Online Threats Research Articles

Exploring the impact of cyber and traditional crime victimization: Impact comparisons and explanatory factors

As cybercrime victimization rates continue to rise, our understanding of their impact, especially compared to traditional crime, remains limited. To address this knowledge gap, we conducted surveys among 910 crime victims, measuring the impact of cyber and traditional variants of property, person-centered, and sexual crime. We compared the impact of: (1) residential burglary and hacking of online bank accounts; (2) doorstep deception and bank helpdesk fraud; (3) in-person threats and online threats; and (4) sexual assault and image-based sexual abuse. Impact was categorized into peritraumatic stress, internalizing problems, externalizing problems, financial impact, and damaged self-image. Our findings reveal that cybercrime impact mostly equals or exceeds that of traditional crimes. Disparities especially emerge in terms of higher peritraumatic stress experienced by victims of cyber property crime, and more damage to self-image suffered by victims of both cyber property and cyber sexual crimes. Furthermore, we identified significant determinants of victim impact among crime characteristics, and social, personal, and demographic factors, some varying between cyber and traditional crime. Our study emphasizes the implications of the technological nature of cybercrime and the relatively active role played by cybercrime victims, while also considering the issue of victim blaming. Furthermore, we underscore the need for targeted victim support and prioritization of cybercrime.

Exploring Current Challenges and Opportunities in Media Literacy Skills for Youth: Stakeholders’ Perspectives

The widespread dissemination of contradictory information through various media platforms can increase anxiety and confusion among younger generations. This highlights the need to promote Media Literacy (ML) skills, enabling them to evaluate online information's validity critically. Video game-based learning can be valuable in this context, fostering active learning and critical thinking. Therefore, developing and sharing innovative resources like interactive content and video games is crucial to enhancing young people's ML skills and resilience to disinformation. The YO-MEDIA project, involving three universities in Italy, Portugal, and Spain and a journalist association, aims to develop ML in young people during times of crisis (e.g., pandemics and wars) through game-based learning strategies and professional training. The project carried out 29 semi-structured interviews in the partner countries with various stakeholders to understand perspectives on media design practices and ML formulation for young people in crises. These interviews aimed to collect education and media professionals' insights on promoting ML and critical thinking among youth during crises. It also seeks to gather best practices and understand these professionals' challenges when fostering ML among youth. This study's findings suggest that the interviewees deem ML vital for distinguishing accurate information from misinformation during crises, especially relevant for young individuals. Essential skills for these young people include critical thinking, filtering information, and recognizing online threats. Effective strategies involve operating within digital environments frequented by young people, encouraging critical thinking, and nurturing collaboration. Professionals face challenges such as a lack of training, engaging young people's attention, and keeping pace with digital environments. The interviewees also emphasized the importance of ML training for informal educators and media professionals. They recommended Massive Open Online Courses (MOOCs) for their flexibility and accessibility. According to the interviewees, an ML game should involve research, interpretation, and decision-making processes and be accessible to young people with disabilities. They identified several ML projects and resources, including multimedia labs, youth parliaments, and critical thinking tools. This study contributes to developing games that promote critical thinking and ML in young people. It also aids in establishing ongoing professional training in ML for informal educators, teachers, media professionals, and other stakeholders.

A Hybrid Framework for Improved Weighted Quantum Particle Swarm Optimization and Fast Mask Recurrent CNN to Enhance Phishing-URL Prediction Performance

Phishing websites are cybercrimes that aim to collect confidential data, including bank card numbers, bank accounts, and credentials. To detect phishing sites, specialists must extract the elements of the websites and utilize third-party resources. One of the drawbacks of these methods is that identifying phishing characteristics takes a lot of effort and knowledge. Second, the recognition of phishing websites is delayed when third-party services are used. A novel detecting system Improved Weighted Quantum Particle Swarm Optimisation (IWQPSO) and Fast Mask Recurrent Convolutional Neural Network (FMRCNN) proposed to strengthen and empower the technique of identifying phishing URLs. The proposed model does not require the retrieval of target website content or the use of any third-party services. Phishing attacks continue to pose significant cyber-security threats, particularly through deceptive URLs. This study proposes a novel approach to enhance phishing-URL prediction using a hybrid methodology. The method integrates an IWQPSO-FMRCNN. The IWQPSO algorithm is leveraged to optimize the weights and parameters of the FMRCNN model, enhancing its performance in distinguishing between legitimate and phishing URLs. By combining the strengths of evolutionary optimization and deep learning, the proposed approach aims to achieve higher accuracy 99.3%, precision 94.6%, F1-Score 96.7%, Recall 98.2% and AUC score 97.9% in detecting phishing URLs compared to existing methods. Experimental results demonstrate the effectiveness of the proposed hybrid method in improving phishing-URL prediction performance, providing a promising avenue for enhancing cyber-security measures against online threats.

Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and Classification

The practice of online astroturfing has become increasingly pervasive in recent years, with the growth in popularity of social media. Astroturfing consists of promoting social, political, or other agendas in a non-transparent or deceitful way, where the promoters masquerade as normative users while acting behind a mask that conceals their true identity, and at times that they are not human. In politics, astroturfing is currently considered one of the most severe online threats to democracy. The ability to automatically identify astroturfers thus constitutes a first step in eradicating this threat. We present a complete framework for handling a dataset of profiles, from data collection and efficient labeling, through feature extraction, and finally, to the identification of astroturfers lurking in the dataset. The data were collected over a period of 15 months, during which three consecutive elections were held in Israel. These raw data are unique in scope and size, consisting of several million public comments and reactions to posts on political candidates’ pages. For the manual labeling stage, we present a technique that can zoom in on a sufficiently large subset of astroturfer profiles, thus making the procedure highly efficient. The feature extraction stage consists of a temporal layer of features, which proves useful for identifying astroturfers. We then applied and compared several algorithms in the classification stage, and achieved improved results, with an F1 score of 77% and accuracy of 92%.

CPanel Server Hosting Security Against Malware and DDoS Attacks on the Open Journal System Platform

Purpose: This research analyzes the security of cPanel in protecting Open Journal Systems (O.J.S.) from DDoS attacks and malware infections. Since contemporary threats to this environment are continuously evolving, this investigation seeks to offer empirical findings and applicable suggestions for platform managers of academic publishing platforms. Methods: The method applies the scope of the literature study and tests system-specific security using cpanel features to test Imunify360, SSL Manager, IP Blocker, Site Quality Monitoring, Awstats, and Jetbackup. This is then followed by observing the server logs on cPanel, where any suspicious activities or signs of attacks can be identified. It contributes to the detection of attack patterns and weaknesses in a system. Result: This indicates several default settings within cPanel were found to be vulnerable and could allow exploitation for DDoS purposes. Tools available in cPanel helped eliminate malware and strengthen defense against DDoS attacks. This is verified by the AWStats check, which means a quicker and more secure access time from the server. Novelty: This study combines many security features and tools from cPanel to implement a complete manner of detecting and improving its security. This method will not only help find out what the weak points are but also provide actionable solutions that can be employed to secure your application. The research results offer a practical guide for system administrators to enhance cPanel security configurations. This includes applying amended security settings and using more tools to safely sweep O.J.S. from online threats.

Weaponization of Social Media – Challenges and Responses

The use of social media as a tool for manipulation poses significant challenges worldwide, affecting public sentiment, intensifying divisions, and endangering democratic systems. This article investigates these issues and examines the responses from governments, tech companies, and civil society. The swift evolution of social media has changed the way we communicate while also facilitating the rapid dissemination of false information and polarizing narratives, threatening societal unity. Researchers emphasize the impact of algorithms in creating echo chambers and filter bubbles that deepen polarization. Additionally, malicious actors take advantage of these platforms to propagate disinformation, sway elections, and incite violence. In response, initiatives such as enhanced fact-checking, media literacy programs, and better content moderation by social networks are being implemented. Regulatory actions to promote transparency and accountability are also recommended, alongside international collaboration to combat cross-border online threats. A comprehensive strategy involving technology, regulation, international co-operation and community resilience is essential to protect democratic ideals in our interconnected society.

The Importance of Cybersecurity and Protecting of Digital Assets and Understanding the Role of Cybersecurity Laws in Safeguarding Digital Assets

In the technologically advanced landscape, companies increasingly leverage cutting-edge technology to enhance operations. However, this progression also amplifies the risk of cyberthreats. Cybersecurity emerges as a pivotal element for safeguarding digital assets, encompassing networks, financial data, intellectual property (IP) and personal information. This note underscores the significance of shielding digital assets from cyberassaults, providing actionable guidance for individuals and businesses. Cybersecurity regulations play a vital role, establishing protective frameworks for sensitive data, IP and critical infrastructure. Compliance with these standards bolsters confidence, national security and global cooperation, elevating cybersecurity consciousness. Amid our digitally driven culture, prioritising cybersecurity ensures the safety and efficiency of digital assets. Implementing robust protocols fortifies individuals and organisations against online threats, facilitating secure navigation of the digital realm while upholding data integrity.

