Towards Secure and Privacy-Preserving IoT Enabled Smart Home: Architecture and Experimental Study

Optimized security algorithm for connected vehicular network

Purpose

As the number of devices that connect to the Internet of Things (IoT) has grown, privacy and security issues have come up. Because IoT devices collect so much sensitive information, like user names, locations, phone numbers and even how they usually use energy, it is very important to protect users' privacy and security. IoT technology will be hard to use on the client side because IoT-enabled devices do not have clear privacy and security controls.

Design/methodology/approach

IoT technology would be harder to use on the client side if the IoT did not offer enough well-defined ways to protect users’ privacy and security. The goal of this research is to protect people's privacy in the IoT by using the oppositional artificial flora optimization (EGPKC-OAFA) algorithm to generate the best keys for the ElGamal public key cryptosystem (EGPKC). The EGPKC-OAFA approach puts the most weight on the IEEE 802.15.4 standard for MAC, which is the most important part of the standard. The security field is part of the MAC header of this standard. In addition, the MAC header includes EGPKC, which makes it possible to make authentication keys as quickly as possible.

Findings

With the proliferation of IoT devices, privacy and security have become major concerns in the academic world. Security and privacy are of the utmost importance due to the large amount of personally identifiable information acquired by IoT devices, such as name, location, phone numbers and energy use. Client-side deployment of IoT technologies will be hampered by the absence of well-defined privacy and security solutions afforded by the IoT. The purpose of this research is to present the EGPKC with optimum key generation using the EGPKC-OAFA algorithm for the purpose of protecting individual privacy within the context of the IoT. The EGPKC-OAFA approach is concerned with the MAC standard defined by the IEEE 802.15.4 standard, which includes the security field in its MAC header. Also, the MAC header incorporates EGPKC, which enables the fastest possible authentication key generation. In addition, the best methodology award goes to the OAFA strategy, which successfully implements the optimum EGPKC selection strategy by combining opposition-based (OBL) and standard AFA ideas. The EGPKC-OAFA method has been proved to effectively analyze performance in a number of simulations, with the results of various functions being identified.

Originality/value

In light of the growing prevalence of the IoT, an increasing number of people are becoming anxious about the protection and confidentiality of the personal data that they save online. This is especially true in light of the fact that more and more things are becoming connected to the internet. The IoT is capable of gathering personally identifiable information such as names, addresses and phone numbers, as well as the quantity of energy that is used. It will be challenging for customers to adopt IoT technology because of worries about the security and privacy of the data generated by users. In this work, the EGPKC is paired with adversarial artificial flora, which leads in an increase to the privacy security provided by EGPKC for the IoT (EGPKC-OAFA). The MAC security field that is part of the IEEE 802.15.4 standard is one of the areas that the EGPKC-OAFA protocol places a high focus on. The Authentication Key Generation Protocol Key Agreement, also known as EGPKCA, is used in MAC headers. The abbreviation for this protocol is EGPKCA. The OAFA technique, also known as the combination of OBL and AFA, is the most successful method for selecting EGPKCs. This method is recognized by its acronym, OAFA. It has been shown via a variety of simulations that the EGPKC-OAFA technique is a very useful instrument for carrying out performance analysis.

FamilyGuard: A Security Architecture for Anomaly Detection in Home Networks

The residential environment is constantly evolving technologically. With this evolution, sensors have become intelligent interconnecting home appliances, personal computers, and mobile devices. Despite the benefits of this interaction, these devices are also prone to security threats and vulnerabilities. Ensuring the security of smart homes is challenging due to the heterogeneity of applications and protocols involved in this environment. This work proposes the FamilyGuard architecture to add a new layer of security and simplify management of the home environment by detecting network traffic anomalies. Experiments are carried out to validate the main components of the architecture. An anomaly detection module is also developed by using machine learning through one-class classifiers based on the network flow. The results show that the proposed solution can offer smart home users additional and personalized security features using low-cost devices.

