1,725 research outputs found
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks
Intrusion detection has become one of the most critical tasks in a wireless
network to prevent service outages that can take long to fix. The sheer variety
of anomalous events necessitates adopting cognitive anomaly detection methods
instead of the traditional signature-based detection techniques. This paper
proposes an anomaly detection methodology for wireless systems that is based on
monitoring and analyzing radio frequency (RF) spectrum activities. Our
detection technique leverages an existing solution for the video prediction
problem, and uses it on image sequences generated from monitoring the wireless
spectrum. The deep predictive coding network is trained with images
corresponding to the normal behavior of the system, and whenever there is an
anomaly, its detection is triggered by the deviation between the actual and
predicted behavior. For our analysis, we use the images generated from the
time-frequency spectrograms and spectral correlation functions of the received
RF signal. We test our technique on a dataset which contains anomalies such as
jamming, chirping of transmitters, spectrum hijacking, and node failure, and
evaluate its performance using standard classifier metrics: detection ratio,
and false alarm rate. Simulation results demonstrate that the proposed
methodology effectively detects many unforeseen anomalous events in real time.
We discuss the applications, which encompass industrial IoT, autonomous vehicle
control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1
Road Context-aware Intrusion Detection System for Autonomous Cars
Security is of primary importance to vehicles. The viability of performing
remote intrusions onto the in-vehicle network has been manifested. In regard to
unmanned autonomous cars, limited work has been done to detect intrusions for
them while existing intrusion detection systems (IDSs) embrace limitations
against strong adversaries. In this paper, we consider the very nature of
autonomous car and leverage the road context to build a novel IDS, named Road
context-aware IDS (RAIDS). When a computer-controlled car is driving through
continuous roads, road contexts and genuine frames transmitted on the car's
in-vehicle network should resemble a regular and intelligible pattern. RAIDS
hence employs a lightweight machine learning model to extract road contexts
from sensory information (e.g., camera images and distance sensor values) that
are used to generate control signals for maneuvering the car. With such ongoing
road context, RAIDS validates corresponding frames observed on the in-vehicle
network. Anomalous frames that substantially deviate from road context will be
discerned as intrusions. We have implemented a prototype of RAIDS with neural
networks, and conducted experiments on a Raspberry Pi with extensive datasets
and meaningful intrusion cases. Evaluations show that RAIDS significantly
outperforms state-of-the-art IDS without using road context by up to 99.9%
accuracy and short response time.Comment: This manuscript presents an intrusion detection system that makes use
of road context for autonomous car
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection
With the development of intelligent transportation systems, vehicles are
exposed to a complex network environment. As the main network of in-vehicle
networks, the controller area network (CAN) has many potential security
hazards, resulting in higher requirements for intrusion detection systems to
ensure safety. Among intrusion detection technologies, methods based on deep
learning work best without prior expert knowledge. However, they all have a
large model size and rely on cloud computing, and are therefore not suitable to
be installed on the in-vehicle network. Therefore, we propose a lightweight
parallel neural network structure, LiPar, to allocate task loads to multiple
electronic control units (ECU). The LiPar model consists of multi-dimensional
branch convolution networks, spatial and temporal feature fusion learning, and
a resource adaptation algorithm. Through experiments, we prove that LiPar has
great detection performance, running efficiency, and lightweight model size,
which can be well adapted to the in-vehicle environment practically and protect
the in-vehicle CAN bus security.Comment: 13 pages, 13 figures, 6 tables, 51 referenc
Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network
Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication. Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learning-based dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era
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