Cybersecurity Threats Research Articles

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. We conducted extensive testing in both binary and multiclass scenarios. The results are remarkable, demonstrating a perfect 100% accuracy in binary classification, an impressive 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification, setting new benchmarks in the field. These achievements underscore the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive IDS for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats.

ORGANIZATION OF INFORMATION PROTECTION AT THE «DRIVE PETROL» ENTERPRISE USING A CRYPTOGRAPHIC ALGORITHM AES

In today's digital age, information protection is becoming a dominant task, as the number of threats in the field of cyber security is constantly increasing. In this context, the implementation of effective protection means, among which encryption takes a key place, becomes especially important. The AES (Advanced Encryption Standard) cipher appears to be an exceptionally powerful tool aimed at ensuring data privacy. Information security is an extremely important aspect in the digital age, where cyber security threats are constantly increasing. In this context, encryption becomes a necessity, and the AES (Advanced Encryption Standard) cipher appears to be an exceptionally effective tool for ensuring data privacy. AES is used to protect information by converting it into cryptographically unreadable form. Due to the high degree of complexity and the possibility of using keys of different lengths, from 128 to 256 bits, AES guarantees a high level of security. Its resistance to attacks provides reliable protection against unauthorized access. One of the key advantages of AES is its versatility – it is used in a variety of industries, including finance, medicine, telecommunications, and more. The cipher has high performance, which makes it the optimal solution for protecting confidential information in a world where security becomes a priority. The application of AES not only protects data from unauthorized access, but also contributes to the overall level of security in the digital environment, ensuring excellent compatibility with various industries and user needs.

Search engine optimization poisoning: A cybersecurity threat analysis and mitigation strategies for small and medium-sized enterprises

This study investigates the emerging cybersecurity threat of search engine optimization (SEO) poisoning and its impact on small and medium-sized enterprises’ (SMEs) digital marketing efforts. Through a comprehensive analysis of SEO poisoning techniques employed by attackers, the study reveals the significant risks and consequences for SMEs, including reputational damage, financial losses, and disrupted operations. To address these threats, the study proposes tailored mitigation strategies aligned with the principles of the NIST Cybersecurity Framework while considering the resource constraints facing SMEs. The mitigation recommendations encompass technical measures such as website security audits and employee training alongside non-technical initiatives to foster a culture of cybersecurity awareness. Additionally, the study offers several discussions that elucidate the multifaceted challenges posed by SEO poisoning in the SME context from both internal and external perspectives. These contributions will empower SMEs and digital marketers to implement proactive safeguards against SEO poisoning risks and preserve their online presence. The study underscores the need for continued vigilance and adaptive security to combat the evolving tactics of cyber adversaries in the digital marketing domain.

Taxonomy-Driven Graph-Theoretic Framework for Manufacturing Cybersecurity Risk Modeling and Assessment

Abstract Identifying, analyzing, and evaluating cybersecurity risks are essential to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. However, a manufacturing-specific quantitative approach is lacking to effectively model threat events and evaluate the unique cybersecurity risks in discrete manufacturing systems. In response, this paper introduces the first taxonomy-driven graph-theoretic model and framework to formally represent this unique cybersecurity threat landscape and identify vulnerable manufacturing assets requiring prioritized control. First, the proposed framework characterizes threat actors’ techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. This facilitates the systematic generation of comprehensive and generalizable cyber-physical attack graphs for discrete manufacturing systems. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. The risk model captures the cascading attack impact of varying attack methods through different cyber and physical entities in manufacturing systems, leading to specific consequences. Then, the constructed cyber-physical attack graphs are analyzed to comprehend threat propagation through the discrete manufacturing value chain and identify potential attack paths. Third, a quantitative risk assessment approach is presented to evaluate the cybersecurity risk associated with potential attack paths. It also identifies the attack path with the maximum likelihood of success, pointing out critical manufacturing assets requiring prioritized control. Finally, the proposed risk modeling and assessment framework is demonstrated using an illustrative example.

Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0

Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts.

CYBERSECURITY CHALLENGES IN SMART CITIES: A CASE REVIEW OF AFRICAN METROPOLISES

The rapid urbanization and digital transformation of cities across Africa have given rise to the concept of Smart Cities, where advanced technologies are integrated to enhance efficiency, sustainability, and the overall quality of urban life. However, this paradigm shift towards interconnected and technology-driven urban environments brings forth a host of cybersecurity challenges that demand careful consideration. This paper explores the cybersecurity challenges in Smart Cities, focusing on a case review of African metropolises. African cities, emblematic of the global urbanization trend, are embracing Smart City initiatives to address urban challenges and foster economic development. While these initiatives promise improved services and enhanced connectivity, they concurrently expose cities to a myriad of cybersecurity threats. The interconnectedness of devices and systems in Smart Cities creates a vast attack surface, making them susceptible to cyber-attacks ranging from data breaches to infrastructure disruptions. This case review delves into specific instances of cybersecurity challenges faced by African metropolises in their quest for technological advancement. It analyzes the vulnerabilities in critical infrastructure, such as energy grids, transportation systems, and healthcare networks, highlighting the potential risks associated with inadequate cybersecurity measures. Moreover, the paper sheds light on the socio-economic implications of cyber threats in Smart Cities, emphasizing the importance of resilient cybersecurity frameworks in safeguarding citizen data and urban functionality. In conclusion, the paper underscores the urgent need for comprehensive cybersecurity strategies tailored to the unique challenges faced by Smart Cities in Africa. The findings aim to contribute to a better understanding of the intricate relationship between urbanization, technology, and cybersecurity, offering insights that can inform policy decisions, technological implementations, and collaborative efforts to build secure and resilient Smart Cities in the African context.
 Keywords: Cybersecurity, Smart Cities, Africa, Metropolis, Review.

Resilient Secondary Control and Stability Analysis for DC Microgrids Under Mixed Cyber Attacks

Distributed control technology has significantly improved the regulation of DC microgrid systems. However, it also introduces potential cyber-security threats during the communication process. In particular, the system is vulnerable to two types of attacks: denial-of-service (DoS) and false data injection (FDI) attacks. If both attacks occur simultaneously, they can destabilize the DC microgrid and jeopardize its safe operation. To ensure system stability under mixed DoS and unbounded FDI attacks, a consensus-based secondary control strategy is proposed. This strategy includes an adaptive feedback term to counteract FDI signals in the presence of DoS attacks. The stability of the system is rigorously analyzed using the Lyapunov method. Finally, numerical and experimental tests confirm the effectiveness of the proposed strategy.

E-Business Ecosystems: Understanding the Dynamics of Digital Platforms and Marketplaces

The study investigates the intricate dynamics of e-business ecosystems, focusing on factors such as network effects, platform governance, technological innovation, and regulatory scrutiny. Through a comprehensive review of literature and analysis of various perspectives, the research aims to elucidate the evolving landscape of digital platforms and marketplaces. Employing a qualitative research design, the study synthesizes insights from scholarly articles, industry reports, and expert opinions to discern patterns and trends in e-business ecosystems. The findings underscore the transformative impact of digital platforms on traditional business models, highlighting the emergence of platform-based business models and the democratization of commerce. Moreover, the study delves into the implications of these dynamics for competition, regulation, and consumer behavior within e-business ecosystems. It identifies key challenges such as platform competition, regulatory scrutiny, data privacy concerns, and cybersecurity threats, and explores strategies for addressing them. The implications of the research extend to businesses, policymakers, and scholars, informing strategic decision-making, regulatory frameworks, and future research agendas. By advancing our understanding of e-business ecosystems, the study contributes to the development of innovative solutions and strategies that drive economic growth, foster innovation, and promote inclusive prosperity.