“It Became No Man’s Land”: The Burden of Moderating Online Harassment in Newswork

ABSTRACT Online harassment against media workers has been on the rise, prompting academic and international organizations to call for comprehensive measures that could protect journalists from intimidation and threats on social media platforms. Despite these efforts, research shows little success in curbing the issue of hostility towards media professionals. With this article, we aim to explore journalists’ perceptions regarding online harassment and the responsibility that they attribute to the media organizations they work for, as well as digital platforms for allowing violence to spread online. Our study is based on ethnographic fieldwork conducted in two Chilean newsrooms, including interviews with media professionals responsible for moderating hate. This article discusses the experiences of reporters who have faced online threats and attacks while reporting the news. Our findings show that digital platforms have provided new opportunities for violent behavior against journalists. More remarkably, some journalists are worried that their media organizations might prefer to benefit from the increased engagement and metrics that come with such aggressive actions. Our findings shed light on the challenges journalists face in the digital age and the need for more significant protection measures.

Cyber Threats in the Health Sector in the DRC: Risks, Opportunities, Consequences and Preventive Measures

While hackers ravage the world, social media, banking and other accounts are hacked every day, but cyber threats in DRC are not taken very seriously. There is a total negligence on the part of the country's authorities and hospital managers and human lives are in danger. one of the large hospitals of Kinshasa HJ Hospital has always been faced with multiple problems concerning its system. Once I left to get tested, I found a crowd waiting for the results because their system was blocked. It took almost a week to find a solution to this problem. But no statement has been released regarding this issue. lives are in danger; personal data is not protected and no one is complaining. We encounter many diverse cases across the country. With a country of more than 100 million inhabitants, the country cannot escape this scourge. As in many other countries, healthcare facilities in the DRC are increasingly dependent on IT systems to manage patient records, appointments, medical prescriptions and other critical aspects of their operation. However, this increased reliance on information and communications technology (ICT) also exposes healthcare organizations to various online threats, such as cyberattacks, malware, ransomware, and data theft. These threats can have serious consequences, including disruption of healthcare services, disclosure of confidential patient data and even endangering lives. To combat these threats, it is crucial that healthcare facilities in the DRC implement robust cybersecurity measures. This includes raising staff awareness of IT security practices, implementing effective firewalls and antivirus software, regularly backing up data, and training and educating users on how to recognize and report malware. suspicious online activities. Additionally, close collaboration between health authorities, regulators, healthcare providers and cybersecurity experts is essential to develop effective policies and protocols to protect healthcare infrastructure and data from cyber threats. constantly evolving. 1 Published by Maxime Gautier, December 13 2023 We decided to put together this article to be able to enlighten the entire Congolese community, more specifically health establishments, and warn them about the dangers and risks of technology.

Strategi Peningkatan Literasi Digital Masyarakat dalam Mencegah Penyebaran Hoaks di Era Digital

The community service program carried out in RT 038, Sukarami Village, aimed to enhance the digital literacy of residents to prevent the spread of hoaxes. The main issue faced was the low awareness and skills of residents in verifying digital information, leading to a high spread of hoaxes. The methods used in this program included initial surveys, program planning meetings, digital literacy education and training, as well as discussion and evaluation sessions. The results of the program showed a significant increase in residents' digital literacy, with 80% of respondents able to verify information after the program. There was a decrease in the spread of hoaxes, from 60% before the program to 20% after the program. Residents also became more critical and inclusive in receiving and disseminating information, and more vigilant against potential online threats. Thus, this program successfully built a stronger and more resilient community in facing information challenges in the digital era.

Analyzing Security Risks in Browser Extension Search Tools: A Literature Review

The literature review titled "Analyzing Security Risks in Browser Extension Search Tools" explores the growing interconnection between the digital and physical domains and the vulnerabilities and threats that accompany it. The study highlights how the Darkweb can be used for communication and anonymity, but because it is encrypted, it presents difficulties for law enforcement. Although browser extensions are essential for improving user experiences when browsing, they also offer security vulnerabilities like cross-site scripting and privilege escalation that can be used maliciously. The study emphasizes the necessity of better detection techniques for malicious extensions using Static and dynamic analysis combined, as well as the significance of advanced threat analysis and AI integration to foresee and mitigate cyber attacks.In order to improve the practical applicability of security measures, the study also urges a deeper comprehension of user behaviors and socioeconomic dynamics inside the Darkweb ecosystem. In order to successfully handle evolving cyber risks, future research topics include developing automated security testing procedures, upgrading authorship attribution algorithms for user privacy, and developing tools such as the WAP static analysis tool. Keywords: Browser add-ons, security hazards, Darkweb, online threats, sophisticated threat analysis, artificial intelligence integration, methods of detection, user conduct, and acknowledgment of authors WAP tool, cybersecurity, and automated security testing.

