Threat Of Cyber Attacks Research Articles

Formation of the TNI Cyber Force: A Strategic and Policy Analysis

The increasing cyber threats in Indonesia require strengthening the country's defense strategy. This study focuses on the formation of the Indonesian National Army (TNI) Cyber Force as part of efforts to strengthen the country's cyber security in facing increasingly complex threats. The purpose of this study is to analyze the policy and strategy for the formation of the TNI Cyber Force and evaluate the synergy between institutions, especially with the National Cyber ​​and Crypto Agency (BSSN). This study uses a qualitative method with a secondary data analysis approach, including reports from Fortinet, AwanPintar.id, and national policy documents related to cybersecurity. The research findings show that the TNI Cyber Force has a crucial role in protecting Indonesia's critical infrastructure from the threat of botnets, ransomware, and other cyber-attacks. The synergy between the TNI and BSSN is key to building an effective cyber defense system. Although there has been an increase in cyber readiness, this study found that challenges still exist, including in terms of human resources and the technology used. In conclusion, the formation of the TNI Cyber Force is an important step in dealing with cyber threats, but there needs to be further strengthening in cross-sector coordination and adoption of the latest technology.

Kashif: A Chrome Extension for Classifying Arabic Content on Web Pages Using Machine Learning

Search engines are significant tools for finding and retrieving information. Every day, many new web pages in various languages are added. The threats of cyberattacks are expanding rapidly with this massive volume of data. The majority of studies on the detection of malicious websites focus on English-language websites. This necessitates more studies on malicious detection on Arabic-content websites. In this research, we aimed to investigate the security of Arabic-content websites by developing a detection tool that analyzes Arabic content based on artificial intelligence (AI) techniques. We contributed to the field of cybersecurity and AI by building a new dataset of 4048 Arabic-content websites. We created and conducted a comparative performance evaluation for four different machine-learning (ML) models using feature extraction and selection techniques: extreme gradient boosting, support vector machines, decision trees, and random forests. The best-performing model was then integrated into a Chrome plugin, created based on a random forest (RF) model, and utilized the features selected via the chi-square technique. This produced plugin tool attained an accuracy of 92.96% for classifying Arabic-content websites as phishing, suspicious, or benign. To our knowledge, this is the first tool designed specifically for Arabic-content websites.

Cyber Security Awareness Model Based on NIST (National Institute of Standards and Technology) for Secondary School Students in Malaysia

As cybersecurity issues surge, it highlights the pressing need for increased awareness. In our digitally interconnected world, where information technology underpins the lives of individuals, businesses, and even national security, the threat of cyberattacks looms large. To enhance national cybersecurity preparedness, there is a critical call to action: we must promote public awareness of cybersecurity issues and bolster the ranks of trained cybersecurity professionals. This research introduces a novel cybersecurity awareness model based on the National Institute of Standards and Technology (NIST). Its primary goal is to make NIST accessible to secondary school students pursuing ICT courses and their invaluable resource, the school counselling units. The model aims to instil cybersecurity awareness in secondary school students and guide them in choosing the most suitable higher education paths, particularly in the ICT and cybersecurity fields. The study comprises three phases. In the first phase, a systematic literature review (SLR) examines globally applicable cybersecurity education models. The second phase investigates the implementation of cybersecurity education for secondary school students in Malaysia. This includes interviews with school counselling units responsible for guiding students toward higher education and surveys of students in grades four and five enrolled in ICT courses. The third and pivotal phase focuses on developing the proposed model. It involves analysing and implementing the results of the SLR, mapping them to the NIST framework, and incorporating themes from interviews and surveys. As a tangible representation of the model, a mobile application prototype has been developed. The Cybersecurity Awareness Model empowers school counselling units to seamlessly integrate career exploration with career decision-making for secondary students at any stage of their development. Moreover, it offers a means to create strategies for after-school programs, particularly in the dynamic field of cybersecurity. This model is expected to guide students in exploring academic majors and careers, preparing for higher education post-high school, initiating internships or job searches, and adapting to a world in constant flux. In doing so, it not only empowers individuals but also strengthens the nation's resilience in the face of cybersecurity challenges.

