Adaptable secure communication for the Cloud of Things

The Internet of Things (IoT) devices is able to communicate with another IoT devices in heterogeneous environments. They are also able to create a network by their own using wireless capabilities without a centralized approach. This network is called wireless mobile ad hoc network (MANET). The MANET facilitate IoT devices to communicate with each other in an infrastructure less network of the Internet of Things environment. The IoT devices can dynamically join, create connection and exchange information among them. The role of cloud in this framework is to store and process the data by IoT devices. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Our purpose of this study is to create a framework for providing the communication in an integrated framework of Cloud and MANET among IoT devices. Our main contribution links a new study that integrates cloud computing to the MANET and provides communication to the internet of things. This study can be used in the framework of the Internet of Things in future.

The internet of things (IoT) is now advancing at a tremendous pace. Devices in the IoT can connect and communicate in a fully autonomous mode. Because of its autonomy and numerous infractions of security policies, the IoT network is subject to various security risks. However, because of this autonomy, dependable security and storage mechanisms for authentication to share data across IoT devices are essential. Furthermore, since it has initially been popularized in Bitcoin, blockchain development has accelerated. Blockchain can be used to solve security challenges in the IoT. Making secure communication between IoT devices is one approach to accomplish this. To address this issue, we present an effective technique for intranetwork and internetwork device‐to‐device secure communication that enables IoT device identification and authentication while also providing secure communication in an open environment. The method also provides data integrity in addition to authentication and secure communication. We have presented informal security analysis, which confirms that there is no important security threats in our protocol. The performance of the protocol is also better in comparison with previously published papers.

The Internet of Things (IoT) presents itself as a promising set of technologies for providing innovative smart applications in a number of domains, spreading from agriculture to buildings and industrial control. For providing smart applications in the various domains, large numbers of devices must be deployed within large areas, communicating wirelessly with each other. These IoT devices are mainly resource constrained, with limited computing capabilities and battery life. Furthermore, it is well-known that the most energy consuming operations in wireless devices is the wireless transmission. Thus, there is a strict requirement that to prolong the lifetime of these devices, the transmissions must be controlled. Additionally, communication security is a key issue in many smart applications, because, according to recent studies, there is a lack of secure communication protocols in most existing IoT devices. This paper aims to address these two main issues in the IoT world, providing a framework for lightweight encryption and compression of data using the Compressive Sensing theory, and discusses the experiences of the implementation of the framework on real world devices. Moreover, it is also well-known that the transmissions of wireless sensors are very sensitive to packet loss due to concurrent transmissions of other wireless protocols (i.e. WiFi). This paper also presents the implementation of a framework for applying Matrix Completion techniques in real world IoT devices.

Internet of Things (IoT) is an emerging technology that consists of tiny sensors embedded with IoT devices used in e-Health, smart cities and assisted living. IoT devices are equipped with constrained power, memory, and computing capabilities, which creates new security challenges. By integrating sensors and wireless technologies, this cutting-edge technology automates numerous routine tasks in different domains. The main goal of advancing IoT in the existing healthcare system is to automate the monitoring of hospitals and patients’ health by spreading goodwill toward the IoT vision. IoT has gained popularity on practically all platforms that are available. This paper presents a remote patient health monitoring and diagnosis system that uses Message Queuing Telemetry Transport protocol (MQTT) and Internet of Things (IoT) devices with one time pad cryptography. The proposed plan offers end-to-end data secrecy for an IoT system using MQTT that enables e-health and mobility. Patients’ movements inside nursing homes and healthcare facilities are also managed safely without the need for periodic reconfigurations. Prior to communicating through the MQTT protocol, this secure crypto scheme encrypts the message on both the publisher and subscriber ends. According to the results, the proposed scheme is more efficient than delegation-based architecture in terms of security and lightweight communication. The proposed system established communication between patients and medical experts using IoT technologies to alleviate the hospital strain caused by congestion in medical treatment and enable an expressway of medical responses. According to our results the proposed scheme has low computational overhead compared to other solutions that use conventional encryption to guarantee robust protection against most of the IoT security attacks.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

