تشخیص حملات منع سرویس توزیع شده در اینترنت اشیاء با استفاده از رویکرد رأی گیری اکثریت

محورهای موضوعی : مهندسی الکترونیک

حبیب اله مزارعی ¹ , مرضیه دادور ² , محمدهادی اتابک زاده ³

1 - گروه کامپیوتر، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر،ایران
2 - گروه کامپیوتر، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر،ایران
3 - گروه ریاضی، واحد بوشهر، دانشگاه آزاد اسلامی، بوشهر،ایران

تاریخ دریافت : 1402/02/10 تاریخ پذیرش : 1402/03/25 تاریخ انتشار : 1402/07/01

کلید واژه: سیستم تشخیص نفوذ, اینترنت اشیاء, رأی گیری اکثریت, یادگیری ماشین, حمله منع سرویس توزیع شده,

چکیده مقاله :

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.

چکیده انگلیسی:

With the ever-increasing number of Internet of Things devices, their security is becoming a very worrying issue. Weak security measures enable attackers to attack IoT devices. One of these attacks is the distributed denial of service(DDOS) attack. Therefore, the existence of intrusion detection systems in the Internet of Things is of special importance. In this research, the majority voting group approach, which is a subset of machine learning, has been used to detect and predict attacks. The motivation for using this method is to achieve better detection accuracy and a very low false positive rate by combining several machine learning classification algorithms in heterogeneous Internet of Things networks. In this research, the new and improved CICDDOS2019 dataset has been used to evaluate the proposed method. The simulation results show that by applying the majority voting Ensemble method on five attacks from this data set, this method respectively has achieved accuracy of detection 99.9668%, 99.9670%, 100%, 99.9686% and 99.9674% in identifying DNS, NETBIOS, LDAP, UDP and SNMP attacks which better and more stable performance in detecting and predicting attacks have achieved than the basic models .

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