الملخص الإنجليزي
Abstract:
With the recent developments in the Internet of Things (IoT), the
amount of data collected has expanded tremendously, resulting in a higher
demand for data storage, computational capacity, and real-time processing
capabilities. Cloud computing has traditionally played an important role in
establishing IoT. However, fog computing has recently emerged as a new
field complementing cloud computing due to its enhanced mobility, location
awareness, heterogeneity, scalability, low latency, and geographic distribution.
However, IoT networks are vulnerable to unwanted assaults because of their
open and shared nature. As a result, various fog computing-based security
models that protect IoT networks have been developed. A distributed architecture
based on an intrusion detection system (IDS) ensures that a dynamic,
scalable IoT environment with the ability to disperse centralized tasks to
local fog nodes and which successfully detects advanced malicious threats
is available. In this study, we examined the time-related aspects of network
traffic data. We presented an intrusion detection model based on a twolayered
bidirectional long short-term memory (Bi-LSTM) with an attention
mechanism for traffic data classification verified on the UNSW-NB15 benchmark
dataset. We showed that the suggested model outperformed numerous
leading-edge Network IDS that used machine learning models in terms of
accuracy, precision, recall and F1 score.
