Internet of things: a cloud-MANET communication framework

The proliferation of Internet of Things (IoT) devices has brought unprecedented convenience to our lives, but it has also opened the door to new security challenges. One of the most pressing threats in the IoT landscape is the proliferation of BotNets, which can compromise and control a multitude of devices for malicious purposes. In this paper, we propose a novel approach to address this issue: a Federated Machine Learning Solution for BotNet detection in IoT environments. Our method leverages the collective intelligence of distributed IoT devices while respecting privacy constraints, ensuring that sensitive data never leaves the device. We present a detailed methodology for federated model construction, including data collection, local model training, and secure aggregation. The resulting federated model offers improved accuracy and robustness in BotNet detection, as demonstrated through rigorous evaluation on the N-BaIoT dataset. Our findings underscore the effectiveness of this approach in enhancing IoT device security by detecting and mitigating BotNet threats while safeguarding data privacy. This paper contributes to the advancement of IoT security strategies and provides a framework for protecting IoT devices against evolving threats in a federated and privacy-preserving manner.

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.

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.

Internet of Things (IoT) devices are increasingly deployed for different purposes such as data sensing, collecting and controlling. IoT improves user experiences by allowing a large number of smart devices to connect and share information. Many existing malware attacks, targeted at traditional computers connected to the Internet, may also be directed at IoT devices. Therefore, efficient protection at IoT devices could save millions of internet users from malicious activities. However, existing malware detection approaches suffer from high computational complexity. In this study, we propose a more accurate and fast model for detecting malware in the IoT environment. We introduce a Malware Threat Hunting System (MTHS) in the proposed model. MTHS first converts malware binary into a color image and then conducts the machine or deep learning analysis for efficient malware detection. We finally prepare a baseline to compare the performance of MTHS with traditional state-of-the-art malware detection approaches. We conduct experiments on two public datasets of Windows and Android software. The experimental results indicate that the response time and the detection accuracy of MTHS are better than those of previous machine learning and deep learning approaches.

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.

In the modern security-conscious world, Deep Packet Inspection (DPI) proxies are increasingly often used on industrial and enterprise networks to perform TLS unwrapping on all outbound connections. However, enabling TLS unwrapping requires local devices to have the DPI proxy Certificate Authority certificates installed. While for conventional computing devices this is addressed via enterprise management, it's a difficult problem for Internet of Things (IoT) devices which are generally not under enterprise management, and may not even be capable of it due to their resource-constrained nature. Thus, for typical IoT devices, being installed on a network with DPI requires either manual device configuration or custom DPI proxy configuration, both of which solutions have significant shortcomings. This poses a serious challenge to the deployment of IoT devices on DPI-enabled intranets. The authors propose a solution to this problem: a method of installing on IoT devices the CA certificates for DPI proxy CAs, as well as other security configuration (security bootstrapping). The proposed solution respects the DPI policies, while allowing the commissioning of IoT and IIoT devices without the need for additional manual configuration either at device scope or at network scope. This is accomplished by performing the bootstrap operation over unsecured connection, and downloading certificates using TLS validation at application level. The resulting solution is light-weight and secure, yet does not require validation of the DPI proxy's CA certificates in order to perform the security bootstrapping, thus avoiding the chicken-and-egg problem inherent in using TLS on DPI-enabled intranets.

With the advancement of 5G mobile telecommunication, various IoT (Internet of Things) devices communicate massive amounts of data by being connected to wireless networks. Since this wireless communication is vulnerable to hackers via data leakage during communication, the transmitted data should be encrypted through block ciphers to protect the data during communication. In addition, in order to encrypt the massive amounts of data securely, it is essential to apply one of secure mode of operation. Among them, CTR (CounTeR) mode is the most widely used in industrial applications. However, these IoT devices have limited resources of computing and memory compared to typical computers, so that it is challenging to process cryptographic algorithms that have computation-intensive tasks in IoT devices at high speed. Thus, it is required that cryptographic algorithms are optimized in IoT devices. In other words, optimizing cryptographic operations on these IoT devices is not only basic but also an essential effort in order to build secure IoT-based service systems. For efficient encryption on IoT devices, even though several ARX (Add-Rotate-XOR)-based ciphers have been proposed, it still necessary to improve the performance of encryption for smooth and secure IoT services. In this article, we propose the first parallel implementations of CTR mode of ARX-based ciphers: LEA (Lightweight Encryption Algorithm), HIGHT (high security and light weight), and revised CHAM on the ARMv8 platform, a popular microcontroller in various IoT applications. For the parallel implementation, we propose an efficient data parallelism technique and register scheduling, which maximizes the usage of vector registers. Through proposed techniques, we process the maximum amount of encryption simultaneously by utilizing all vector registers. Namely, in the case of HIGHT and revised CHAM-64/128 (resp. LEA, revised CHAM-128/128, and CHAM-128/256), we can execute 48 (resp. 24) encryptions simultaneously. In addition, we optimize the process of CTR mode by pre-computing and using the intermediate value of some initial rounds by utilizing the property that the nonce part of CTR mode input is fixed during encryptions. Through the pre-computation table, CTR mode is optimized up until round 4 in LEA, round 5 in HIGHT, and round 7 in revised CHAM. With the proposed parallel processing technique, our software provides 3.09%, 5.26%, and 9.52% of improved performance in LEA, HIGHT, and revised CHAM-64/128, respectively, compared to the existing parallel works in ARM-based MCU. Furthermore, with the proposed CTR mode optimization technique, our software provides the most improved performance with 8.76%, 8.62%, and 15.87% in LEA-CTR, HIGHT-CTR, and revised CHAM-CTR, respectively. This work is the fastest implementation of CTR mode on ARMv8 architecture to the best of our knowledge.