ARTIFICIAL INTELLIGENCE ENHANCED IDENTITY AND ACCESS MANAGEMENT PREVENTING UNAUTHORIZED ACCESS IN MODERN ENTERPRISES

As enterprises increasingly migrate to digital platforms, securing systems from unauthorized access becomes crucial to safeguarding sensitive data. Identity and Access Management (IAM) systems have traditionally been implemented to manage user access and authenticate identities. However, with growing threats of sophisticated cyber-attacks, traditional IAM methods are proving inadequate. Artificial Intelligence (AI) has emerged as a transformative force in enhancing IAM systems by providing adaptive, intelligent solutions to prevent unauthorized access. This paper explores the role of AI-enhanced IAM in modern enterprises, emphasizing its ability to predict, detect, and respond to potential security breaches. Through a detailed examination of current AI applications in IAM, this study demonstrates how AI-enabled tools can increase security resilience, reduce false positives, and automate responses to evolving threats, thus offering enterprises a proactive defense against unauthorized access.

The Impact of Cybersecurity Attacks on Human Trust in Autonomous Vehicle Operations.

This study examines the extent to which cybersecurity attacks on autonomous vehicles (AVs) affect human trust dynamics and driver behavior. Human trust is critical for the adoption and continued use of AVs. A pressing concern in this context is the persistent threat of cyberattacks, which pose a formidable threat to the secure operations of AVs and consequently, human trust. A driving simulator experiment was conducted with 40 participants who were randomly assigned to one of two groups: (1) Experience and Feedback and (2) Experience-Only. All participants experienced three drives: Baseline, Attack, and Post-Attack Drive. The Attack Drive prevented participants from properly operating the vehicle in multiple incidences. Only the "Experience and Feedback" group received a security update in the Post-Attack drive, which was related to the mitigation of the vehicle's vulnerability. Trust and foot positions were recorded for each drive. Findings suggest that attacks on AVs significantly degrade human trust, and remains degraded even after an error-less drive. Providing an update about the mitigation of the vulnerability did not significantly affect trust repair. Trust toward AVs should be analyzed as an emergent and dynamic construct that requires autonomous systems capable of calibrating trust after malicious attacks through appropriate experience and interaction design. The results of this study can be applied when building driver and situation-adaptive AI systems within AVs.

ANALIZA STATYSTYK SIECI KOMPUTEROWYCH W CELU IDENTYFIKACJI PRZEPŁYWÓW INFORMACJI ZAKŁÓCAJĄCYCH STABILNOŚĆ W WOJSKOWYCH SIECIACH LOKALNYCH

The increasing complexity and scope of military computer networks necessitate robust methods to ensure network stability and security. This study presents a comprehensive analysis of computer network statistics in military local networks to develop a method for detecting information flows that disrupt stability. By leveraging advanced statistical techniques and machine learning algorithms, this research aims to enhance the cybersecurity posture of military local networks globally. Military networks are vital for communication, data exchange, and operational coordination. However, the dynamic nature of network traffic and the persistent threat of cyberattacks pose significant challenges to maintaining network stability. Traditional monitoring techniques often fail to meet the unique requirements of military networks, which demand high levels of security and rapid response capabilities. This study employs a multi-faceted approach to detect anomalies in network traffic, utilizing statistical methods such as Z-score analysis, Principal Component Analysis (PCA), and Autoregressive Integrated Moving Average (ARIMA) models. Machine learning techniques, including Support Vector Machines (SVM), Random Forests, Neural Networks, K-means clustering, and Reinforcement Learning, are also applied to identify patterns indicative of stability-disrupting information flows. The integration of statistical and machine learning methods forms a hybrid model that enhances anomaly detection, providing a robust framework for network security. The research problem is formulated as follows: does data collection include comprehensive network traffic data from various segments of military local area networks, including packet flows, transmission rates, and error rates over a specified period? Statistical analysis identifies patterns in the network traffic, which are then used to train machine learning models to classify normal and abnormal traffic. The research hypothesis states that machine learning models achieve high accuracy in detecting stability-disrupting information flows, with a precision rate exceeding 90%. The models identified several instances of stability-disrupting events, correlating these with known security incidents to validate the effectiveness of the detection method. This study underscores the importance of continuous monitoring and analysis of network statistics to ensure stability and security. The proposed method can be integrated with existing network monitoring and intrusion detection systems, providing a comprehensive approach to network security. Future research can build on these findings to develop more sophisticated models and explore additional factors influencing network stability, including the incorporation of advanced machine learning techniques, such as deep learning, and the exploration of other network metrics, like latency and packet loss. This comprehensive approach aims to enhance the security and operational reliability of military local networks.

