Malware Infection Research Articles

Synergistic Approaches to Enhance IoT Intrusion Detection: Balancing Features through Combined Learning

The Internet of Things (IoT) plays a crucial role in ensuring security by preventing unauthorized access, malware infections, and malicious activities. IoT monitors network traffic as well as device behaviour to identify potential threats and take appropriate mitigation measures. However, there is a need for an IoT Intrusion Detection system with enhanced generalization capabilities, leveraging deep learning and advanced anomaly detection techniques. This study presents an innovative approach to IoT IDS that combines SMOTE-Tomek link and BTLBO, CNN with XGB classifier which aims to address data imbalances, improve model performance, reduce misclassifications, and improve overall dataset quality. The proposed IoT IDS system, using the IoT-23 dataset, achieves 99.90% accuracy and a low error rate, all while requiring significantly less execution time. This work represents a significant step forward in IoT security, offering a robust and efficient IDS solution tailored to the changing challenges of the interconnected world.

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.

Assessing cybersecurity risks and prioritizing top strategies In Vietnam's finance and banking system using strategic decision-making models-based neutrosophic sets and Z number

The financial and banking system in Vietnam is currently confronted with a myriad of cybersecurity risks, a matter of paramount concern for the government, businesses, and individuals alike. These risks threaten the financial system's security, reliability, and integrity, potentially resulting in significant economic losses. This research endeavors to identify the prevailing cybersecurity risks in Vietnam's financial and banking system, assess their impact, and elucidate the interrelationships among these risks. Employing the Multi-Criteria Decision Making (MCDM) approach, the study integrates the DELPHI technique, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and Combined Compromise Solution (COCOSO) methods, complemented by Neutrosophic Sets (NS) and Z-number concepts to enhance the accuracy and reliability of the findings. The research findings reveal the existence of 15 cybersecurity risks in Vietnam's financial and banking system, with Malware Infections and Supply Chain Vulnerabilities emerging as the most consequential risks. Moreover, the study identifies investing in advanced threat detection systems as the optimal strategy for mitigating cybersecurity risks in Vietnam. The results underscore the importance of addressing these critical risks to safeguard the financial infrastructure, focusing on deploying robust cybersecurity measures to enhance overall system resilience.

Dynamic Malware Analysis Using Machine Learning-Based Detection Algorithms

With the increasing popularity of cell phone use, the risk of malware infections on such devices has increased, resulting in financial losses for both individuals and organizations. Current research focuses on the application of machine learning for the detection and classification of these malware programs. Accordingly, the present work uses the frequency of system calls to detect and classify malware using the XGBoost, LightGBM and random forest algorithms. The highest results were obtained with the LightGBM algorithm, achieving 94,1 % precision and 93,9 % accuracy, recall, and F1-score, demonstrating the effectiveness of both machine learning and dynamic malware analysis in mitigating security threats on mobile devices.

Analysis Of Android Malware Analysis Of Android Malware "Undangan Pernikahan.Apk" Using Static And Dynamic Methods

This research aims to analyze Android malware "UndanganPernikahan.apk" using static methods with reverse engineering techniques and dynamic analysis. The analysis process uses several tools to understand the characteristics, behavior, and impact of the malware after installation on an Android device. The research shows that this malware covertly steals user credentials through several vulnerabilities. The "UndanganPernikahan" malware is a type of spyware disguised as a wedding invitation and runs in the background without the user's knowledge. To avoid similar malware infections, it is recommended to always update the system and applications, only download applications from trusted sources, and conduct digital security training.