Stochastic Models for Cyber Attack Detection and Response: A Mathematical Approach to Intrusion Detection Systems

Cyberattacks are a big problem in today's world because everything is linked online. They can damage the security, privacy, and access of private information. Intrusion Detection Systems (IDS) are very important for keeping networks safe because they quickly find and stop harmful activity. To successfully find and stop attacks, however, we need more advanced methods because online risks are always changing. This paper suggests a new mathematical approach for improving IDS that is based on random models. Traditional ways of finding intrusions often use signature- or anomaly-based methods, which might not be able to keep up with how attackers' strategies change all the time. By using random processes, our method provides a more flexible and adaptable way to find online threats. IDS can tell the difference between normal network traffic and hostile activities because stochastic models use a statistical framework to capture the uncertainty that comes with cyberattack behaviors. We use methods from probability theory, Markov chains, and queue theory to make models of how network traffic and possible cyberattacks might behave. Our random models can find differences that could mean someone is doing something bad by looking at the statistical features of different network factors, like the rate at which packets arrive, the length of a link, and the size of the payloads. Using Markov models, the IDS can also guess how likely it is that an attack will happen in the future based on past data, which helps with strategic strategies for reducing threats. Along with monitoring, our system includes ways to respond to cyberattacks and lessen their effects. By using stochastic optimization methods, we can change how resources are used and how reactions are prioritized based on how likely and how bad the threats are to be. This flexible method makes the network infrastructure more resistant to complex attack routes, which lowers the damage and downtime that can happen during cyber events. We show that our stochastic models can improve the performance of IDS in real-world situations by analyzing them theoretically and running simulations. Adopting a scientific approach to cyber security makes it possible for stronger and smarter defenses against cyber risks that are always changing.

Securing the Web

In an era characterized by the ubiquity of the internet, the proliferation of online services, and the increasing frequency of cyber threats, the detection of look-alike domains has become a critical component of cybersecurity. The current paper presents an approach for the detection of look-alike domains, leveraging the power of open-source intelligence (OSINT) tools. It included gathering and analyzing a wide range of publicly available data sources, including permutations, WHOIS records, IP information, website content, Geo IP, similarity percentage, name server, and mail server records, and building a comprehensive profile of domains under investigation. Through the application of online search engines, patterns and features that distinguish legitimate domains from their deceptive counterparts were established. The analysis demonstrated that OSINT tools provided significant information about the sample domains and successfully detected 1598 registered look-alike domains among 10 sample domains using dnstwist, while OpenSquat identified 103 squatting domains, 960 active phishing websites, and 53 domains with suspicious certificates across five sample websites. The research contributes to the enhancement of cybersecurity practices by providing a cost-effective and scalable solution for identifying look-alike domains, which can serve as precursors to various online threats, including phishing attacks, malware distribution, and fraud.

Phishing Website Detection

Abstract: Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. The work carried out in this study is an update in the previous systematic literature surveys with more focus on the latest trends in phishing detection techniques. This study enhances readers' understanding of different types of phishing website detection techniques, the data sets used, and the comparative performance of algorithms used. Our findings show that the model based on the K means clustering is the most accurate of the other four techniques and outperforms other solutions in the literature.

Cybersecurity Considerations in Disaster Recovery Planning

Abstract: This article talks about the important link between cybersecurity and disaster recovery planning, showing how important it is to include strong security measures in disaster recovery plans. It looks at the risks and weaknesses that come up during disaster recovery, like being more open to online threats, having security controls broken, and the chance that vulnerabilities will appear again during the recovery process. Some of the most important things that the piece talks about to lower cyber risks are strong backup and recovery plans, data encryption, access controls, and authentication measures. It also stresses how important it is to have a clear plan for how to handle an event, regular training and education programs for cybersecurity, and ongoing improvement through testing and using new technologies. Case studies and examples from real life are used to show what happens when safety measures aren't up to par and how important it is to plan for all possible disasters.