Artificial Intelligence, Fintech and Challenges to Central Banks

Abstract Technological development particularly boosted by artificial intelligence (AI) has substantial potential to transform many aspects of human lives and the way doing businesses. On the one side, it can offer opportunities, while on the other brings challenges and increases risks. Financial industry is considered the largest user of digital technologies and provider of innovative services. Therefore, it is strongly influenced by digital transformation and under constant threat of cyberattacks. In this paper, the authors are researching the opportunities and risks stemming from the application of AI and its macroeconomic and financial system impacts. The special attention is given to the challenges posed by financial technological development and AI to central banks as they have to adopt to the novel times dominated by electronic financial services and AI tools while at the same time stay persistently dedicated to achieving their key objectives of safeguarding monetary and financial stability as well as contributing to the stability of economic growth. Additionally, the invention of generative artificial intelligence (GenAI) has significantly influenced processes throughout numerous industries, including the financial sector, due to the ability to imitate human behaviour which has enabled computers to behave like humans. Hence, it is important to develop human-centric innovations where AI tools create benefits and serve people instead of replacing them. AI can deteriorate overall inequality so policymakers should act towards developing policies that will ensure AI is used for the good of people and provide benefits for them. The authors further draw attention to the necessity of adopting a robust regulatory framework and building strong and resilient institutions with developed systems for prevention of ever raising cyberattacks.

Automated Windows-Based Timelining Tool for Memory and Disk Image Analysis: Leveraging Timsort Algorithm with WinPmem, FTK Imager, Volatility 3 and The Sleuth Kit

As technology evolves, so does the threat of cyberattacks – making Digital Forensics crucial for damage control and prevention. This paper aims to address the inefficiencies faced by investigators in a forensic setting by automating the processes necessary for disk and memory image acquisition, forensic artifact parsing, and timeline generation. Leveraging publicly available tools such as: WinPmem, FTK Imager, Volatility 3 (VOL3), and The Sleuth Kit (TSK), the developed Python script is then able to provide for a clearer insight into the series of events that have transpired during a cyber incident through the generation of detailed and cohesively organized timelines, then using the Timsort algorithm for timeline analysis.

Examining the factors that impact the severity of cyberattacks on critical infrastructures

In light of the rising threats of cyberattacks on critical infrastructures, cybersecurity has become a high priority for government agencies worldwide. In particular, the severity of cyberattacks could lead to devastating consequences for national security, economic growth, and public health and safety. While earlier studies have examined several factors related to detecting, preventing, and predicting cyberattacks on critical infrastructures, they have largely neglected to consider the severity aspect of these attacks. This study aims to bridge this research gap by examining the factors that influence the severity of cyberattacks on critical infrastructures. To achieve this, we analyze 897 reported attacks on critical infrastructures to examine the impact of incident type, ransomware, zero-day vulnerability, attacker type, conflict type, initial access vector, and the number of targeted countries on the severity of these cyberattacks. The results show that cyberattacks employing ransomware and initiated by nation-state actors have the most impact on severity. On the contrary, cyberattacks that include data theft, disruption, hijacking with or without misuse, involve multiple types of conflict, and target the energy and finance sectors have the least impact on the severity of attacks. To gain further insight into these results, we perform sub-analyses on the metrics that makeup severity. Findings show that cyberattacks on the health sector are more vulnerable to data theft of sensitive information compared to other sectors. Also, nation-state-led attacks are more likely to involve data theft of sensitive information and long-term disruptions. Finally, as years progress, the results generally indicate a decreasing likelihood of attacks involving data theft of sensitive information and hijacking with misuse.

India’s Framework for Cyber Security since 2014 and its Significance for Armed Forces

The character of warfare changes with the change in its nature. In the last two decades, cyberattacks have expanded in their dimensions with the advent of digital networking in the military sector. Cyber security occupies a significant position in the Indian armed forces because of the increasing threat of cyberattacks from hostile neighbouring countries, particularly China and Pakistan. Cyber threat undermines military preparedness and warfighting capabilities by infiltrating critical weapon systems, vital infrastructure, crucial communication networks, and Intelligence, Surveillance, and reconnaissance (ISR) systems. India is now concentrating on framing its cyber security policy to mitigate the cyber threat and enhance the cyber capabilities of its armed forces. With its cyber security policy, India aspires to make digital armed forces. The present research article will focus on the efforts the Indian government has taken since 2014 to neutralize cyberattacks and the policies it has framed to ensure cyber security. The article will also analyze the significance of the government efforts on the armed forces in the direction of defensive cyber policy.