Anomaly Detection Integration-Framework for Network Services in Computer Education Systems

Public computer education systems provide students essential opportunities to enhance computer literacy and information skills. However, the widespread adoption of online education technology exposes the field to several critical security risks. Threats, such as malware infections, data breaches, and other network intrusions, are all challenging the security of education systems, posing potential hazards to students’ personal information and even the entire teaching environment. To spur further work into specialized anomaly detection techniques for computer education, this paper presents an anomaly detection framework tailored for network services in computer education environments to safeguard these systems. Specifically, the proposed approach learns from large-scale online educational traffic data to classify the security state into five alert levels, enabling more granular anomaly detection and analysis. To assess their detection performance, deep learning and traditional machine learning algorithms are implemented and compared for multi-class intrusion classification. The results show that the proposed framework provides an effective security solution to bolster the integrity and stability of computer education systems against evolving network threats, enhancing threat intelligence to inform proactive security by detecting and characterizing anomalies through multilevel classification.

Securing Breakneck Pace of 5G Networks Air Interfaces Through Proactive AI Monitoring

The promise of 5G networks enabling emerging technologies comes with formidable new security challenges. This paper proposes an AI-driven real-time security monitoring and incident response framework tailored to 5G infrastructure. A multi-layered architecture is presented using specialized deep learning models for radio access, edge, core, and network slice threat detection. Models including CNNs, RNNs, and transformers perform traffic analysis, signal classification, and log anomaly detection. A centralized controller aggregates model outputs into an integrated threat intelligence engine that deduces attack context and recommends mitigations. Further, a conversational bot interacts with security analysts in natural language to explain threats, suggest responses, and answer queries. The intelligent assistant is designed using dialog trees and transformer networks trained on security datasets. Evaluation on real-world 5G trial networks demonstrates 95% accuracy in classifying radio signal spoofing attacks and 98% precision in identifying malware infections. Analyst surveys confirm improved productivity and faster incident response with the AI assistant. As 5G matures, robust analytics and AI collaboration will grow increasingly critical for secure network operations. This research aims to provide both a conceptual framework and proven techniques as key enablers.

Navigating Cyber Perils in Bhutan: Challenges and Opportunities

Over a decade, Bhutan witnessed massive digital transformation in various sectors offering numerous opportunities, but at the same time, this has also brought in a considerable share of cybersecurity threats and challenges. By reviewing and analysing information related to cybersecurity from various sources, such as journals, news articles, press releases, websites, and annual reports of government agencies related to Bhutan’s cybersecurity, this study identified the prominent vulnerabilities, challenges, and opportunities, and explored the mitigation strategies. It was determined that the cybersecurity threat landscape of Bhutan is dynamic and multifaceted, ranging from online scams, phishing attacks, malware infections, vulnerabilities in systems, and a lack of security awareness among the general population. Bhutan has made noticeable efforts towards cyber threat mitigation which includes improving cybersecurity awareness at various levels, collaborating with stakeholders both at national and international levels, and implementing guiding strategies. However, there are still significant challenges being faced in terms of limited allocations of budget for infrastructure and human resource development and management. In addition, penetrating security awareness at the grassroots level is a prominent challenge. There are also opportunities to work towards maintaining and retaining a technical talent pool and implementing the national cybersecurity strategy. This article provides insights into Bhutan’s cybersecurity landscape and offers recommendations for addressing current challenges and building capabilities to combat cyber threats effectively.

Strategi Pengembangan Sistem Operasi Windows untuk Memperkuat Proteksi Windows terhadap Ancaman Malaware

Behind the widespread proliferation of malware, IT experts have developed robust security systems to minimize the risk of malware infection. In the Windows security system, numerous software solutions have been created to prevent malware threats, including Windows Defender, which serves as the core of Windows security, virus & threat protection, real-time protection, firewall & network protection, account protection, and many more. This study uses a qualitative approach based on literature review. The results indicate that by integrating machine learning technology, enhancing security features, and adopting a layered approach, the strategy for developing the Windows operating system has successfully strengthened protection against malware threats. These measures not only improve the ability to detect and respond to threats but also ensure that users can enjoy a safe and protected computing experience.