AI and Cyber Security with Reference to Information Technology Act, 2000 and other Laws in India

Abstract: The term artificial intelligence (AI) is prominent in today’s world of technology. In so many ways, it is still a developing science considering the problems presented by the twenty-first century. AI use has been established in daily life. Since AI has such a significant influence on human existence now, it is difficult to comprehend a world without it. Simply define, artificial intelligence (AI) is the study of how people think, work, learn, and decide in every situation in life, whether it is connected to problem-solving, learning something new, thinking logically, or coming up with a solution, etc. If Artificial Intelligence mostly connected with the daily life of the human being it must be protect the relationship between the human being and society people and there should be remedy to the peoples in case of violation of laws by AI machines. Cyber security and cyber law already new concept in the development of modern laws of the country. Cybercrimes are becoming common and frequently reported in the news. It is a global challenge, not simply one that affects one nation. AI is meaningless without strong security measures since it may be readily accessible by outsiders. Governments, banks, and global corporations now face a serious threat because of cyber security threats. Hackers use a lot of private and business data to their advantage, which poses a serious threat to the online community

Parsing Data Log Hasil Pemblokiran Situs Negatif Di Satuan Kerja Perangkat Aceh

In today's digital age, internet access has become an integral part of everyday life, providing broad access to information and communication. However, these technological advances also bring negative impacts, including the potential for the spread of unwanted content or harm to society. The process of parsing negative site blocking datalog is a technique used to analyze and decipher log data resulting from site blocking activities. In this context, negative site blocking is not only focused on illegal content, but also involves efforts to mitigate cybersecurity threats, including phishing attacks, malware, and other malicious online activities. Use of Observation Methods (Observation), Interviews, Literature Studies where the author made direct observations at the Aceh Communication, Informatics and Coding Agency to find out about the negative site blocking datalog in the Aceh Equipment Work Unit, conducted direct interviews with Field Supervisors and staff at the agency to find out about the process of parsing negative site blocking datalogs at the Aceh Equipment Work Unit, collect and study references related to data parsing, negative site categories, websites, CodeIgniter 4 documentation, Bootstrap documentation, and information system design tools from various journals, theses, articles, and various other reference sources. The proposed system for parsing datalogs has clear functional and non-functional needs. Functional needs involve features such as login, blocking data management, category management, blocking graph representation and reporting. Meanwhile, non-functional needs include authentication security, responsive performance, and an intuitive user interface. Thus, parsing log data resulting from negative site blocking in the Aceh Device Work Unit is expected to provide significant benefits for Diskominsa Aceh in maintaining cyber security and morality in the use of the internet in its territory. These conclusions highlight the importance of implementing sophisticated technology solutions to understand, analyze, and manage block log data efficiently and effectively.
 

Security Challenges in the Application of Blockchain Technology in Energy Trading

This paper examines the interplay between blockchain technology and the energy sector, focusing on security limits, barriers, and challenges. The authors discusses the primary components of cyber risks, including threats, vulnerabilities, and impacts that plague blockchain systems and their application, network, and data layers. Further, anonymity is a key feature of blockchain, ensuring that blockchain users, nodes, and miners remain unidentifiable by any measure. Therefore, perpetrator-focused measures are not viable when assigning responsibility for dangerous and illegal conduct. There are concerns that the concealment of identity will broaden blockchain attack surfaces and pose risks to energy security. The authors also emphasises the need for a well-defined and consistent legal and regulatory framework to address the complexities of blockchain development in the energy sector and assert that the maturity of blockchain in this industry will depend on balancing security and user rights and suggest implementing ex-ante and ex-post measures. This paper is novel; the author seeks to provide an in-depth analysis of the security challenges faced by blockchain-based energy applications and offer practical solutions for mitigating these cybersecurity threats and vulnerabilities.