The number of Internet of Things (IoT) devices has increased recently and will increase further in the future. IoT devices are vulnerable, so vendors update and distribute firmware. At the time of firmware distribution, a huge number of IoT devices may connect to the firmware server and excessive network traffic may occur. Consequently, a method using a peer-to-peer file sharing system and a blockchain has been proposed. One study proposed an update scheme considering incentives for distributors to help with updating. However, this method requires the IoT devices, which have limited processing power, to decrypt encrypted files, and the distribution nodes to pay the transaction cost. This paper proposes a firmware distribution method that provides incentives for distributors to help with distribution to reduce the gas costs, using a smart contract and access control based on update records. The additional computations performed by IoT devices and distributors key management were reduced compared to those in previous studies by using access control instead of encryption. Further, the proposed approach was implemented and evaluated using a Raspberry Pi and laptop. The evaluation demonstrated that the gas cost per update was successfully lowered compared to the costs in previous studies.

As location-aware IoT (Internet of Things) devices intend to spread across almost every application, IoT devices are going to require different GNSS (Global Navigation Satellite System) receiver design requirements. To deliver on IoT opportunities, system architects and designers need to look carefully at every aspect of system design and architecture to be able to enable the full potential of the IoT era. IoT devices are equipped with different connectivity sensors that can provide a reliable integrated location-awareness solution. IoT devices are mainly relying on GNSS chip to deliver a service when no other RF signal is available. Hence, GNSS chip is expected to maintain a robust and reliable performance even in harsh environment. The harsh environment includes multipath environment, NLOS (Non-Line of Sight) GNSS signal reception, weak GNSS signal power reception, superimposed motion (e.g., arm swing, floating platform fluctuation …etc.), operation under freezing temperature, and interference. The targeted low-cost system design along with the expected harsh operational environment derived us to face exceptional GNSS signal reception condition. This paper discusses lessons learned, and challenges faced signal acquisition, reacquisition, and tracking for several IoT applications. Different operational modes are also explored to accommodate various use cases scenarios for several IoT applications. These different power consumption operational modes allow the GNSS chip to achieve an Ultra-low power consumption which indeed required for IoT applications. This paper provides an overview about the addressed GNSS chip fabricated in a 28nm FD-SOI process. We demonstrate a system performance and power consumption comparison of this chip along with other state-of-the-art GNSS chip. We compare the positioning performance for this device alongside another state-of-the-art GNSS modules and Novatel’s tightly coupled GNSS/INS (Inertial Navigation System) reference solution at numerous scenarios ranging from walking, running, and driving at several sites under different environment.

ABSTRACTIn recent years, the emerging development of Internet of things (IoT) has created new opportunities for IoT service provider, device manufacturer and customer (end user). In agriculture, employing IoT can help reducing manpower as well as standardizing agricultural processes, thus increasing the benefit for customer who does agricultural business. Beside the opportunities, there are also issues while doing maintenance for an agricultural IoT system. One of them is how to automate the process to detect faults of IoT devices in a large deployment area. While previous studies focus on investigating algorithms as well as theoretical model to detect faults for specific IoT devices, automatically expanding of IoT management process for detecting faults of sensor/actuator has not been investigated yet. To achieve this, we should be able to deal with the relationship between IoT device manufacturer, service provider and customer. In this study, we conceptualize the knowledge of IoT devices, management process and fault detection requirement. To realize this model, we introduce a runtime system to expand the IoT management process for detecting sensor/actuator faults which is able to provide the above features. We do several experiments to show the effectiveness and usefulness of the proposed solution.