Malware Detection in Files and URL’s Using Machine Learning

The rampant extrusion of personal or sensitive data by malicious software has inflicted significant damage worldwide, posing a critical threat to individuals and various sectors of society. Cyber attackers engage in relentless warfare against computer systems, deploying malware to breach crucial data. Consequently, safeguarding this data has become a paramount concern for researchers. This paper aims to address this challenge by proposing the development of an application leveraging machine learning algorithms to discern between malicious and legitimate files effectively. In the contemporary era, marked by pervasive technological advancements, the widespread adoption of the internet has facilitated unprecedented connectivity but also heightened the risk of cyber threats perpetrated by malicious actors. These threats, often executed through the deployment of malware, have inflicted substantial financial losses, amounting to billions of dollars globally. Consequently, safeguarding against malicious attacks has become a paramount endeavor in the ongoing battle against cybercrime. To address this imperative, this paper endeavors to leverage machine learning algorithms to prognosticate the likelihood of malware infections in computer systems, utilizing a supervised learning approach alongside gradient boosting algorithms. Drawing upon a publicly available dataset, meticulously partitioned into training and testing sets, the study embarks on four distinct experiments employing the aforementioned algorithms. Notably, the findings underscore Light GBM as the most efficacious model, boasting an impressive AUC Score of 0.73926, thus signifying a pivotal advancement in fortifying cyber defenses through predictive analytics. Malware poses a significant threat in today's digital landscape, with various forms proliferating over the last decade, leading to substantial financial losses for organizations. Defined as malicious software, malware wreaks havoc on users' computers through various means. To address this challenge, a solution leveraging machine learning techniques has been proposed to detect malware in downloaded files. By analyzing features such as MD5 hash, Optional Header size, and Load Configuration Size, different machine learning algorithms are trained to distinguish between malicious and benign files effectively. Through rigorous training and comparison using Validation and Test datasets, the SVM Classifier emerged as the most accurate, achieving up to 99.99% accuracy in detecting malware types like Adware, Trojan, Backdoors, Unknown, Multidrop, Rbot, Spam, and Ransomware. This approach not only identifies malware but also mitigates potential risks to users' systems, safeguarding against detrimental impacts from infections.

Internet of Things Security Design Based on Blockchain and Identity Re-encryption

With the development of IoT technology, IoT devices have penetrated various fields, such as smart homes, industrial automation, healthcare, etc. However, the security issues of IoT devices have always troubled people. Attackers can use various means to attack IoT devices, such as stealing data, tampering with programs, malware infection, etc. These attack methods will bring great losses to users. In response to the above issues, this study uses proxy re-encryption technology to transform data collected by IoT devices into ciphertext. Then, it stores the encrypted IoT data on the blockchain. The combination of blockchain and proxy re-encryption technologies offers a method for the secure sharing of IoT data. The experimental results show that whether the algorithm encrypts a small or large amount of data, the encryption time is relatively short, and the time consumption does not exceed 15 ms. When encrypting 8196 bytes of data, its encryption time is 50% lower than the Attribute-Based Encryption (ABE) algorithm. The proposed IoT data security sharing method has the advantages of high security, fast encryption speed, and good stability, which helps to improve the security performance of the IoT and provides a new approach to IoT security.

Enhancing Security and Reliability in Industrial IoT Networks through Machine Learning

