Oral Presentation , Page 321-326 (6)

Volume Title: 2nd IEEE International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 3-4 July. 2021

¹Youssef Farouk Sallam ; ²Hossam Eldin H. Ahmed; ³Adel Abdel-masseh Saleeb; ⁴Nirmeen A. El-Bahnasawy; ⁵Fathi E. Abd El-Samie

¹Communications and Electronics Department/ Faculty of Electronic Engineering,Menoufia University: Menouf, Egypt

²Communications and Electronics Department Faculty of Electronic Engineering,Manoufia University: Menouf, Egypt

³Communications and Electronics Department Faculty of Electronic Engineering,Menoufia University: Menouf, Egypt

⁴Computer Science and Engineering-Faculty of Electronic Engineering-Menoufia-Egypt

⁵Communications and Electronics Department Faculty of Electronic Engineering, Menoufia University: Menouf, Egypt

The Internet obviously has a major impact on the global economy and human life every day. This boundless use pushes the attack programmers to attack the data frameworks on the Internet. Web attacks influence the reliability of the Internet and its administrations. These attacks are classified as User-to-Root (U2R), Remote-to-Local (R2L), Denial-of-Service (DoS) and Probing (Prob). Subsequently, making sure about web framework security and protecting data are pivotal. The conventional layers of safeguards like antivirus scanners, firewalls and proxies, which are applied to treat the security weaknesses are insufficient. So, Intrusion Detection Systems (IDSs) are utilized to screen PC and data frameworks for security shortcomings. An IDS adds more effectiveness in securing networks against attacks. This paper presents an IDS model based on Deep Learning (DL) with Convolutional Neural Network (CNN) hypothesis. The model has been evaluated on the NSLKDD dataset. It has been trained by Kddtarin+ and tested twice, once using kddtrain+ and the other using kddtest+. The achieved test accuracies are 99.7% and 98.43% with 0.002 and 0.008 wrong alert rates for the two test scenarios, respectively.

Intrusion detection system (IDS); Deep Learning (DL); Convolutional Neural Network (CNN); NSL-KDD