A comprehensive review of embedded systems in autonomous vehicles: Trends, challenges, and future directions

The integration of embedded systems in autonomous vehicles represents a transformative paradigm shift in the automotive industry, offering unprecedented opportunities for enhanced safety, efficiency, and user experience. This comprehensive review explores the current landscape of embedded systems in autonomous vehicles, delving into emerging trends, persistent challenges, and future directions that shape the trajectory of this rapidly evolving field. The review begins by examining the foundational concepts of embedded systems in the context of autonomous vehicles, elucidating the intricate interplay between hardware and software components. It surveys the state-of-the-art technologies that empower these systems, including advanced sensors, actuators, and communication protocols, highlighting their pivotal roles in perception, decision-making, and control aspects of autonomous driving. One of the prominent trends discussed in this review is the increasing reliance on artificial intelligence (AI) and machine learning algorithms within embedded systems. The incorporation of these intelligent algorithms enables vehicles to adapt and learn from real-world scenarios, enhancing their ability to navigate diverse and dynamic environments. Additionally, the review sheds light on the growing emphasis on connectivity and edge computing, illustrating how embedded systems leverage these technologies to facilitate seamless communication between vehicles and their surrounding infrastructure. Despite the promising advancements, the review critically examines the persistent challenges that impede the widespread adoption of embedded systems in autonomous vehicles. Issues such as safety concerns, cybersecurity threats, and regulatory frameworks are analyzed, providing insights into the complex ecosystem in which these technologies operate. In addressing the future directions of embedded systems in autonomous vehicles, the review envisions a trajectory marked by continuous innovation and collaboration across industries. It anticipates the evolution of embedded systems towards more robust, adaptive, and fault-tolerant architectures, paving the way for increased autonomy and widespread deployment of autonomous vehicles. This comprehensive review provides a holistic understanding of embedded systems in autonomous vehicles, encapsulating current trends, challenges, and future directions. As the automotive landscape undergoes a paradigm shift, this review serves as a valuable resource for researchers, practitioners, and policymakers seeking to navigate the dynamic terrain of autonomous vehicle technology.

Penerapan Sanksi Pidana terhadap Kejahatan Siber: Sebuah Kajian terhadap Perkembangan Hukum

Cybercrime has become a serious challenge in the digital age, requiring a dynamic legal approach to respond to this threat. This study aims to investigate legal developments in the application of criminal sanctions against cybercrimes. Through a critical analysis of various cases and regulatory developments, this study will explore the effectiveness of criminal sanctions applied in tackling cybercrime. This research method includes comparative law analysis, case studies, and interviews with criminal law experts. Research findings will include the evolution of the concept of cybercrime, the legal challenges faced in enforcing criminal sanctions, and the impact of the application of criminal law on cybersecurity levels The results of this study are expected to provide in-depth insight into the progress of criminal law in responding to cybercrime, as well as provide a basis for further improvement and development in the relevant legal framework. The practical implications of this research are expected to help authorities, legal professionals, and academics in understanding the dynamics of criminal law related to cybercrime, so as to formulate policies that are more effective and responsive to evolving cybersecurity threats.

Indonesia Government Policy Strategy to Support Autonomous Vehicles Implementation for Public Transport

Autonomous vehicles are now a unique and modern new trend in the development of public transport. Its application in various countries such as China, South Korea, Germany, and the United States is interesting to study from policy to implementation. Indonesia also has a great opportunity to develop autonomous vehicles to reach level 3 to 5, considering that the New Capital City (IKN) also has plans to use Integrated Transport System (ITS) and autonomous vehicles as public transport in the future. However, challenges and obstacles such as road facilities, cybersecurity threats, and public indiscipline can be a barrier to the implementation of autonomous vehicles in Indonesia. Therefore, this article aims to take a deeper look into the needs that Indonesia needs to make a policy on autonomous vehicles as public transport in the future.

A Review of IoT Firmware Vulnerabilities and Auditing Techniques.

