Abstract

The widespread use of interconnectivity and interoperability of computing systems have become an indispensable necessity to enhance our daily activities. Simultaneously, it opens a path to exploitable vulnerabilities that go well beyond human control capability. The vulnerabilities deem cyber-security mechanisms essential to assume communication exchange. Secure communication requires security measures to combat the threats and needs advancements to security measures that counter evolving security threats. This paper proposes the use of deep learning architectures to develop an adaptive and resilient network intrusion detection system (IDS) to detect and classify network attacks. The emphasis is how deep learning or deep neural networks (DNNs) can facilitate flexible IDS with learning capability to detect recognized and new or zero-day network behavioral features, consequently ejecting the systems intruder and reducing the risk of compromise. To demonstrate the model's effectiveness, we used the UNSW-NB15 dataset, reflecting real modern network communication behavior with synthetically generated attack activities.

Meeting Name

Complex Adaptive Systems Conference Theme: Big Data, IoT, and AI for a Smarter Future (2021: Jun. 16-18, Malvern, PA)

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Cybersecurity; Deep Learning; Intrusion Detection Systems; Zero-Day Attacks

International Standard Serial Number (ISSN)

1877-0509

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2021 The Authors, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Publication Date

18 Jun 2021

Download

Full Text Link

Share

 
COinS