Improving resilience of cyber–physical power systems against cyber attacks through strategic energy storage deployment

The widespread integration of advanced technologies has increased the vulnerability of Cyber–Physical Power Systems (CPPS) against cyber attacks. In addition, the incorporation of renewable energy sources (RESs) poses extra uncertainty challenges in CPPS security. In this paper, focusing on resilience assessment and improvement, we first propose a comprehensive framework to reveal the whole process from attack initiation to performance recovery. Then, regarding the protection strategies before and after cyber attacks, we improve resilience performance by protecting critical nodes and deploying energy storage nodes. The effectiveness of our proposed methods is validated through testing on a simulated model, which couples the IEEE 39-bus power system with a scale-free communication network. The results demonstrate that the deployment of energy storage plays a significant role in suppressing the uncertainty of RESs and improving the resilience of CPPS against cyber attacks. In addition, we employ a heuristic algorithm to optimize the placement of energy storage nodes. Our work not only represents an overview of the resilience process but also provides system dispatchers with practical strategies for improving the resilience of renewable CPPS against the growing threat of cyber attacks.

Privacy challenges and policy implications of in-home monitoring systems

In the evolving landscape of the Internet of Things (IoT) home monitoring systems, this paper addresses the pressing privacy challenges and policy implications, highlighting the dual threats of sophisticated cyber-attacks and cloud-based vulnerabil- ities. We advocate a harmonized approach integrating technical solutions with regulatory frames to counter these issues. Our research systematically evaluates existing security mechanisms and introduces an innovative framework that blends advanced encryption techniques, robust data management practices, and comprehensive policy solutions to enhance IoT security. Central to this framework is a collaborative engagement model that brings together the efforts of legislative bodies, service provision entities, and the end-user community, ensuring a dynamic user- centric security posture against emerging threats. This study con- tributes a scalable, user-centric security model that significantly advances the discourse on IoT privacy and security, addressing the unique demands of IoT environments.

Consequence simulation of cyber attacks on key smart grid business cases

The increasing threat of cyber-attacks on modern power systems highlights the need for a comprehensive examination through simulations. This study conducts an in-depth simulation of cyber-attacks on critical smart grid components, including smart meters, substation automation, and battery management systems, to expose and analyze potential disruptions to power system operations. We identify vulnerabilities that can lead to severe grid instabilities, such as voltage variations, system collapses, and inverter failures. Our analysis underscores the complex interactions between cyber threats and grid components, revealing how disruptions extend beyond mere load interruptions to affect the core infrastructure. We advocate for integrating established cybersecurity frameworks like NIST, ISO/IEC 27001, and IEC 62443, essential in fortifying grid stability against these dynamic threats. Our findings highlight the urgent need for continuous adaptation and enforcement of these frameworks to enhance resilience and ensure the reliability of modern power grids against cyber-attacks.

Опыт проведения киберучений с использованием разработанного сценария

The threat of cyber attacks has become a serious problem for organizations, the solution of which also lies in the plane of modeling scenarios of possible attacks using digital twin technologies. A cyber polygon based on the AMPIRE software suite is used to study the impact of cyber threats. The attacker's scenario includes the use of various tactics and vulnerabilities, such as active scanning, provisioning, vulnerabilities in public applications, malicious links and files, and compromising domain user accounts. The vector of the malicious attack is related to the tactics, techniques and procedures of the Mitre ATT&CK matrix. Vulnerabilities include the wpDiscuz plugin, Zerologon, and the consequences of using Wordpress Shell and gaining unauthorized access. The ongoing intercollegiate cyber exercises have revealed strengths and weaknesses that need to be taken into account when conducting the following events. Scenario testing with the participation of universities showed the effectiveness of the complex in assessing the time required to close vulnerabilities and eliminate consequences. Implementation of different scenarios with specialized functionality at the cyber polygon makes it possible to form practical skills to prevent attacks on computer networks, and also allows you to analyze the situation, interact with other participating specialists.

