Analyze the Network Firewall Traffic of Malware Infecting IoT Firmware in Hospitals

Опубликована: Май 13, 2024

The integration of Internet Things (IoT) devices within healthcare facilities has brought significant advancements in patient care through increased data connectivity and medical monitoring capabilities. However, the rise also exposed these systems to a greater risk cyber threats, specifically targeted malware attacks. Extensive analyses conducted on network firewall traffic from hospital IoT reveal that such as Mozi, EwDoor, Reaper, Mirai Fork, Hive, Zusy are exploiting vulnerabilities distinctively suited environment. These threats undermine not only privacy security sensitive but operational integrity services. Through robust combination machine learning models statistical analysis techniques, study identified classified behavioral patterns attack strategies variants, providing insights into their mechanisms impact operations. research highlights necessity for stringent cybersecurity measures, advocating implementation advanced encryption standards, regular system updates, comprehensive mitigate potential risks posed by threats. Furthermore, policy recommendations proposed foster culture awareness compliance institutions. outcomes this underline critical need an integrated approach settings, emphasizing both technological enhancements strategic frameworks essential safeguarding vital services against sophisticated cyber-attacks.

Язык: Английский

AI Driven Cybersecurity

Human computer interaction., Год журнала: 2024, Номер 8(1), С. 119 - 119

Опубликована: Дек. 17, 2024

The advent of Artificial Intelligence (AI) has revolutionized the field cybersecurity by introducing advanced mechanisms for detecting, preventing, and mitigating cyber threats. This research explores intersection AI cybersecurity, highlighting transformative potential AI-driven solutions in combating increasingly sophisticated cyberattacks. By leveraging machine learning, deep neural network algorithms, enhances real-time threat detection, predictive analytics, anomaly detection across diverse digital infrastructures. study evaluates current frameworks, emphasizing their efficacy handling dynamic landscapes addressing limitations traditional methods. Additionally, it examines ethical considerations, such as misuse malicious actors need transparent systems. Through comprehensive analysis, this underscores importance developing resilient models to secure critical data infrastructure an era rapidly evolving risks. findings provide actionable insights policymakers, organizations, technology developers, advocating collaborative efforts harness AI’s while its inherent challenges.

Язык: Английский