Remote and at-home data collection: Considerations for the NIH HEALthy Brain and Cognitive Development (HBCD) study

The NIH HEALthy Brain and Cognitive Development (HBCD) study aims to characterize the impact of in utero exposure to substances, and related environmental exposures on child neurodevelopment and health outcomes. A key focus of HBCD is opioid exposure, which has disproportionately affected rural areas. While most opioid use and neonatal abstinence syndrome has been reported outside of large cities, rural communities are often under-represented in large-scale clinical research studies that involve neuroimaging, in-person assessments, or bio-specimen collections. Thus, there exists a likely mismatch between the communities that are the focus of HBCD and those that can participate. Even geographically proximal participants, however, are likely to bias towards higher socioeconomic status given the anticipated study burden and visit frequency. Wearables, ‘nearables’, and other consumer biosensors, however, are increasingly capable of collecting continuous physiologic and environmental exposure data, facilitating remote assessment. We review the potential of these technologies for remote in situ data collection, and the ability to engage rural, affected communities. While not necessarily a replacement, these technologies offer a compelling complement to traditional ‘gold standard’ lab-based methods, with significant potential to expand the study’s reach and importance.

Eavesdropping Vulnerability and Countermeasure in Infrared Communication for IoT Devices

Infrared (IR) communication is one of the wireless communication methods mainly used to manipulate consumer electronics devices. Traditional IR devices support only simple operations such as changing TV channels. These days, consumer electronic devices such as smart TV are connected to the internet with the introduction of IoT. Thus, the user’s sensitive information such as credit card number and/or personal information could be entered with the IR remote. This situation raises a new problem. Since TV and the set-top box are visual media, these devices can be used to control and/or monitor other IoT devices at home. Therefore, personal information can be exposed to eavesdroppers. In this paper, we experimented with the IR devices’ reception sensitivity using remotes. These experiments were performed to measure the IR reception sensitivity in terms of distance and position between the device and the remote. According to our experiments, the transmission distance of the IR remote signal is more than 20 m. The experiments also revealed that curtains do not block infrared rays. Consequently, eavesdropping is possible to steal the user’s sensitive information. This paper proposes a simple, practical, and cost-effective countermeasure against eavesdropping, which does not impose any burden on users. Basically, encryption is used to prevent the eavesdropping. The encryption key is created by recycling a timer inside the microcontroller typically integrated in a remote. The key is regenerated whenever the power button on a remote is pressed, providing the limited lifecycle of the key. The evaluation indicates that the XOR-based encryption is practical and effective in terms of the processing time and cost.

Enabling Security Services in Socially Assistive Robot Scenarios for Healthcare Applications

Today’s IoT deployments are highly complex, heterogeneous and constantly changing. This poses severe security challenges such as limited end-to-end security support, lack of cross-platform cross-vertical security interoperability as well as the lack of security services that can be readily applied by security practitioners and third party developers. Overall, these require scalable, decentralized and intelligent IoT security mechanisms and services which are addressed by the SecureIoT project. This paper presents the definition, implementation and validation of a SecureIoT-enabled socially assisted robots (SAR) usage scenario. The aim of the SAR scenario is to integrate and validate the SecureIoT services in the scope of personalized healthcare and ambient assistive living (AAL) scenarios, involving the integration of two AAL platforms, namely QTrobot (QT) and CloudCare2U (CC2U). This includes risk assessment of communications security, predictive analysis of security risks, implementing access control policies to enhance the security of solution, and auditing of the solution against security, safety and privacy guidelines and regulations. Future perspectives include the extension of this security paradigm by securing the integration of healthcare platforms with IoT solutions, such as Healthentia with QTRobot, by means of a system product assurance process for cyber-security in healthcare applications, through the PANACEA toolkit.

Investigating Approaches of Integrating BIM, IoT, and Facility Management for Renovating Existing Buildings: A Review

The importance of building information is highly attached to the ability of conventional storing to provide professional analysis. The Internet of Things (IoT) and smart devices offer a vast amount of live data stored in heterogeneous repositories, and hence the need for smart methodologies to facilitate IoT–BIM integration is very crucial. The first step to better integrating IoT and Building Information Modeling (BIM) can be performed by implementing the Service-Oriented-Architecture (SOA) to combining software and other services by replacing the sematic information that was failed to display elements of indoor conditions. The other development is to create link that able to update static models towards real-time models using SOA approach. The existing approach relies on one-way interaction; however, developing two-way communication to mimic human cognitive has become very crucial. The high-tech approach requires highly involving Cloud computations to better connect IoT devices throughout Internet infrastructure. This approach is based on the integration of Building Information Modeling (BIM) with real-time data from IoT devices aiming at improving construction and operational efficiencies and to provide high-fidelity BIM models for numerous applications. The paper discusses challenges, limitations, and barriers that face BIM–IoT integration and simultaneously solves interoperability issues and Cloud computing. The paper provides a comprehensive review that explores and identifies common emerging areas of application and common design patterns of the traditional BIM-IoT integration followed by devising better methodologies to integrate IoT in BIM.