Vulnerability of the technological infrastructure of the Salinas Fire Department, year 2023

The present research addressed the critical issue in the field of cybersecurity. The technological infrastructure of the Salinas Fire Department is exposed to significant risks due to the growing threat of cyber attacks. Given this situation, the main objective of the research was to assess the cyber risks present in the institution's information technology (IT) infrastructure. To achieve this objective, the methodology of risk analysis and management of information systems known as MAGERIT was employed, providing a systematic and structured framework to identify, analyze, and manage risks related to information security in an organization. In the context of this research, this methodology was applied to conduct a comprehensive risk analysis of the institution's infrastructure. During the analysis, risk levels were verified in each department, identifying critical areas of vulnerability that could compromise the integrity and operational effectiveness of the institution. The results confirmed the existence of critical areas of vulnerability that require immediate attention. For this reason, the creation of a contingency plan was proposed to reduce the risks identified during the analysis. This plan includes specific measures to strengthen the security of the Salinas Fire Department's technological infrastructure and ensure its operational effectiveness against potential cyber attacks.

Financial Technology as a Tool for Promoting Financial Inclusion in Nigeria

Financial technology (FinTech) as a concept and practice, started gaining serious worldwide traction following the outbreak of the global financial crisis in 2008. Thus, this paper is undertaken to critically discuss the emerging trends in financial technology and how they enhance financial inclusion, as well as the problems faced by financial technology in financial inclusion, with a specific focus on Nigeria. The paper adopts the exploratory qualitative research approach, using secondary data sources involving an extensive review of recent articles, periodicals, empirical and analytical studies, policy discussions, working papers, and other documented materials found to be pertinent to the study. The paper reveals that over the last decade, Fintech’s contribution to financial inclusion in Nigeria has exploded. FinTech has improved the usual way of rendering financial services and has created new inventions that have made financial activities not only more accessible but also faster, thereby easing the lives of financial service users in Nigeria, where mobile network technology is powering financial inclusion and bringing more people into the financial system. Despite the contributions of FinTech to financial inclusion in Nigeria, the paper notes the issue of high level of attrition in customer patronization and the threat of Cyber-attacks as major challenges. Based on this therefore, the paper recommends the need for regulatory bodies to do more to bring synergy between the operating companies and their interactions with the customers and prospective consumers for proper sensitization about these fintech services, considering the disinterest of many due to distrust of the system. Furthermore, the paper recommends that regulatory bodies should develop a more effective framework for monitoring cyber risk management at financial institutions using technology and laws that will duly prosecute cyber criminals and serve as a deterrent to others.

Cyber Security Incident Response

In response to the growing cyber-attack threat, incident response teams have become a critical component of an organization's cybersecurity strategy. These teams are responsible for detecting, analyzing, and responding to security incidents promptly and effectively. However, detecting code injection attacks can be particularly challenging, as they can be difficult to detect and often go unnoticed until it is too late. Cybersecurity professionals use detection tools to detect and respond to DLL injection attacks that monitor system activity and detect unusual behavior. A large portion of the related literature focuses on the use of commercial DLL injection tools. In contrast, little attention has been paid to the effectiveness of using open-source DLL injection detection tools. Thus, this research project aims to evaluate the effectiveness of three widely used open-source tools, VirusTotal, Sysinternals, and Yara, in detecting DLL injection incidents. This study's findings highlight each tool's strengths and limitations, which in turn enables cybersecurity professionals to make informed decisions when selecting the most suitable tool for DLL injection detection. Furthermore, the study emphasizes the importance of continuous tool development and updates to keep pace with evolving malware techniques and emerging threats. By highlighting the effectiveness of the tools, this research enhances the overall security posture of organizations and individuals, empowering them to mitigate the risks associated with DLL injection attacks proactively. The outcomes of this research project also underscore the significance of leveraging advanced tools to fortify cybersecurity defenses and safeguard critical systems and data.

