SHAPEIoT: Secure Handshake Protocol for Autonomous IoT Device Discovery and Blacklisting using Physical Unclonable Functions and Machine Learning

2021 ◽  
Author(s):  
Cem Ata Baykara ◽  
Ilgın Şafak ◽  
Kübra Kalkan
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Network Simulation ◽  
Anomalous Behavior ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Session Key ◽  
Device Discovery ◽  
Handshake Protocol ◽  
Iot Devices

This paper proposes a new lightweight handshake protocol implemented on top of the Constrained Application Protocol (CoAP) that can be used in device discovery and ensuring the IoT network security by autonomously managing devices of any computational complexity using whitelisting and blacklisting. A Physical Unclonable Function (PUF) is utilized for the session key generation in the proposed handshake protocol. The CoAP server performs real-time device discovery using the proposed handshake protocol, and anomaly detection using machinelearning algorithms to ensure the security of the IoT network. To the best of our knowledge, the presented PUF-based handshake protocol is the first to performs blacklisting and whitelisting. Whitelisted IoT devices not displaying anomalous behavior can join and remain in the IoT network. IoT devices that display anomalous behavior are autonomously blacklisted by the CoAP server and are either disallowed from joining the IoT network or are removed from the IoT network. Simulation results show that amongst the five machine learning algorithms studied, the stacking classifier displays the highest overall anomaly detection accuracy of 99.98%. Based on the results of the network simulation performed, the CoAP server is capable of blacklisting malicious IoT devices within the network with perfect accuracy.

scholarly journals A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8320
Author(s):  
Abebe Diro ◽  
Naveen Chilamkurti ◽  
Van-Doan Nguyen ◽  
Will Heyne
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Resource Constraints ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Detection Systems ◽  
Massive Number ◽  
Iot Devices ◽  
Detection Schemes

The Internet of Things (IoT) consists of a massive number of smart devices capable of data collection, storage, processing, and communication. The adoption of the IoT has brought about tremendous innovation opportunities in industries, homes, the environment, and businesses. However, the inherent vulnerabilities of the IoT have sparked concerns for wide adoption and applications. Unlike traditional information technology (I.T.) systems, the IoT environment is challenging to secure due to resource constraints, heterogeneity, and distributed nature of the smart devices. This makes it impossible to apply host-based prevention mechanisms such as anti-malware and anti-virus. These challenges and the nature of IoT applications call for a monitoring system such as anomaly detection both at device and network levels beyond the organisational boundary. This suggests an anomaly detection system is strongly positioned to secure IoT devices better than any other security mechanism. In this paper, we aim to provide an in-depth review of existing works in developing anomaly detection solutions using machine learning for protecting an IoT system. We also indicate that blockchain-based anomaly detection systems can collaboratively learn effective machine learning models to detect anomalies.

scholarly journals Log Message Anomaly Detection with Oversampling

2020 ◽  
Author(s):  
Amir Farzad ◽  
T. Aaron Gulliver
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Anomaly Detection ◽  
Learning Algorithms ◽  
Imbalanced Data ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Data Sets ◽  
Significant Challenge ◽  
Proposed Model

Imbalanced data is a significant challenge in classification with machine learning algorithms. This is particularly important with log message data as negative logs are sparse so this data is typically imbalanced. In this paper, a model to generate text log messages is proposed which employs a SeqGAN network. An Autoencoder is used for feature extraction and anomaly detection is done using a GRU network. The proposed model is evaluated with three imbalanced log data sets, namely BGL, OpenStack, and Thunderbird. Results are presented which show that appropriate oversampling and data balancing improves anomaly detection accuracy.

Anomaly Detection Using Deep Learning With Modular Networks

2019 ◽  
pp. 256-290
Author(s):  
Manu C. ◽  
Vijaya Kumar B. P. ◽  
Naresh E.
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Anomaly Detection ◽  
User Behavior ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Modular Networks ◽  
Iot Devices

In daily realistic activities, security is one of the main criteria among the different machines like IOT devices, networks. In these systems, anomaly detection is one of the issues. Anomaly detection based on user behavior is very essential to secure the machines from the unauthorized activities by anomaly user. Techniques used for an anomaly detection is to learn the daily realistic activities of the user, and later it proactively detects the anomalous situation and unusual activities. In the IOT-related systems, the detection of such anomalous situations can be fine-tuned with minor and major erroneous conditions to the machine learning algorithms that learn the activities of a user. In this chapter, neural networks, with multiple hidden layers to detect the different situation by creating an environment with random anomalous activities to the machine, are proposed. Using deep learning for anomaly detection would help in enhancing the accuracy and speed.

scholarly journals Anomaly detection and missing data imputation in building energy data for automated data pre-processing

2021 ◽  
Vol 2069 (1) ◽  
pp. 012144
Author(s):  
K Takahashi ◽  
R Ooka ◽  
S Ikeda
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Missing Data ◽  
Anomaly Detection ◽  
Prediction Accuracy ◽  
Energy Savings ◽  
Predictive Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Detection Accuracy

Abstract A new trend in building automation is the implementation of smart energy management systems to measure and control building systems without a need for decision-making by human operators. Artificial intelligence can optimize these systems by predicting future demand to make informed decisions about how to efficiently operate individual equipment. These machine learning algorithms use historical data to learn demand trends and require high quality datasets in order to make accurate predictions. But because of issues with data transmission or sensor errors, real world datasets often contain outliers or have data missing. In most research settings, these values can be simply omitted, but in practice, anomalies compromise the automation system’s prediction accuracy, rendering it unable to maximize energy savings. This study explores different machine learning algorithms for anomaly detection for automatically pre-processing incoming data using a case study on an actual electrical demand in a hospital building in Japan, namely cluster-based techniques such as k-means clustering and neural network-based approaches such as the autoencoder. Once anomalies were identified, the missing data was filled with prediction values from a deep neural network model. The newly composed data was then evaluated based on detection accuracy, prediction accuracy and training time. The proposed method of processing anomaly values allows the prediction model to process collected data without interruption, and shows similar predictive accuracy as manually processing the data. These predictions allow energy systems to optimize HVAC equipment control, increasing energy savings and reducing peak building loads.

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

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