A Review on the Mechanism Mitigating and Eliminating Internet Crimes using Modern Technologies

Khadim, Sahar; Ali Hassen, Oday; Ibrahim, Hussein

doi:10.31185/wjcm.48

Cited by 6 publications

(2 citation statements)

References 71 publications

(124 reference statements)

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“…Among the problems PaaS encounters are interoperability, privacy, authorization, host sensitivity, extensibility, and reliability. However, SaaS security issues include dependability, data security, authorization, authentication, and network monitoring [8][9][10][11][12][13]. These security issues need to be taken seriously by cloud providers.…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud Computing Security via Intelligent Intrusion Detection Mechanisms

Hamza Nasir,

Ayaz,

Nizamani

et al. 2024

ijisct

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New challenges of data breaches, unauthorized access, and insider threats have emerged with the rise of Cloud Computing. Attackers find cloud systems attractive due to their common infrastructure. To combat these security threats, strong security procedures must be integrated. An intrusion detection system (IDS) is a vital instrument for safeguarding networks and cloud environments. It tracks system activity and network traffic. This study compares the efficacy of various machine learning models for intrusion detection. The Random-Forest-Classifier model performed the best with a test accuracy of 99.88% in recognizing known and unknown threats. Furthermore, various benefits have been identified when contrasting this intelligent intrusion detection system with conventional rule-based and signature-based methods. The use of machine learning in intrusion detection systems offers enhanced security measures and improved threat detection capabilities, highlighting the importance of leveraging advanced technologies to secure cloud environments. This research illuminates the necessity to employ cutting-edge techniques to protect cloud computing systems from malicious actions in the era of cybersecurity. More than that, it stresses the necessity to invest in enrichment mechanisms in machine learning-based intrusion detection systems to stay abreast with the evolving security problems.

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Section: Literature Reviewmentioning

confidence: 99%

Cloud Computing Security via Intelligent Intrusion Detection Mechanisms

Hamza Nasir,

Ayaz,

Nizamani

et al. 2024

ijisct

View full text Add to dashboard Cite

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“…Nevertheless, browsers are not immune from vulnerabilities [116], [117]. As a result of these vulnerabilities, attackers often gain access to a victim's computer, steal PII, corrupt files, or use the hacked machine to attack others, potentially becoming a part of a botnet [118].…”

Section: ) the Cloud Layermentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a datadriven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.

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Efficient Data Collaboration Using Multi-Party Privacy Preserving Machine Learning Framework

Salam,

Abrar,

Ullah

et al. 2023

IEEE Access

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In a modern era where data-driven insights are the foundation of technological advancements preserving the privacy and security of sensitive information while harnessing the collective intelligence of multiple parties is imperative. This research presents a Secure Collaborative Learning Algorithm (SCLA) designed to facilitate efficient multi-party machine learning without compromising data privacy. Our research focus is on leveraging existing, secure databases without requiring an additional data collection process. SCLA seamlessly integrates homomorphic encryption and Federated Learning (FL) to enable secure data collaboration among various stakeholders. The proposed algorithm aggregates model updates in a privacy-preserving manner, demonstrating enhanced model accuracy, competitive convergence speed, and robust scalability. By carefully balancing privacy preservation and learning efficiency, the SCLA showcases a promising avenue for privacy-focused collaborative learning using existing data repositories.

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A Review on the Mechanism Mitigating and Eliminating Internet Crimes using Modern Technologies

Cited by 6 publications

References 71 publications

Cloud Computing Security via Intelligent Intrusion Detection Mechanisms

Cloud Computing Security via Intelligent Intrusion Detection Mechanisms

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Efficient Data Collaboration Using Multi-Party Privacy Preserving Machine Learning Framework

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