Industrial Internet of Things (IIoT) networks are very important to modern manufacturing because they allow processes to be monitored, controlled, and improved in real time. IoT systems are linked to each other, which makes them vulnerable to cyberattacks, system breakdowns, and communication problems. This makes them less reliable and secure. To deal with these problems, we need advanced technologies that can find problems before they happen, reduce risks, and keep processes running smoothly. In this paper, we suggest a new way to make IIoT networks safer and more reliable by using machine learning methods together. Our system uses machine learning techniques to look at network traffic trends, spot strange behaviors, and find possible security holes in real time, using the huge amounts of data that IIoT devices produce. Our system can successfully find and stop a wide range of cyberattacks, such as hacking attempts, malware infections, and denial-of-service (DoS) attacks, because it is always learning from past data and changing to new threats. We also use machine learning models to predict when systems might fail or perform worse, so we can do preventative maintenance and keep downtime to a minimum.To prove that our method worked, we did a lot of tests using real-world IIoT datasets and checked how well our system worked by looking at how accurate it was at finding things, how often it gave false positives, and how fast it responded. These results show that our approach based on machine learning makes IIoT networks much safer and more reliable than standard rule-based approaches. In addition, our framework is strong against new and unknown threats, which shows that it could be used in a wide range of business settings.Overall, the paper research shows that machine learning has a lot of potential to make IIoT networks more reliable and to make sure that processes in industrial settings run smoothly and safely

ECDIS Cyber Security Dynamics Analysis based on the Fuzzy-FUCOM Method

ECDIS is one of the most important pieces of navigational and information equipment on board ships, as well as a vital component of the ship's cyberspace. ECDIS has cyber vulnerabilities because of its connections to external systems like RADAR or GPS, sensors via serial (IEC61162-1/2), analogue, and digital interfaces, as well as onboard Wi-Fi, internet, and LAN technologies. This study identifies and ranks the cyber risks that cause ECDIS control loss, as well as the barriers that can be put in place to stop them, the potential consequences if they are not stopped, and the mitigations that can be utilised to avoid them. Due to a lack of historical data and research on identifying and prioritising ECDIS cyber security dynamics in the literature and the fact that this field necessitates specialised knowledge in terms of computer science and operational maritime navigation, the Fuzzy Triangular Full Consistency Method (FUCOM-F), based on expert opinion, is used in this study. Then, a bow-tie framework is employed to visualize the dynamics of ECDIS cyber security and their hierarchical classification from the analysis as a cyber-architecture. The results indicate that the primary cyber threat for ECDIS is "malware infection via the internet and intranet (M1)." The primary potential consequence, in the event that these cyber threats targeting ECDIS cannot be prevented, is the unavailability of the system (O1). The most efficient barriers against M1 attacks are “up-to-date virus protection” and “scanning software," while the most crucial measure to prevent the impact of O1 is "network segregation." Consequently, in addition to its strong methodological foundation, this research offers significant benefits to maritime professionals and cybersecurity experts by providing valuable insights on preventing cyber-attacks on bridge system infrastructure, particularly ECDIS.

Central Medical Centre Healthcare Data Security with Lightweight Blockchain Model in IoT Sensor Environment

Data security involves implementing measures to protect digital information from unauthorized access, disclosure, alteration, or destruction, ensuring the confidentiality, integrity, and availability of sensitive data. Hence, this paper addresses the critical imperative of ensuring robust data security within the healthcare domain. With the proliferation of digital technologies and interconnected systems, safeguarding patient information has become paramount. The study comprehensively reviews existing strategies for data security in healthcare, including encryption, access controls, and secure authentication mechanisms. It delves into the unique challenges faced by healthcare organizations, such as the interoperability of systems, compliance with regulatory frameworks and the increasing sophistication of cyber threats. The paper emphasizes the significance of fostering a culture of cybersecurity awareness among healthcare professionals and implementing proactive measures to mitigate vulnerabilities. Future directions in healthcare data security, including the integration of emerging technologies like blockchain and artificial intelligence, are also explored. By providing a holistic overview of data security challenges and solutions in healthcare, this paper contributes to the ongoing efforts to fortify the confidentiality, integrity, and availability of sensitive patient data in the digital age. The effectiveness of two medical treatments (Treatment A and Treatment B) is assessed based on the responses of 20 participants, revealing subtle yet notable variations in efficacy. Additionally, the security scores of Healthcare Systems A and B are evaluated, demonstrating a marginal superiority of System B. A detailed classification of cyber attacks, ranging from phishing and malware infections to insider threats and zero-day exploits, sheds light on the diverse nature of cybersecurity challenges. The findings underscore the importance of a multifaceted approach to medical data security, emphasizing the need for robust treatments, secure healthcare systems, and targeted defense strategies against evolving cyber threats. This paper contributes valuable insights to the ongoing discourse on bolstering cybersecurity measures in healthcare to safeguard patient information and fortify the integrity of healthcare systems in an increasingly digital landscape.