In recent years, the Internet of Things (IoT) paradigm has been widely applied across a variety of industrial and consumer areas to facilitate greater automation and increase productivity. Higher dependability on connected devices led to a growing range of cyber security threats targeting IoT-enabled platforms, specifically device firmware vulnerabilities, often overlooked during development and deployment. A comprehensive security strategy aiming to mitigate IoT firmware vulnerabilities would entail auditing the IoT device firmware environment, from software components, storage, and configuration, to delivery, maintenance, and updating, as well as understanding the efficacy of tools and techniques available for this purpose. To this effect, this paper reviews the state-of-the-art technology in IoT firmware vulnerability assessment from a holistic perspective. To help with the process, the IoT ecosystem is divided into eight categories: system properties, access controls, hardware and software re-use, network interfacing, image management, user awareness, regulatory compliance, and adversarial vectors. Following the review of individual areas, the paper further investigates the efficiency and scalability of auditing techniques for detecting firmware vulnerabilities. Beyond the technical aspects, state-of-the-art IoT firmware architectures and respective evaluation platforms are also reviewed according to their technical, regulatory, and standardization challenges. The discussion is accompanied also by a review of the existing auditing tools, the vulnerabilities addressed, the analysis method used, and their abilities to scale and detect unknown attacks. The review also proposes a taxonomy of vulnerabilities and maps them with their exploitation vectors and with the auditing tools that could help in identifying them. Given the current interest in analysis automation, the paper explores the feasibility and impact of evolving machine learning and blockchain applications in securing IoT firmware. The paper concludes with a summary of ongoing and future research challenges in IoT firmware to facilitate and support secure IoT development.

The applicability of a hybrid framework for automated phishing detection

Phishing attacks are a critical and escalating cybersecurity threat in the modern digital landscape. As cybercriminals continually adapt their techniques, automated phishing detection systems have become essential for safeguarding Internet users. However, many current systems rely on single-analysis models, making them vulnerable to sophisticated bypass attempts by hackers. This research delves into the potential of hybrid approaches, which combine multiple models to enhance both the robustness and effectiveness of phishing detection. It highlights existing hybrid models' limitations that focus primarily on effectiveness while ignoring broader applicability. To address these gaps, we introduce a novel framework explicitly designed for applicability in the real world, which poses the foundation for practical and robust phishing detection architectures. We develop a proof of concept to evaluate its effectiveness, robustness, and detection speed. Additionally, we introduce an innovative methodology for simulating bypass attacks on single-analysis base models. Our experiments demonstrate that the proposed hybrid framework outperforms individual models, displaying higher effectiveness, robustness against bypassing attempts, and real-time detection capabilities. Our proof of concept achieves an accuracy of 97.44% thereby outperforming the current state-of-the-art approach while requiring less computational time. The results provide insights into the multifaceted factors of hybrid models, extending beyond mere effectiveness, and emphasize the importance of holistic applicability in hybrid approaches to address the critical need for robust defenses against phishing attacks.

Research on Development of Cybersecurity Software and Threat Intelligence Techniques

This research project looks on the dynamic interactions that exist between threat intelligence technique strategic integration and cybersecurity software development in today's digital environment. The paper examines the historical development of cybersecurity software, from basic tools to state-of-the-art frameworks, stressing the increasing complexity of cyber threats and examining breakthroughs that have shaped the industry. Through the use of case studies, it examines the critical role that threat intelligence plays in proactive cybersecurity, classifying it into strategic, operational, and tactical aspects. The study tackles the difficulties of effectively combining threat intelligence with software development, offering techniques and best practices backed by actual instances. It also looks at governance and legislative frameworks, investigates the effects of AI and machine learning on cybersecurity, and highlights the value of teamwork in building a robust global cybersecurity ecosystem. The results highlight the importance of taking a comprehensive strategy while navigating the changing cybersecurity landscape and provide insightful information to help professionals, researchers, and policymakers improve cyber resilience