DeepRoughNetID: A Robust Framework for Network Anomaly Intrusion Detection with High Detection Rates

Network security faces challenges, including reduced true positives, increased false positives, and inadequate anomaly detection efficacy. This paper introduces the DeepRoughNetID (DRNID) approach to address these issues and confront the increasing cyberattack threat in modern communication systems. DRNID presents an innovative framework for network intrusion detection, incorporating kNNImputer, GreedyRoughSelector, DeepVAEEnsembler, and IntrusionNet components. kNNImputer enables adaptable data preprocessing by leveraging instance-based learning, facilitating efficient handling of evolving datasets without necessitating full retraining. GreedyRoughSelector enhances classifier performance through systematic attribute selection, focusing on relevant features while eliminating redundancies. DeepVAEEnsembler leverages Variational Autoencoders (VAEs) to learn underlying data distributions, enabling robust anomaly detection. IntrusionNet utilizes VAEs to classify intrusions, providing a comprehensive solution to network security challenges. By integrating these components, DRNID offers a refined approach that promises significant advancements in network intrusion detection. The experimental results demonstrate the effectiveness of DRNID, with an impressive accuracy of 98.8% achieved through 10-fold cross-validation. DRNID exhibits superior performance compared to existing methods across various datasets, showcasing its robust anomaly detection capabilities. This methodology empowers organizations to proactively prevent security violations and safeguard sensitive data from malicious entities. Beyond its theoretical contributions, our research carries tangible implications for strengthening cybersecurity defenses across diverse sectors, including telecommunications, finance, and healthcare.

Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids

The evolution of smart grids has led to technological advances and a demand for more efficient and sustainable energy systems. However, the deployment of communication systems in smart grids has increased the threat of cyberattacks, which can result in power outages and disruptions. This paper presents a semi-supervised hybrid deep learning model that combines a Gated Recurrent Unit (GRU)-based Stacked Autoencoder (AE-GRU) with anomaly detection algorithms, including Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptical Envelope. Using GRU units in both the encoder and decoder sides of the stacked autoencoder enables the effective capture of temporal patterns and dependencies, facilitating dimensionality reduction, feature extraction, and accurate reconstruction for enhanced anomaly detection in smart grids. The proposed approach utilizes unlabeled data to monitor network traffic and identify suspicious data flow. Specifically, the AE-GRU is performed for data reduction and extracting relevant features, and then the anomaly algorithms are applied to reveal potential cyberattacks. The proposed framework is evaluated using the widely adopted IEC 60870-5-104 traffic dataset. The experimental results demonstrate that the proposed approach outperforms standalone algorithms, with the AE-GRU-based LOF method achieving the highest detection rate. Thus, the proposed approach can potentially enhance the cybersecurity in smart grids by accurately detecting and preventing cyberattacks.

Smart Defense: How Self-Learning AI Can Shield Bangladeshi Medical Records

The digitalization of healthcare records in Bangladesh presents both opportunities and challenges, particularly concerning the security and protection of sensitive patient information. As electronic health records (EHRs) become increasingly prevalent, the threat of cyberattacks targeting medical data escalates, necessitating innovative solutions to fortify the country's healthcare cybersecurity infrastructure. This paper investigates the efficacy of self-learning artificial intelligence (AI) systems in safeguarding Bangladeshi medical records against cyber threats. The traditional methods of securing medical records, such as firewalls and antivirus software, are proving inadequate against the evolving tactics of cybercriminals. Bangladesh faces unique challenges in this regard, including limited resources, lack of cybersecurity awareness among healthcare professionals, technological fragmentation, and an increasingly sophisticated threat landscape. To address these challenges, there is a growing imperative to explore novel approaches that can adapt and evolve in real-time to counter emerging cyber threats. Self-learning AI systems represent a promising frontier in healthcare cybersecurity. By leveraging advanced machine learning algorithms, these systems can analyze vast amounts of data to detect patterns indicative of cyber threats. Unlike static security measures, self-learning AI continuously learns from new information and adjust their defense strategies, accordingly, enabling them to stay ahead of evolving threats. Key functionalities of self-learning AI include anomaly detection, threat prediction, and adaptive defense mechanisms, all of which are essential for safeguarding medical records in Bangladesh's healthcare landscape. The implications of integrating self-learning AI into Bangladesh's healthcare cybersecurity framework are significant. Not only can these technologies enhance the detection and prevention of cyber threats, but they can also alleviate resource constraints and technical challenges faced by healthcare organizations. However, successful implementation requires comprehensive training, adherence to data privacy regulations, and ongoing monitoring to ensure the effectiveness and reliability of AI-driven security measures. The protection of medical records is paramount as Bangladesh continues its digital transformation in healthcare. Self-learning AI offers a dynamic and proactive approach to cybersecurity, empowering healthcare organizations to mitigate risks and preserve patient privacy in an increasingly digitized landscape. Embracing these innovative technologies is crucial for building a resilient healthcare ecosystem that prioritizes data security and patient trust.