The Evolving Nature of Cyber Attacks and the Need for Proactive Defense Measures

IA growing and pervasive threat in today’s interconnected digital landscape. Cyber-attacks refer to malicious activities carried out by individuals or groups to compromise computer systems, networks, or data for various purposes, including financial gain, espionage, or disruption of services. The research highlights the evolving nature of cyber-attacks, with attackers constantly adapting their tactics, techniques, and procedures to exploit vulnerabilities in technology and human behavior. The analysis emphasizes the wide range of cyber-attack types, such as phishing, malware infections, ransomware attacks, distributed denial-of-service (DDoS) attacks, and advanced persistent threats (APTs). It discusses the potential impact of cyber-attacks on individuals, organizations, and even nations, including financial losses, reputational damage, compromised privacy, and disruption of critical infrastructure. Furthermore, the research acknowledges the challenges in combating cyber-attacks, including the rapid proliferation of sophisticated attack methods, the complexity of modern IT systems, and the shortage of skilled cybersecurity professionals. It emphasizes the importance of proactive defense measures, such as robust network security architectures, continuous monitoring and threat detection systems, timely patching and updates, employee education, and incident response plans. Overall, this highlights the urgency and significance of addressing cyber-attacks in today’s digital era. It underscores the need for ongoing research, collaboration, and innovation in the field of cybersecurity to stay ahead of the evolving threat landscape and safeguard the integrity, confidentiality, and availability of digital systems and data

Research on Internet Technology Innovation and Information Security Challenges in New Vehicle Energy Sector

With the rapid proliferation and advancement of new energy vehicles, Internet technology has garnered significant attention and application in this field. This study focuses on innovations in Internet technology in the domain of new automotive energy and the associated information security challenges. The innovations in Internet technology relevant to new energy vehicles, including Telematics, intelligent driving, and remote control, are initially explored. Subsequently, the information security challenges brought about by these innovations are analyzed, encompassing various aspects such as data privacy breaches, remote attacks, and malware infections. The significance of information security in the development of new energy vehicles is emphasized. Solutions and suggestions, including the strengthening of encryption technology, the establishment of a robust security framework, and the enhancement of user education and awareness, are put forward. In conclusion, the current state of research on Internet technology innovation and information security challenges is summarized, and it is suggested that future research should delve deeper into the trajectory of Internet technology development in the field of new energy vehicles, along with the attendant information security challenges, to provide theoretical and practical support for promoting the healthy development and secure application of the new energy vehicle industry.

Challenges for Use Mobile Devices, Aden Refinery Company (A.R.C) Case Study

Mobile devices such as smartphones, tablets, iPads, and laptops have become increasingly used in the workplace, bringing new cybersecurity challenges to organizations where they have been connected to the company's network. Bring Your Own Devices (BYOD) practice, which allows employees to use their personal devices for work purposes from anywhere and at any time. Although BYOD offers benefits, it also has drawbacks in the workplace. So, its adoption is a source of concern. This study's objective is to know which of the fourteen BYOD potential security concerns mentioned in the questionnaire were faced before by the IT department staff and how they ranked these BYOD potential security concerns depending on their experience from low to very high. A descriptive methodology was employed in this study. A questionnaire was used to collect data from forty people in the IT department at Aden Refinery Company. The results show that some BYOD potential security concerns have been faced by all the staff in the IT department, for instance (viruses and malware infections). Also, the results indicate that organizations need to create more awareness regarding BYOD security due to the IT department staff having faced more than one of the BYOD potential security concerns.

