehension and confidence in automated security systems. Reinforcement Learning is focused on a trial-and-error methodology, wherein the model acquires new tasks through a system of rewards and penalties. These sophisticated algorithms jointly improve the effectiveness, precision, and clarity of cybersecurity protocols, offering strong protection against emerging cyber threats.

Deep Learning-Based Image Steganography for Visual Data Cybersecurity in Construction Management

Deep Learning-Based Image Steganography for Visual Data Cybersecurity in Construction Management

Features of creating a multi-level model of cyber security of critical infrastructure data: an interdisciplinary approach

The article examines the model of ensuring cyber security of Ukraine and documents the organizational and legal support for the protection of critical infrastructure objects from cyber attacks. The main concepts are described: multi-level data model, identification of cyber security, and critical infrastructure facilities. The levels of protection of critical infrastructure objects and measures at each level that ensure cyber security are highlighted and described. The stability of the critical infrastructure object is substantiated, and the formula for determining risks in critical infrastructure objects is described. The developed basic tuple model for the classification of critical information infrastructure objects by multi-level data models is highlighted. The prospects and risks of implementing multi-level data models in critical infrastructure facilities have been identified. It was concluded that multi-level data models are an important tool for ensuring cyber security in today's digital world and critical infrastructure enterprises in private. They allow for the effective protection of confidential information by isolating and controlling different levels of data access

Cybersecurity Data Sources and Practices

Cybersecurity Data Sources and Practices

Optimal Inference of Hidden Markov Models Through Expert-Acquired Data.

This paper focuses on inferring a general class of hidden Markov models (HMMs) using data acquired from experts. Expert-acquired data contain decisions/actions made by humans/users for various objectives, such as navigation data reflecting drivers' behavior, cybersecurity data carrying defender decisions, and biological data containing the biologist's actions (e.g., interventions and experiments). Conventional inference methods rely on temporal changes in data without accounting for expert knowledge. This paper incorporates expert knowledge into the inference of HMMs by modeling expert behavior as an imperfect reinforcement learning agent. The proposed method optimally quantifies experts' perceptions about the system model, which, alongside the temporal changes in data, contributes to the inference process. The proposed inference method is derived through a combination of dynamic programming and optimal recursive Bayesian estimation. The applicability of this method is demonstrated to a wide range of inference criteria, such as maximum likelihood and maximum a posteriori. The performance of the proposed method is investigated through a comprehensive numerical experiment using a benchmark problem and biological networks.

Unveiling Cyber Threat Actors: A Hybrid Deep Learning Approach for Behavior-based Attribution

In this paper, we leverage natural language processing and machine learning algorithms to profile threat actors based on their behavioral signatures to establish identification for soft attribution. Our unique dataset comprises various actors and the commands they have executed, with a significant proportion using the Cobalt Strike framework in August 2020-October 2022. We implemented a hybrid deep learning structure combining transformers and convolutional neural networks to benefit global and local contextual information within the sequence of commands, which provides a detailed view of the behavioral patterns of threat actors. We evaluated our hybrid architecture against pre-trained transformer-based models such as BERT, RoBERTa, SecureBERT, and DarkBERT with our high-count, medium-count, and low-count datasets. Hybrid architecture has achieved F1-score of 95.11% and an accuracy score of 95.13% on the high-count dataset, F1-score of 93.60% and accuracy score of 93.77% on the medium-count dataset, and F1-score of 88.95% and accuracy score of 89.25% on the low-count dataset. Our approach has the potential to substantially reduce the workload of incident response experts who are processing the collected cybersecurity data to identify patterns.

LOCKing Patient Safety: A Dynamic Cybersecurity Checklist for Healthcare Workers

Ensuring the cybersecurity of patient data is particularly challenging for healthcare organizations, and healthcare professionals play a key role here. Therefore, they must have the necessary knowledge and skills to be able to identify cybersecurity risks and respond appropriately to them. As part of the CyberSecPro project, this work-in-progress paper aims to provide healthcare professionals with a simple and memorable cybersecurity checklist highlighting important factors to consider. The purpose of the checklist is to support busy healthcare workers in implementing effective cybersecurity measures to secure sensitive information and guarantee patient privacy. The interview method was used to find out the cybersecurity challenges faced by healthcare workers and gather their opinions into a checklist. The mini-mental cybersecurity checklist created in the study, emphasizes the importance of being aware of cyber threats and maintaining secure and reliable information systems. Its name “LOCK” stands for Logging Out every time you leave your computer, Checking e-mails before opening links, and Keeping safe. Keep calm and LOCK on.