The Internet of Things (IoT) is a vast network of real, tangible objects or "things" that can communicate and share data with other systems and gadgets over the Internet. A vital component of assuring the secure and dependable operation of IoT systems and devices is IoT security. Attackers may use IoT devices to get unauthorized access, change functionality, or compromise the data that the device collects and transmits. The risks of IoT security breaches grow as more devices connect and exchange sensitive data. To check the vulnerability in Iot devices, a novel Blockchain enabled Iot based Security Assessment for intrusion (Block-ISAI) technique has been proposed. Bag of Words (BoW) technique is used for feature extraction of API documents which helps to make the document simpler. Blockchain technology is utilized for secure data storage and IoT device registration. In order to detect intrusion, a deep learning architecture is designed using the verified data The attack is either detected or not detected when the vulnerability is found using the GloVe-CNN-BiLSTM Model. If the vulnerability is detected then alerts will be given. Utilizing evaluation measures like accuracy, time efficiency, precision, F1 score, detection rate, recall, false alarm rate, usability and reliability the efficacy of the suggested ISAI technique has been assessed. By the comparison analysis, the proposed ISAI technique’s detection rate is 18.22%, 19.43%, and 3.13% higher than the existing HIDS, NIDS, and ML-DDoS techniques respectively. The accuracy of the proposed system is increased by 0.69%, 6.04%, and 36.15% as compared to the HIDS, NIDS, and ML-DDoS method using UNSW-NB 15 dataset and increases by 2.37%, 18.32%, and 5.95% using KDDCUP 19 dataset respectively.

As the number of Internet of Things (IoT) devices and applications increases, the capacity of the IoT access networks is considerably stressed. This can create significant performance bottlenecks in various layers of an end-to-end communication path, including the scheduling of the spectrum, the resource requirements for processing the IoT data at the Edge and/or Cloud, and the attainable delay for critical emergency scenarios. Thus, a proper classification or prediction of the time varying traffic characteristics of the IoT devices is required. However, this classification remains at large an open challenge. Most of the existing solutions are based on machine learning techniques, which nonetheless present high computational cost, whereas they are not considering the fine-grained flow characteristics of the traffic. To this end, this paper introduces the following four contributions. Firstly, we provide an extended feature set including, flow, packet and device level features to characterize the IoT devices in the context of a smart environment. Secondly, we propose a custom weighting based preprocessing algorithm to determine the importance of the data values. Thirdly, we present insights into traffic characteristics using feature selection and correlation mechanisms. Finally, we develop a two-stage learning algorithm and we demonstrate its ability to accurately categorize the IoT devices in two different datasets. The evaluation results show that the proposed learning framework achieves 99.9% accuracy for the first dataset and 99.8% accuracy for the second. Additionally, for the first dataset we achieve a precision and recall performance of 99.6% and 99.5%, while for the second dataset the precission and recall attained is of 99.6% and 99.7% respectively. These results show that our approach clearly outperforms other well-known machine learning methods. Hence, this work provides a useful model deployed in a realistic IoT scenario, where IoT traffic and devices’ profiles are predicted and classified, while facilitating the data processing in the upper layers of an end-to-end communication model.

A review of the security vulnerabilities and countermeasures in the Internet of Things solutions: A bright future for the Blockchain

