2 research outputs found
Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks
In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a
promising future architecture in terms of minimizing energy consumption and
allocating resources efficiently by providing real-time cloud infrastructures,
cooperative radio, and centralized data processing. Recently, given their
vulnerability to malicious attacks, the security of C-RAN networks has
attracted significant attention. Among various anomaly-based intrusion
detection techniques, the most promising one is the machine learning-based
intrusion detection as it learns without human assistance and adjusts actions
accordingly. In this direction, many solutions have been proposed, but they
show either low accuracy in terms of attack classification or they offer just a
single layer of attack detection. This research focuses on deploying a
multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN
that can detect and classify four types of jamming attacks: constant jamming,
random jamming, deceptive jamming, and reactive jamming. This deployment
enhances security by minimizing the false negatives in C-RAN architectures. The
experimental evaluation of the proposed solution is carried out using WSN-DS
(Wireless Sensor Networks DataSet), which is a dedicated wireless dataset for
intrusion detection. The final classification accuracy of attacks is 94.51\%
with a 7.84\% false negative rate.Comment: 6 pages, 6 figures, conferenc
Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing
Key components of current cybersecurity methods are the Intrusion Detection
Systems (IDSs) were different techniques and architectures are applied to
detect intrusions. IDSs can be based either on cross-checking monitored events
with a database of known intrusion experiences, known as signature-based, or on
learning the normal behavior of the system and reporting whether some anomalous
events occur, named anomaly-based. This work is dedicated to the application to
the Internet of Things (IoT) network where edge computing is used to support
the IDS implementation. New challenges that arise when deploying an IDS in an
edge scenario are identified and remedies are proposed. We focus on
anomaly-based IDSs, showing the main techniques that can be leveraged to detect
anomalies and we present machine learning techniques and their application in
the context of an IDS, describing the expected advantages and disadvantages
that a specific technique could cause.Comment: Paper submitted for publication in the IEEE Communications Surveys &
Tutorial