Understanding the role of artificial intelligence in enhancing GRC practices in cybersecurity

In cybersecurity, the integrity and security of data and systems can be preserved with governance, risk, and compliance (GRC) practices. As the complexity of cyber threats increases, organizations need to enhance their GRC practices with advanced technologies. This research article studies the role of artificial intelligence in reinforcing GRC practices in cybersecurity. The study provides a comprehensive GRC overview in the context of cybersecurity, highlighting its importance in maintaining a secure and compliant environment. It then examines how AI can enhance GRC practices, including analyzing data, automating compliance processes, and identifying potential threats. Furthermore, the paper discusses implementing AI-driven GRC solutions, outlining key organizational considerations and best practices. It also addresses ethical and regulatory considerations surrounding AI in GRC in decision-making processes. This study highlights insights into how AI can effectively strengthen cybersecurity GRC practices, ultimately helping governments and organizations mitigate risks and enhance their cybersecurity posture in an increasingly digital world.

Revolutionizing Cybersecurity with Machine Learning: A Comprehensive Review and Future Directions

In the realm of computing, data science has revolutionized cybersecurity operations and technologies. The key to creating automated and intelligent security systems lies in extracting patterns or insights from cybersecurity data and building data-driven models. Data science, encompassing various scientific approaches, machine learning techniques, processes, and systems, studies real-world occurrences through data analysis. Machine learning techniques, known for their flexibility, scalability, and adaptability to new and unknown challenges, have been applied across many scientific fields. Cybersecurity is rapidly expanding due to significant advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, and more. Various machine learning techniques have effectively addressed a wide range of computer security issues. This article reviews several machine learning applications in cybersecurity, including phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns associated with machine learning techniques themselves. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine learning model using supervised learning algorithms. These models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and employ ensemble methods that combine multiple machine learning models for improved performance

2023-2024 High School Big Data Challenge: Leveraging Generative AI and Data Cybersecurity to Conserve and Foster Local Biodiversity

The STEM Fellowship High School Big Data Challenge provides students with the unique opportunity of Open Data inquiry into one of the UN Sustainable Development Goals and experiential learning of fundamentals of data analysis – an essential skill set for a young researcher in the digital age. This year, students explore Generative AI and Data Cybersecurity to Conserve and Foster Local Biodiversity and to suggest their own evidence-based solutions following the principles of Open Science. They investigated different topics, ranging from Enhancing Forest Fire Predictions with Sequential Models for Ecosystem Preservation and Public Safety to Leveraging Semantic Segmentation to Perform Wildfire Prediction. We designed an interdisciplinary and agile educational environment, and in-depth learning modules for students as a means of bridging the gap between traditional high school courseware and digital reality and computational science. Students learned how to uncover hidden patterns and trends in structured and unstructured data using a range of data analytics tools and programming languages. Python, R, LaTeX, and machine learning were some of the tools the students learned and used. On behalf of the STEM Fellowship, we extend our sincere congratulations to all students who participated in the challenge, and wish them the best for their future endeavours. We want to express our appreciation to all the mentors and volunteers. This program would not be possible without patronage of CC UNESCO and generous support of our sponsors: RBC Future Launch, Let’s Talk Science, CISCO Networking Academy, Canadian Science Publishing, Schulich Foundation, SciNet at University of Toronto, and the University of Calgary Hunter Hub for Entrepreneurial Thinking. We were privileged to witness first-hand the analytical capabilities of the data-native generation of students, and we are confident they will demonstrate excellence throughout their academic and professional careers.

SYN-GAN: A robust intrusion detection system using GAN-based synthetic data for IoT security

As technological communication progresses, diverse datasets are exchanged across distributed environments using the Internet of Things (IoT). However, the IoT environment is vulnerable to attacking and breaching data privacy or making a robust system worse by providing attack data. To address potential risks of attacks, researchers have been conducting experiments on network intrusion detection systems (NIDS) to mitigate threats effectively. The issue of data imbalance and associated data collection costs persists, hindering the ability of machine learning (ML) models to learn malicious behaviour effectively and consequently impacting the accuracy of network threat detection. Addressing these issues, our study explores the potential of using 100% synthetic data generated via Generative Adversarial Networks (GAN) for training ML models in Network Intrusion Detection Systems (NIDS). This approach reduces the dependency on real-world data significantly, paving the way for a more flexible and ethically convenient model-building process. For the UNSW-NB15 dataset, we achieved an accuracy of 90%, a precision of 91%, a recall of 90%, and an F1 score of 89%. For the NSL-KDD dataset, our results showed an accuracy of 84%, a precision of 85%, a recall of 84%, and an F1 score of 84%. For the BoT-IoT dataset, we attained perfect scores of 100% across all metrics. These outcomes indicate that the values obtained from our analysis demonstrate high performance, yielding comparative or superior results to previous studies. Therefore, our study successfully replicates real-world network intrusion detection data, showing new opportunities for the use of generative data in cyber security.

