17 research outputs found
Malicious relay node detection with unsupervised learning in amplify-forward cooperative networks
This paper presents malicious relay node detection in a cooperative network using unsupervised learning based on the received signal samples over the source to destination (S-D) link at the destination node. We consider the situations in which possible maliciousness of the relay is the regenerative, injection or garbling type attacks over the source signal according to attack modeling in the communication. The proposed approach here for such an attack detection problem is to apply unsupervised machine learning using one-class classifier (OCC) algorithms. Among the algorithms compared, One-Class Support Vector Machines (OSVM) with kernel radial basis function (RBF) has the largest accuracy performance in detecting malicious node attacks with certain types and also detect trustable relay by using specific features of the symbol constellation of the received signal. Results show that we can achieve detection accuracy about 99% with SVM-RBF and k-NN learning algorithms for garbling type relay attacks. The results also encourage that OCC algorithms considered in this study with different feature selections could be effective in detecting other types of relay attacks
Machine Learning For In-Region Location Verification In Wireless Networks
In-region location verification (IRLV) aims at verifying whether a user is
inside a region of interest (ROI). In wireless networks, IRLV can exploit the
features of the channel between the user and a set of trusted access points. In
practice, the channel feature statistics is not available and we resort to
machine learning (ML) solutions for IRLV. We first show that solutions based on
either neural networks (NNs) or support vector machines (SVMs) and typical loss
functions are Neyman-Pearson (N-P)-optimal at learning convergence for
sufficiently complex learning machines and large training datasets . Indeed,
for finite training, ML solutions are more accurate than the N-P test based on
estimated channel statistics. Then, as estimating channel features outside the
ROI may be difficult, we consider one-class classifiers, namely auto-encoders
NNs and one-class SVMs, which however are not equivalent to the generalized
likelihood ratio test (GLRT), typically replacing the N-P test in the one-class
problem. Numerical results support the results in realistic wireless networks,
with channel models including path-loss, shadowing, and fading
Cooperative Authentication in Underwater Acoustic Sensor Networks
With the growing use of underwater acoustic communications (UWAC) for both
industrial and military operations, there is a need to ensure communication
security. A particular challenge is represented by underwater acoustic networks
(UWANs), which are often left unattended over long periods of time. Currently,
due to physical and performance limitations, UWAC packets rarely include
encryption, leaving the UWAN exposed to external attacks faking legitimate
messages. In this paper, we propose a new algorithm for message authentication
in a UWAN setting. We begin by observing that, due to the strong spatial
dependency of the underwater acoustic channel, an attacker can attempt to mimic
the channel associated with the legitimate transmitter only for a small set of
receivers, typically just for a single one. Taking this into account, our
scheme relies on trusted nodes that independently help a sink node in the
authentication process. For each incoming packet, the sink fuses beliefs
evaluated by the trusted nodes to reach an authentication decision. These
beliefs are based on estimated statistical channel parameters, chosen to be the
most sensitive to the transmitter-receiver displacement. Our simulation results
show accurate identification of an attacker's packet. We also report results
from a sea experiment demonstrating the effectiveness of our approach.Comment: Author version of paper accepted for publication in the IEEE
Transactions on Wireless Communication
Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines
This paper presents a framework for converting wireless signals into
structured datasets, which can be fed into machine learning algorithms for the
detection of active eavesdropping attacks at the physical layer. More
specifically, a wireless communication system, which consists of K legal users,
one access point (AP) and one active eavesdropper, is considered. To cope with
the eavesdropper who breaks into the system during the uplink phase, we first
build structured datasets based on several different features. We then apply
support vector machine (SVM) classifiers and one-class SVM classifiers to those
structured datasets for detecting the presence of eavesdropper. Regarding the
data, we first process received signals at the AP and then define three
different features (i.e., MEAN, RATIO and SUM) based on the post-processing
signals. Noticeably, our three defined features are formulated such that they
have relevant statistical properties. Enabling the AP to simulate the entire
process of transmission, we form the so-called artificial training data (ATD)
that is used for training SVM (or one-class SVM) models. While SVM is preferred
in the case of having perfect channel state information (CSI) of all channels,
one-class SVM is preferred in the case of having only the CSI of legal users.
We also evaluate the accuracy of the trained models in relation to the choice
of kernel functions, the choice of features, and the change of eavesdropper's
power. Numerical results show that the accuracy is relatively sensitive to
adjusting parameters. Under some settings, SVM classifiers (or even one-class
SVM) can bring about the accuracy of over 90%.Comment: All versions on this site are withdrawn because of their serious
mistakes. Moreover, the contributions of the co-authors were not considered
carefully. Two co-authors have little contributions, which cannot constitute
any main contribution. It was a mistake when the first author forgot to
update the actual authors, and he hurried to upload the incomplete and flaw
file