Research Paper on Cyber Security Challenges and Threats

Cybersecurity is important to the computerised news industry. One of the defects in competition issues in the new planet is news security. Cybercrimes, what act the rise everyday, are mainly that springs to mind at any time we deem computerised guardianship. If skill is no safety to secure it, plans, awake files, file, and different main essential items are imperilled. Every business, either an IT firm or a hint of adjustment, needs to be protected equally. The attackers do not trail as a result of the advancement of new complicated security orders. They are utilising more brand-new and upgraded taxicab strategies to aim the feeble points of miscellaneous firms generally. Because of the tremendous amounts of files that the military, management, commercial, restorative, and friendly areas accumulate, use, and store on PCs and additional tools, computerised safety is important. Sensitive information, holding commercial dossiers, shielded features forged by original understanding, individual information, and various types of file that unauthorised approach or familiarity protect have undesirable belongings, can hold a considerable portion of earlier dossiers

UNRAVELING RANSOMWARE IN THE DIGITAL BATTLEFIELD: THREAT ANALYSIS AND COUNTERMEASURES

Ransomware is a well-known form of malware known for causing severe and permanent damage to its targets. Timely identification of such attacks is important to mitigate the consequences of these attacks. According to Data Breach Investigation Report (DBIR), since 2021, ransomware attacks have grown 17% yearly. It is widely considered a major cybersecurity threat at individual and organizational levels. There are several techniques that organizations can use to manage ransomware, such as backup, network segmentation, HR education, endpoint protection, and advanced threat hunting. It’s worth noting that only some techniques are foolproof, and a comprehensive defense strategy often involves combining multiple techniques. Ransomware has been used in the context of the Russia-Ukraine war, primarily by Russian-backed cybercriminal groups. These groups have targeted Ukrainian infrastructure and businesses with ransomware attacks, encrypting their victims’ data and demanding payment to unlock the data. These attacks have caused significant disruptions and financial losses for the targeted organizations. The paper aims to study the ransomware technique and summarize the most prominent threat actors involved in the war. We have chosen one of the well-known malwares,” HermeticRansom”, performed its thorough analysis and created a Yara rule for its detection.

The dark web: A hidden menace or a tool for privacy protection

The dark web has long been a subject of debate, as it raises questions about its covert threats and privacy safeguarding capabilities. It's an internet realm requiring specific software or authorization, notorious for illegal activities and untraceable transactions. Researchers and law enforcement agencies use the dark web for intelligence gathering on cybercriminals, making it crucial in understanding the evolution of the internet and emerging criminal activities. Additionally, studying the dark web helps identify cybersecurity threats and vulnerabilities. This research explores the relationship between the dark web and mental health, investigating the public's perception of the dark web as both a hidden menace and a tool for privacy protection. The primary aim is to examine the correlation between public perception and engagement in dark web services, while also exploring potential mental health ramifications. To collect data, we conducted a survey among university students and regular internet users. The research process involved identifying 18 research questions covering topics such as dark web knowledge, usage frequency, security measures, awareness of government regulations, ability to differentiate legal and illegal content, transaction methods, and understanding the dark web's role in promoting freedom of speech. We collected 158 responses from different departments of Chandigarh University, removing duplicates to retain 156 responses meeting our criteria. Data was compiled in an Excel file for further statistical evaluation. While the dark web is not widely accessed, users may experience psychological consequences due to its association with cybercrime, disturbing content, and radicalization. This connection leads to heightened anxiety, fear, and distress among users. Concerns about privacy, security, and associated risks further contribute to these apprehensions. Motivations for dark web access, such as curiosity and anonymity, may stem from underlying psychological factors. However, navigating illegal and potentially harmful content can result in emotional turmoil, moral dilemmas, guilt, or shame. These findings highlight the need for public awareness campaigns, educational initiatives, and targeted interventions promoting mental well-being and safe internet practices. Enhancing individuals' knowledge about the dark web's risks and potential psychological consequences empowers them to make informed decisions and protect their mental health in the digital age.