Intelligent Intrusion Detection in Low-Power IoTs

Abstract

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.

Machine Learning in IoT Security: Current Solutions and Future Challenges

“…For instance, Recurrent Neural Network (RNN) is used by Kim et al [134] to train the IDS model which is based on Long Short Term Memory (LSTM) architecture. Similarly, Saeed et al [135] used Random Neural Networks (RaNN) for the realization of efficient and fast anomaly-based intrusion detection in lowpower IoT networks. The authors proposed a two-layer model where at the first layer, normal behavior is learnt by the system and at the second layer, different kinds of Illegal Memory Access (IMA) bugs and data integrity attacks on the network are detected.…”

Comparing the Area of Data Mining Algorithms in Network Intrusion Detection

“…Subba et al [23] employed an ANN model in order to introduce an intelligent agent for classifying whether the underlying patterns of audit records are normal or abnormal while classifying them into new and unseen records. Saeed et al [24] proposed a two-level anomaly-based IDS using a Random Neural Network (RNN) model in an IoT environment. The RNN model was employed in order to build a behavior profile based on both valid and invalid system input parameters to distinguish 115 normal and abnormal patterns.…”

COLIDE: a collaborative intrusion detection framework for Internet of Things

“…A number of machine learning systems have also been proposed for detecting malicious nodes in an IoT network [38,39,40,41]. However, measuring behaviour patterns of device usage and processing it via multistage neural networks could have a high-energy consumption.…”