Experimental research testbed for internet of things: A survey from security services perspectives

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Deep Learning Approach for Intrusion Detection System (IDS) in the Internet of Things (IoT) Network using Gated Recurrent Neural Networks (GRU)

The Internet of Things (IoT) is a complex paradigm where billions of devices are connected to a network. These connected devices form an intelligent system of systems that share the data without human-to-computer or human-to-human interaction. These systems extract meaningful data that can transform human lives, businesses, and the world in significant ways. However, the reality of IoT is prone to countless cyber-attacks in the extremely hostile environment like the internet. The recent hack of 2014 Jeep Cherokee, iStan pacemaker, and a German steel plant are a few notable security breaches. To secure an IoT system, the traditional high-end security solutions are not suitable, as IoT devices are of low storage capacity and less processing power. Moreover, the IoT devices are connected for longer time periods without human intervention. This raises a need to develop smart security solutions which are light-weight, distributed and have a high longevity of service. Rather than per-device security for numerous IoT devices, it is more feasible to implement security solutions for network data. The artificial intelligence theories like Machine Learning and Deep Learning have already proven their significance when dealing with heterogeneous data of various sizes. To substantiate this, in this research, we have applied concepts of Deep Learning and Transmission Control Protocol/Internet Protocol (TCP/IP) to build a light-weight distributed security solution with high durability for IoT network security. First, we have examined the ways of improving IoT architecture and proposed a light-weight and multi-layered design for an IoT network. Second, we have analyzed the existingapplications of Machine Learning and Deep Learning to the IoT and Cyber-Security. Third, we have evaluated deep learning\u27s Gated Recurrent Neural Networks (LSTM and GRU) on the DARPA/KDD Cup \u2799 intrusion detection data set for each layer in the designed architecture. Finally, from the evaluated metrics, we have proposed the best neural network design suitable for the IoT Intrusion Detection System. With an accuracy of 98.91% and False Alarm Rate of 0.76 %, this unique research outperformed the performance results of existing methods over the KDD Cup \u2799 dataset. For this first time in the IoT research, the concepts of Gated Recurrent Neural Networks are applied for the IoT security

A profile-driven dynamic risk assessment framework for connected and autonomous vehicles

The Internet of Things has already demonstrated clear benefits when applied in many areas. In connected and autonomous vehicles (CAV), IoT data can help the autonomous systems make better decisions for safer and more secure transportation. For example, different IoT data sources can extend CAV's risk awareness, while the incoming data can update these risks in real-time for faster reactions that may mitigate possible damages. However, the current state of the art CAV research has not addressed this matter well enough. This paper proposes a profile-driven approach to manage IoT data in the context of CAV systems through a dynamic risk management framework. Unlike the current inflexible risk assessment strategies, the framework encourages more flexible investigation of risks through different risk profiles, each representing risk knowledge through a set of risk input considerations, assessment methods and optimal reaction strategies. As the risks change frequently with time and location, there will be no single profile that can cover all the risks that CAVs face on the road. The uses of different risk profiles, therefore can help interested parties to better understand the risks and adapt to various situations appropriately. Our framework includes the effective management of IoT data sources to enable the run-time risk assessment. We also describe a case study of using the proposed framework to manage the risks for the POD being developed in the Innovate UK-funded CAPRI project

Assessing the Risk of an Adaptation using Prior Compliance Verification

Autonomous systems must respond to large amounts of streaming information. They also must comply with critical properties to maintain behavior guarantees. Compliance is especially important when a system self-adapts to perform a repair, improve performance, or modify decisions. There remain significant challenges assessing the risk of adaptations that are dynamically configured at runtime with respect to critical property compliance. Assuming compliance verification was performed for the originally deployed system, the proof process holds valuable meta-data about the variables and conditions that impact reusing the proof on the adapted system. We express this meta-data as a verification workflow using Colored Petri Nets. As dynamic adaptations are configured, the Petri Nets produce alert tokens suggesting the potential proof reuse impact of an adaptation. Alert tokens hold risk values for use in a utility function to determine the least risky adaptations. We illustrate the modeling and risk assessment using a case study

Securing Wearables through the Creation of a Personal Fog

Increased reliance on wearables using Bluetooth requires additional security and privacy measures to protect these devices and personal data, regardless of device vendor. Most wearables lack the ability to monitor their communication connections and protect personal data without assistance. Attackers can force wearables to disconnect from base stations. When a wearable loses its connection to its base station, an attacker can connect to the wearable to steal stored personal data or await reconnection to the base station to eavesdrop on communications. If the base station inadvertently disconnects from the cloud serving a security-aware app, it would be unable to respond to a rapid change in the security of its current environment. We design a personal fog incorporating wearables, a base station, and the cloud that allows the wearable to be situationally aware and manage inter- and intra-fog communications, given local personal fogs with the same app

Defining Objectives For Securing The Internet Of Things: A Value-Focused Thinking Approach

Over the past few years Internet of Things (IoT) has touched most people. Companies have been competing with each other in inventing new IoT based products and services. It has become a real business opportunity for various companies and a luxury for end users. Yet, the research on securing the Internet of Things (IoT) is in its infancy. In this study, we use the “value-focused thinking” approach to systematically identify IoT security values and objectives from 58 IT professionals. This study provides a foundation for strategically planning and thinking about IoT security. We present four fundamental objectives and thirteen means objectives. The results of this qualitative study will help researchers and practitioners identify and prioritize key IoT security issues

Automatic Intrusion Detection System Using Deep Recurrent Neural Network Paradigm

Network security field had gained research community attention in the last decade due to its growing importance. This paper addresses directly one vital problem in that field is “Intrusion Detection System” (IDS). As much as many researchers tackle this problem, many challenges arise while converting this research to reliable automatic system. The biggest challenge is to make the system works with low false alarm with new unseen threats. In this paper, we address this challenge by building a descriptive model using different models of deep Recurrent Neural Network (RNNs). (RNN) models has the ability to generalize the knowledge that can be used to identify seen and unseen threats. This generalization comes from RNN capabilities to define in its terms the normal behavior and the deviation accepted to be normal. Four different models of RNN were tested on a benchmark dataset, NSL-KDD, which is a standard test dataset for network intrusion. The proposed system showed superiority over other previously developed systems according to the standard measurements: accuracy, recall, precision and f-measure