A security framework to protect ePHI in Saudi Arabia's healthcare infrastructure

Today, protecting patient privacy and ensuring the accuracy and integrity of their data are the two most crucial concerns in the healthcare field. Unauthorized access or changes to patients' private health records can lead to serious issues. Moreover, if healthcare providers fail to update a patient's records quickly, it could result in dangerous, even life-threatening situations. Attacks on hospital computer systems also present a significant danger to patient care. Establishing strong security measures and procedures through cybersecurity frameworks can help protect sensitive patient information, known as electronic protected health information (ePHI). The Security Rule by Health Insurance Portability and Accountability Act (HIPAA), a well-established set of security guidelines, focuses on safeguarding ePHI held by healthcare organizations and their associates. This paper suggests creating a Data Cybersecurity Framework (DCF) specifically for the healthcare sector in Saudi Arabia. This framework aims to shield ePHI and align with the security recommendations of the HIPAA Security Rule. The development of this proposed framework involved consultations with healthcare cybersecurity experts and concentrated on the healthcare system in Saudi Arabia. The research concludes that enhancing the protection of patient information and raising public awareness requires the joint efforts of various entities, including government bodies.

Machine Learning (In) Security: A Stream of Problems

Machine Learning (ML) has been widely applied to cybersecurity and is considered state-of-the-art for solving many of the open issues in that field. However, it is very difficult to evaluate how good the produced solutions are, since the challenges faced in security may not appear in other areas. One of these challenges is the concept drift, which increases the existing arms race between attackers and defenders: malicious actors can always create novel threats to overcome the defense solutions, which may not consider them in some approaches. Due to this, it is essential to know how to properly build and evaluate an ML-based security solution. In this article, we identify, detail, and discuss the main challenges in the correct application of ML techniques to cybersecurity data. We evaluate how concept drift, evolution, delayed labels, and adversarial ML impact the existing solutions. Moreover, we address how issues related to data collection affect the quality of the results presented in the security literature, showing that new strategies are needed to improve current solutions. Finally, we present how existing solutions may fail under certain circumstances and propose mitigations to them, presenting a novel checklist to help the development of future ML solutions for cybersecurity.

Cybersecurity data breaches and internal control

The present study provides novel evidence of the effects of data breaches on firms' subsequent internal control. We find data breaches to be negatively associated with subsequent disclosure of internal control material weakness (i.e., improved internal control), and CEO dismissal to be an effective mechanism for improving internal control. We further find that data breaches can magnify the accounting expertise effect and attenuate the complexity effect on subsequent internal control, and that firms are less likely to file “Big R” restatements and face litigation subsequent to making improvements in internal control.

Detecting Anomalies and Intrusions in Unstructured Cybersecurity Data Using Natural Language Processing

Abstract: Due to the growing volume and variety in data generated by cybersecurity systems, it is crucial that unstructured text be used for detecting anomalies. Natural language processing is a powerful tool for analyzing unstructured information and identifying threats. This paper presents a comprehensive review of NLP applications for cybersecurity. We first present the motivations for and challenges associated with using NLP to improve cybersecurity. Then, we provide background information on unstructured data that is relevant to cybersecurity, and discuss NLP techniques such as named entity recognition (NEAR), sentiment analysis, topic modelling, and document classifying. This paper focuses on how these techniques are used to detect anomalies and intrusions. We present a taxonomy for NLP-driven approaches, and we conduct a literature review that is categorized according to this taxonomy. We examine critically the strengths and weaknesses of current techniques. We highlight research gaps based on this analysis and propose a research agenda to advance NLP research in cybersecurity applications. This paper summarizes previous research and lays the foundation for using NLP to tackle cybersecurity challenges that involve unstructured data.

Knowledge graph reasoning for cyber attack detection

AbstractIn today's digital landscape, cybercriminals are constantly evolving their tactics, making it challenging for traditional cybersecurity methods to keep up. To address this issue, this study explores the potential of knowledge graph reasoning as a more adaptable and sophisticated approach to identify and counter network attacks. By leveraging graph structures imbued with human‐like thinking, this method enhances the resilience of cybersecurity systems. The study focuses on three critical aspects: data preparation, semantic foundations, and knowledge graph inference techniques. Through an in‐depth analysis of these components, the research aims to reveal how knowledge graph reasoning can improve cyberattack detection and enhance the overall efficacy of cybersecurity measures, including intrusion detection systems. The proposed approach has undergone extensive experimentation to validate its effectiveness compared to existing methods. The results of the experiment have shown a remarkable advancement in accuracy, speed, and recall for recognition, surpassing current methods. This achievement is a notable contribution in the realm of managing big data in cybersecurity. The study establishes a foundation for the automation of network attack detection, ultimately enhancing overall network security.

Role of Machine Learning in Cybersecurity: Techniques and Challenges

Abstract: Machine learning (ML) plays a crucial role in cybersecurity across various aspects of threat detection, prevention, and response. Here are specific roles and applications of machine learning in cybersecurity. Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such a discrepancy has its root cause in the current state of the art, which does not allow us to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. 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. Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting data-bases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators

Role of Machine Learning in Cybersecurity: Techniques and Challenges

Abstract: Machine learning (ML) plays a crucial role in cybersecurity across various aspects of threat detection, prevention, and response. Here are specific roles and applications of machine learning in cybersecurity. Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such a discrepancy has its root cause in the current state of the art, which does not allow us to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. 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. Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting data-bases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators