Hệ thống phát hiện xâm nhập dựa trên bất thường cho ứng dụng IoT

Discover Internet of Things - Tập 3 - Trang 1-23 - 2023
Mansi Bhavsar1, Kaushik Roy2, John Kelly1, Odeyomi Olusola2
1Electrical & Computer Engineering, North Carolina A &T State University, Greensboro, USA
2Computer Science, North Carolina A&T State University, Greensboro, USA

Tóm tắt

Internet vạn vật (IoT) kết nối nhiều đối tượng vật lý khác nhau qua Internet và nó có ứng dụng rộng rãi, chẳng hạn như trong giao thông, quân sự, y tế, nông nghiệp và nhiều lĩnh vực khác. Những ứng dụng này ngày càng trở nên phổ biến vì chúng giải quyết các vấn đề theo thời gian thực. Ngược lại, việc sử dụng các giao thức truyền tải và giao tiếp đã đặt ra những lo ngại nghiêm trọng về an ninh cho các thiết bị IoT, và các phương pháp truyền thống như chữ ký và phương pháp dựa trên quy tắc không hiệu quả trong việc bảo vệ các thiết bị này. Do đó, việc xác định hành vi lưu lượng mạng và giảm thiểu các cuộc tấn công mạng là rất quan trọng trong IoT để đảm bảo an ninh mạng. Vì vậy, chúng tôi phát triển một Hệ thống Phát hiện Xâm nhập (IDS) dựa trên một mô hình học sâu có tên là Hệ số Tương quan Pearson - Mạng nơ-ron Tích chập (PCC-CNN) để phát hiện các bất thường trong mạng. Mô hình PCC-CNN kết hợp các đặc trưng quan trọng thu được từ các phương pháp trích xuất dựa trên tuyến tính và sau đó là Mạng nơ-ron Tích chập. Nó thực hiện phân loại nhị phân cho việc phát hiện bất thường và cũng thực hiện phân loại đa lớp cho nhiều loại tấn công khác nhau. Mô hình được đánh giá trên ba tập dữ liệu công khai: NSL-KDD, CICIDS-2017 và IOTID20. Chúng tôi trước tiên đã huấn luyện và kiểm tra năm mô hình Machine Learning dựa trên PCC khác nhau (Phân tích Hồi quy Logistic, Phân tích Phân biệt Tuyến tính, K Láng giềng Gần nhất, Cây Phân loại và Hồi quy, & Máy Vector Hỗ trợ) để đánh giá hiệu suất của mô hình. Chúng tôi đạt được độ chính xác tương tự tốt nhất từ các mô hình KNN và CART là 98%, 99% và 98%, tương ứng, trên ba tập dữ liệu. Mặt khác, chúng tôi đạt được hiệu suất hứa hẹn với độ chính xác phát hiện tốt hơn là 99,89% và tỷ lệ phân loại sai thấp là 0,001 với mô hình PCC-CNN mà chúng tôi đề xuất. Mô hình tích hợp hứa hẹn, với tỷ lệ phân loại sai (hoặc tỷ lệ báo động giả) là 0,02, 0,02 và 0,00 với các bộ phân loại phát hiện xâm nhập nhị phân và đa lớp. Cuối cùng, chúng tôi so sánh và thảo luận về mô hình PCC-CNN của chúng tôi so với năm mô hình PCC-ML truyền thống. Hệ thống IDS dựa trên Học sâu (DL) mà chúng tôi đề xuất vượt trội hơn so với các phương pháp truyền thống.

Từ khóa

#Internet-of-Things #xâm nhập mạng #phát hiện bất thường #học sâu #Hệ số Tương quan Pearson #Mạng nơ-ron Tích chập

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