A Lightweight Forwarding Strategy for Named Data Networking in Low-end IoT

The Internet of Things (IoT) paradigm aims to enhance human society and living standards with the vast deployment of smart and autonomous devices, which requires seamless collaboration. The number of connected devices increases daily, introducing identity management requirements for edge IoT devices. Due to IoT devices' heterogeneity and resource-constrained configuration, traditional identity management systems are not feasible. As a result, identity management for IoT devices is still an open issue. Distributed Ledger Technology (DLT) and blockchain-based security solutions are becoming popular in different application domains. This paper presents a novel DLT-based distributed identity management architecture for edge IoT devices. The model can be adapted with any IoT solution for secure and trustworthy communication between devices. We have comprehensively reviewed popular consensus mechanisms used in DLT implementations and their connection to IoT research, specifically identity management for Edge IoT devices. Our proposed location-based identity management model is generic, distributed, and decentralized. The proposed model is verified using the Scyther formal verification tool for security performance measurement. SPIN model checker is employed for different state verification of our proposed model. The open-source simulation tool FobSim is used for fog and edge/user layer DTL deployment performance analysis. The results and discussion section represents how our proposed decentralized identity management solution should enhance user data privacy and secure and trustworthy communication in IoT.

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

Due to the rapid growth of the Internet of Things (IoT), researchers have demonstrated various IoT solutions, which are used to interconnect a wide range of IoT devices through the Internet. However, IoT stumbled into vertical silos; the available solutions provide specific IoT infrastructure, devices, protocols, data formats and models. This diversity and heterogeneity lead to interoperability issues. Heterogeneity happens at all IoT layers, especially at the application layer; devices often adopt mutually incompatible application-layer communication protocols to connect devices to IoT services. Furthermore, in order to integrate semantics to raw data, each system uses its one domain-specific ontology to make data more understandable and interpretable by adding semantic annotations. Working in isolation reduces the interoperability among IoT devices and systems, things across domains need to internetwork and collaborate to provide high level IoT services. Therefore, to alleviate the problem of both communication protocol interoperability and semantic interoperability across vertical silos of systems at the application layer, this paper proposes a semantic gateway (SGIoT) that acts as a bridge between heterogeneous sink nodes at the physical level and IoT services. SGIoT enables interconnectivity between communication protocols such as CoAP and MQTT regardless of their communication model, meanwhile it enables semantics integration throu gh cross-domain ontology (CDOnto) for semantic annotation, in order to provide interpretation of messages among IoT applications across domains. Our approach focuses on modularity and extensibility.

Internet of Things (IoT) which consists of heterogeneous devices is an enabling technology that can greatly improve the quality of lives in Sub-Saharan Africa. For instance, soil humidity and irrigation for e-agriculture, energy consumption, or even health data. As with technology, however, IoT has introduced security and privacy challenges. IoT devices create, transfer, process, and store sensitive data that must be protected from unauthorized access. Similarly, the devices and infrastructure linking with IoT and the IoT devices themselves are assets that must be protected. Though IoT devices are being adopted, there isn’t wide access to GPRS in rural parts of Africa; hence users need to have access to technology that is seamless, viable, and easy to use. This paper introduces a Multimodal Authentication and Authorization Model for the Security of IoT Communication via GSM Messaging (MAAMSIC). The MAAMSIC introduces a secure server that will encrypt devices’ data and the AT Commands from the IoT. The model is illustrated with a case study of a soil and moisture control system. The system will have a secure server to store data and send feedback to the user. In case of any anomalies, mitigation protocols will be executed. The system will be available for both internet capable phones and simple phones that can’t access the internet. In smartphones, a mobile app will be used to send and receive messages from the server. JSON Web Encryption strategy will be used to encrypt and decrypt messages to prevent third party apps from reading them.

Seeing is not always believing: Insights on IoT manufacturing from firmware composition analysis and vendor survey

With the advancement in science and technology, the demand for electronic Internet of Things (IoT) devices has increased to store and process digital data. With the advancement in IoT technology, we are more reliant on IoT devices to perform our regular tasks. IoT Forensics deals with the investigation of IoT devices such as smartphones, tablets, smart watches, or any device which is connected to the Internet. The data on these devices can still be retrieved via IoT forensics even if they are destroyed, resold, or misplaced. Before discarding any IoT device, we must consider how to secure the data stored on them. We are then going to discuss the several types of personal data that could be retrieved from discarded IoT devices. Finally, we are going to discuss the protection measures for the safe disposal of IoT devices.