تشخیص حملات منع سرویس توزیع شده در اینترنت اشیاء با استفاده از رویکرد رأی گیری اکثریت
الموضوعات :
حبیب اله مزارعی
1
(گروه کامپیوتر، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر،ایران)
مرضیه دادور
2
(گروه کامپیوتر، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر،ایران)
محمدهادی اتابک زاده
3
(گروه ریاضی، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر،ایران)
الکلمات المفتاحية: سیستم تشخیص نفوذ, اینترنت اشیاء, رأی گیری اکثریت, یادگیری ماشین, حمله منع سرویس توزیع شده,
ملخص المقالة :
با افزایش روزافزون دستگاه های اینترنت اشیاء، امنیت آنها به موضوعی بسیار نگران کننده تبدیل شده است. اقدامات امنیتی ضعیف، مهاجمان را قادر می سازد تا دستگاه‌های اینترنت اشیاء را مورد حمله قرار دهند. یکی از این حملات، حمله منع سرویس توزیع شده است. بنابراین وجود سیستمهای تشخیص نفوذ در اینترنت اشیاء، از اهمیت ویژه ای برخوردار است. در این پژوهش، از رویکرد گروهی رأی گیری اکثریت که زیرمجموعه یادگیری ماشین است جهت تشخیص و پیش بینی حملات استفاده شده است. انگیزه استفاده از این روش، دستیابی به دقت تشخیص بهتر و نرخ مثبت کاذب بسیار پایین با ترکیب چند الگوریتم طبقه بندی یادگیری ماشین، در شبکه‌های ناهمگن اینترنت اشیاء است. در این پژوهش از مجموعه داده جدید و بهبود یافته CICDDOS2019 برای ارزیابی روش پیشنهادی استفاده شده است. نتایج شبیه‌سازی نشان می‌دهد که با اعمال روش گروهی رأی گیری اکثریت روی پنج حمله از این مجموعه داده، این روش به ترتیب به دقت تشخیص 99.9668%، 99.9670%، 100%، 99.9686% و 99.9674% در شناسایی حملات DNS، NETBIOS، LDAP، UDP و SNMP دست یافت که نسبت به مدلهای پایه، عملکرد بهتر و پایدارتری در تشخیص و پیش بینی حملات، از خود نشان داده است.
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_||_[1] J. Alsamiri and K. Alsubhi, "Internet of Things Cyber Attacks Detection using Machine Learning," International Journal of Advanced Computer Science and Applications, vol. 10, no. 12, pp. 627-634, 2019, doi: 10.14569/IJACSA.2019.0101280.
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[3] S. M. Tahsien, H. Karimipour and P. Spachos, "Machine learning based solutions for security of Internet of Things (IoT): A survey," Journal of Network and Computer Applications, vol. 161, 2020, doi: 10.1016/j.jnca.2020.102630 .
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[5] D. K. Sharma, T. Dhankhar, G. Agrawal, S. K. Singh, D. Gupta, J. Nebhen and I. Razzak, "Anomaly detection framework to prevent DDoS attack in fog empowered IoT networks," Ad Hoc Networks, vol. 121, 2021, doi: 10.1016/j.adhoc.2021.102603 .
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[10] I. Sharafaldin, A. H. Lashkari, S. Hakak and A. A. Ghorbani, "Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy," International Carnahan Conference on Security Technology (ICCST), 2019, pp. 1-8, doi: 10.1109/CCST.2019.8888419.
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.
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