Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2024.2372749
An adaptive multistage intrusion detection and prevention system in software defined networking environment
N Maheswaran
; Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
*
S Bose
; Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India
Buvaneswari Natarajan
; Middlesex College, Edison, NJ, USA
* Dopisni autor.
Sažetak
The advancements made in Software-Defined Networking (SDN) technology seem quite promising, with potential wide application in managing and controlling the latest network infrastructures. SDN technology decouples the control plane from the data plane, enabling effective and
flexible network management. However, this dynamic phenomenon brings new security challenges. With the increasing dynamism and programmable nature of networks, conventional
security protocols may not sufficient to protect against advanced and sophisticated attacks.
Although Intrusion Detection Systems (IDSs) have been extensively applied for identifying and
preventing security threats in traditional network environments, IDS models designed specifically for traditional network requirements may not be adequate for SDN environments. These
issues may stem from the static nature of conventional networks, contrasting with the dynamicity of advanced SDN networks, and the traditional IDS’s inability to adapt to the dynamic nature
of SDN. To address these challenges, the current research proposes a novel Deep Hybrid IDS
model to enhance network security in SDN environments and prevent attacks using Scapy. The
proposed model detects signature-based attacks by integrating Gated Recurrent Units (GRU)
and Long Short-Term Memory (LSTM) for real-time simulated datasets, achieving an accuracy
of 97.8%, which is comparatively better than existing models.
Ključne riječi
Software-defined networking; deep one-class Intrusion Detection System; open network operating system; Canadian institute for Cyber security Flow meter; Scapy
Hrčak ID:
326332
URI
Datum izdavanja:
11.7.2024.
Posjeta: 0 *