How to keep your information secure? Toward a better understanding of users security behavior

Use of computers and the Internet is an integral part of our lives, with business becoming more digital. As a result, individuals are using their home computers to perform diverse tasks and to store sensitive data. This paper investigates the relative efficacy of two strategies to protect home computers from security threats: security tools and security activities. For the analysis, we collected data from over 1900 individuals in Spain, following an approach combining self- reported data, via an online survey, with actual data collected directly from home users' computers. The main contribution of the paper is to provide a model, based on routine activity theory, explaining the role of security tools and security activities in protecting personal computers from malware infection, thus offering an in-depth understanding of users' security behavior. Using multivariate, logit and probit regressions, our study reveals that having security tools is positively related with higher risk activities and more infections, while pursuing security activities reduces malware infections. These results have important implications for policy makers and organizations, reinforcing the view that security tools are not sufficient to protect users from malware infection, and the need to develop security education and awareness programs for computer users.

Exploring Current Trends and Challenges in Cybersecurity: A Comprehensive Survey

Cyber security is the process of preventing unauthorized access, theft, damage, and interruption to computers, servers, networks, and data. It entails putting policies into place to guarantee the availability, confidentiality, and integrity of information and information systems. Cyber security seeks to protect against a variety of dangers, including as hacking, data breaches, malware infections, and other nefarious actions. Cyber security has grown to be a major worry as a result of the quick development of digital technology and the growing interconnection of our contemporary society. In order to gain insight into the constantly changing world of digital threats and the countermeasures put in place to address them, this survey seeks to study current trends and issues in the area of cyber security. The study includes responses from end users, business executives, IT administrators, and experts across a wide variety of businesses and sectors. The survey gives insight on important problems such the sorts of cyber threats encountered, the efficacy of current security solutions, future technology influencing cyber security, and the human elements leading to vulnerabilities via a thorough analysis of the replies. The most important conclusions include an evaluation of the most common cyber dangers, such as malware, phishing scams, ransom ware, and data breaches, as well as an investigation of the methods and tools used to counter these threats. The survey explores the significance of staff education and awareness in bolstering cyber security defenses and pinpoints opportunities for development in this area. The survey also sheds insight on how cutting-edge technologies like cloud computing, artificial intelligence, and the Internet of Things (IoT) are affecting cyber security practices. It analyses the advantages and disadvantages of using these technologies while taking into account issues like data privacy, infrastructure security, and the need for specialized skills. The survey also looks at the compliance environment, assessing how industry norms and regulatory frameworks affect cyber security procedures. The survey studies the obstacles organizations encounter in attaining compliance and assesses the degree of knowledge and commitment to these requirements. The results of this cyber security survey help to better understand the current status of cyber security and provide organizations and individual’s useful information for creating effective policies to protect digital assets. This study seeks to promote a proactive approach to cyber security, allowing stakeholders to stay ahead of threats and build a safe digital environment by identifying relevant trends and concerns.

Effects of Individual and Contextual Dimensions on an Aggregate Measure of Cybervictimization

ABSTRACT Cybervictimization is a significant challenge and increasing concern. This study explored the predictors of an aggregate measure of six different types of cybervictimization, incorporating property (online consumer fraud, online identity theft), interpersonal (cyberbullying and cyberstalking), as well as phishing and malware infection crimes. An online survey was self-administered to 726 individuals, measuring sociodemographic variables and routine activity variables, stemming from Routine Activities Theory (RAT). Of the 726 individuals, 440 were already victims of at least one cybercrime. Communicating or giving personal information to unknown individuals doubled the likelihood of being victimized online. Education increased the likelihood of victimization, while higher income was associated with a lower likelihood of online victimization. These results are discussed in relation to the RAT framework and existing empirical studies that have applied it to explain online victimization.