3,583 research outputs found
Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT
Wireless sensor networks (WSN) are fundamental to the Internet of Things
(IoT) by bridging the gap between the physical and the cyber worlds. Anomaly
detection is a critical task in this context as it is responsible for
identifying various events of interests such as equipment faults and
undiscovered phenomena. However, this task is challenging because of the
elusive nature of anomalies and the volatility of the ambient environments. In
a resource-scarce setting like WSN, this challenge is further elevated and
weakens the suitability of many existing solutions. In this paper, for the
first time, we introduce autoencoder neural networks into WSN to solve the
anomaly detection problem. We design a two-part algorithm that resides on
sensors and the IoT cloud respectively, such that (i) anomalies can be detected
at sensors in a fully distributed manner without the need for communicating
with any other sensors or the cloud, and (ii) the relatively more
computation-intensive learning task can be handled by the cloud with a much
lower (and configurable) frequency. In addition to the minimal communication
overhead, the computational load on sensors is also very low (of polynomial
complexity) and readily affordable by most COTS sensors. Using a real WSN
indoor testbed and sensor data collected over 4 consecutive months, we
demonstrate via experiments that our proposed autoencoder-based anomaly
detection mechanism achieves high detection accuracy and low false alarm rate.
It is also able to adapt to unforeseeable and new changes in a non-stationary
environment, thanks to the unsupervised learning feature of our chosen
autoencoder neural networks.Comment: 6 pages, 7 figures, IEEE ICC 201
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Intelligent intrusion detection in low power IoTs
Security and privacy of data are one of the prime concerns in today’s Internet of Things (IoT). Conventional security techniques like signature-based detection of malware and regular updates of a signature database are not feasible solutions as they cannot secure such systems effectively, having limited resources. Programming languages permitting immediate memory accesses through pointers often result in applications having memory-related errors, which may lead to unpredictable failures and security vulnerabilities. Furthermore, energy efficient IoT devices running on batteries cannot afford the implementation of cryptography algorithms as such techniques have significant impact on the system power consumption. Therefore, in order to operate IoT in a secure manner, the system must be able to detect and prevent any kind of intrusions before the network (i.e., sensor nodes and base station) is destabilised by the attackers. In this article, we have presented an intrusion detection and prevention mechanism by implementing an intelligent security architecture using random neural networks (RNNs). The application’s source code is also instrumented at compile time in order to detect out-of-bound memory accesses. It is based on creating tags, to be coupled with each memory allocation and then placing additional tag checking instructions for each access made to the memory. To validate the feasibility of the proposed security solution, it is implemented for an existing IoT system and its functionality is practically demonstrated by successfully detecting the presence of any suspicious sensor node within the system operating range and anomalous activity in the base station with an accuracy of 97.23%. Overall, the proposed security solution has presented a minimal performance overhead.</jats:p
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