IFMN Model: An Advanced Classification Architecture for Intrusion
Detection with HYFSPSO Method
Abstract
Intrusion Detection Systems (IDSs) are crucial in protecting IoT
networks from unauthorized access and security threats. The ability to
detect anomalies and suspicious activities in real-time is vital for
preventing cyber-attacks and minimizing the damage caused by them.
Traditional IDSs have relied on signature-based approaches, which are
limited in their ability to detect novel and unknown attacks. To
overcome these limitations, this paper proposes a novel deep learning
architecture named as Inverted Funnelized Multilayer Network (IFMN) for
detecting intrusions in IoT networks. The proposed approach for
intrusion detection employs a feature selection model that uses a Hybrid
Yellow saddle goatfish algorithm and Particle Swarm Optimization
algorithm for Feature Selection (HYFSPSO) to identify the optimal
features. The effectiveness of the selected features is evaluated using
a decision tree (DT) classification method, ensuring only the most
informative features are used in the deep learning architecture for
intrusion detection. For analyzing and proving the effectiveness of our
scheme the current research have used three benchmark datasets i.e.
KDD-CUP99, NSL-KDD and UNSW-NB15 datasets. The simulations of the
proposed architecture are conducted in MATLAB and evaluated using
performance matrices. While comparing the outcomes on 3 datasets results
revealed that proposed HYSGPSO-DL based IDS approach is more effective
on NSL-KDD and UNSW-NB15 datasets with an accuracy of 99.96% and
99.80%, while as it achieved an accuracy of 99.53% on KDD-CUP99
dataset. Additionally, comparative analysis with existing intrusion
detection systems shows that our proposed scheme outperforms the
state-of-the-art methods.