Cyber Security and Digital Economy: Opportunities, Growth and Challenges

The digital age has witnessed the exponential growth of the international economy, propelled by digital technology and the deepening interconnectedness of the global landscape. However, amidst this rapid expansion lies a plethora of Cyber Security (CS) risks within the Digital Economy (DE), ranging from hacker assaults to data breaches and intellectual property theft. The heightened importance of CS resonates across organizations, nations, and individuals worldwide. This study delves into the dynamics of development and expansion within the DE while concurrently addressing the looming threats to CS. Central to its analysis is the imperative of implementing efficient CS protocols, encompassing preventive measures, detection strategies, and swift response mechanisms against online threats. Moreover, the discourse extends to strategies for enhancing CS resilience through investments in infrastructure, capacity building initiatives, and public awareness campaigns, involving both corporate entities and governmental bodies. Furthermore, this research probes into the intricate interplay between cutting-edge technologies such as blockchain, Artificial Intelligence (AI), and the Internet of Things (IoT) and their impact on CS. While these technologies hold immense potential to revolutionize the DE landscape, they concurrently introduce novel security vulnerabilities that malicious actors may exploit. In conclusion, the study underscores that while the DE offers boundless opportunities for development and innovation, robust CS measures stand as indispensable safeguards against online threats, ensuring the sustained viability and integrity of the sector in the long term.

Algorithmic Exploitation in Social Media Human Trafficking and Strategies for Regulation

Human trafficking thrives in the shadows, and the rise of social media has provided traffickers with a powerful and unregulated tool. This paper delves into how these criminals exploit online platforms to target and manipulate vulnerable populations. A thematic analysis of existing research explores the tactics used by traffickers on social media, revealing how algorithms can be manipulated to facilitate exploitation. Furthermore, the paper examines the limitations of current regulations in tackling this online threat. The research underscores the urgent need for collaboration between governments and researchers to combat algorithmic exploitation. By harnessing data analysis and machine learning, proactive strategies can be developed to disrupt trafficking networks and protect those most at risk.

Explorning the Role of Machine Learning in Enhancing Cloud Security

The use and acceptance of cloud computing are growing quickly. Numerous businesses are making investments in this area, either for their own needs or to offer services to others. The rise of the cloud has led to a number of security issues for both consumers and businesses. Machine learning is one method of cloud security (ML). ML approaches have been applied in a variety of ways to stop or identify online threats and security holes. We present a Systematic Literature Review (SLR) of cloud security and machine learning approaches and techniques in this work. After 63 pertinent papers were examined, the SLR's findings were divided into three primary study areas: I. The many forms of cloud security

AUTOMATED DETECTION OF POTENTIALLY DANGEROUS URL ADDRESSES USING THE SCIKIT-LEARN LIBRARY

The methodology of automated detection of potentially dangerous URLs using the sci-kit-learn library is considered. The proposed methodology includes data preparation, feature generation, and model evaluation based on the random forest algorithm for classifying URLs into phishing and safe ones. The methodology is implemented using the Python programming language and the scikit-learn library. Experimental results show the effectiveness of the model in identifying potentially dangerous URLs, which plays an essential role in protecting users from fraud and other online threats.

Impacting Robustness in Deep Learning-Based NIDS through Poisoning Attacks

The rapid expansion and pervasive reach of the internet in recent years have raised concerns about evolving and adaptable online threats, particularly with the extensive integration of Machine Learning (ML) systems into our daily routines. These systems are increasingly becoming targets of malicious attacks that seek to distort their functionality through the concept of poisoning. Such attacks aim to warp the intended operations of these services, deviating them from their true purpose. Poisoning renders systems susceptible to unauthorized access, enabling illicit users to masquerade as legitimate ones, compromising the integrity of smart technology-based systems like Network Intrusion Detection Systems (NIDSs). Therefore, it is necessary to continue working on studying the resilience of deep learning network systems while there are poisoning attacks, specifically interfering with the integrity of data conveyed over networks. This paper explores the resilience of deep learning (DL)—based NIDSs against untethered white-box attacks. More specifically, it introduces a designed poisoning attack technique geared especially for deep learning by adding various amounts of altered instances into training datasets at diverse rates and then investigating the attack’s influence on model performance. We observe that increasing injection rates (from 1% to 50%) and random amplified distribution have slightly affected the overall performance of the system, which is represented by accuracy (0.93) at the end of the experiments. However, the rest of the results related to the other measures, such as PPV (0.082), FPR (0.29), and MSE (0.67), indicate that the data manipulation poisoning attacks impact the deep learning model. These findings shed light on the vulnerability of DL-based NIDS under poisoning attacks, emphasizing the significance of securing such systems against these sophisticated threats, for which defense techniques should be considered. Our analysis, supported by experimental results, shows that the generated poisoned data have significantly impacted the model performance and are hard to be detected.