The proliferation of Internet of Things (IoT) devices has introduced significant challenges in cybersecurity, particularly in the realm of intrusion detection. While effective, traditional centralized machine learning approaches often compromise data privacy and scalability due to the need for data aggregation. In this study, we propose a federated learning framework for near-real-time intrusion detection in IoT environments. Federated learning enables decentralized model training across multiple devices without exchanging raw data, thereby preserving privacy and reducing communication overhead. Our approach builds upon a previously proposed hybrid model, which combines a machine learning model deployed on IoT devices with a second-level cloud-based analysis. This previous work required all data to be passed to the cloud in aggregate form, limiting security. We extend this model to incorporate federated learning, allowing for distributed training while maintaining high accuracy and privacy. We evaluate the performance of our federated-learning-based model against a traditional centralized model, focusing on accuracy retention, training efficiency, and privacy preservation. Our experiments utilize actual attack data partitioned across multiple nodes. The results demonstrate that this hybrid federated learning not only offers significant advantages in terms of data privacy and scalability but also retains the previous competitive accuracy. This paper also explores the integration of federated learning with cloud-based infrastructure, leveraging platforms such as Databricks and Google Cloud Storage. We discuss the challenges and benefits of implementing federated learning in a distributed environment, including the use of Apache Spark and MLlib for scalable model training. The results show that all the algorithms used maintain an excellent identification accuracy (98% for logistic R=regression, 97% for SVM, and 100% for Random Forest). We also report a very short training time (less than 11 s on a single machine). The previous very low application time is also confirmed (0.16 s for over 1,697,851 packets). Our findings highlight the potential of federated learning as a viable solution for enhancing cybersecurity in IoT ecosystems, paving the way for further research in privacy-preserving machine learning techniques.

The rapid proliferation of Internet of Things (IoT) devices, coupled with their inherent security vulnerabilities, has significantly expanded the attack surface, intensifying threats such as man-in-the-middle attacks, traffic hijacking, and distributed denial-of-service (DDoS) attacks, thereby posing serious risks to the security and reliability of the entire ecosystem. The network traffic associated with IoT devices is diverse and dynamic, often exhibiting complex structural features such as periodic fluctuations, varying packet sizes, and time-varying patterns that make detection challenging. Although deep learning has demonstrated strong capabilities in efficiently identifying complex and dynamic malicious traffic through powerful feature extraction and adaptive learning abilities, its high model complexity, substantial computational demands, and large parameter sizes hinder direct deployment on resource-constrained IoT devices. In order to tackle this issue, this paper proposes a malicious traffic detection framework for the Internet of Things (IoT) based on multi-teacher knowledge distillation. The proposed model, termed the Lightweight Multi-Teacher Spatiotemporal Distillation Neural Network (LMT-SDNN), employs two high-performance teacher models: Residual Inception and a One-Dimension Convolution Netural Network (1D-CNN) integrated with idirectional Long Short-Term Memory (BiLSTM), to effectively capture the complex structural features of network traffic. Furthermore, a novel Time-Related Window Loss (TRW) function is design to enhance the student’s ability to capture temporal features, thereby improving its overall performance. The effectiveness of LMT-SDNN is validated through comparisons with five baseline models on two publicly available datasets, ToN_IoT and BoT_IoT. Experimental results show that LMT-SDNN achieves a compression rate of over 99% in both model complexity and parameter count, while maintaining an accuracy exceeding 99%, indicating its strong potential for multiclass malicious traffic detection in IoT environments.

The deployment of a client–server-based distributed intelligent system involves application development in both the network domain and the device domain. In the network domain, an application server (typically in the cloud) is deployed to execute the network applications. In the device domain, several Internet of Things (IoT) devices may be configured as, for example, wireless sensor networks (WSNs), and interact with each other through the application server. Developing the network and the device applications are tedious tasks that are the major costs for building a distributed intelligent system. To resolve this issue, a low-code or no-code (LCNC) approach has been purposed to automate code generation. As traditional LCNC solutions are highly generic, they tend to generate excess code and instructions, which will lack efficiency in terms of storage and processing. Fortunately, optimization of automated code generation can be achieved for IoT by taking advantage of the IoT characteristics. An IoT-based distributed intelligent system consists of the device domain (IoT devices) and the network domain (IoT server). The software of an IoT device in the device domain consists of the Device Application (DA) and the Sensor Application (SA). Most IoT LCNC approaches provide code generation in the network domain. Very few approaches automatically generate the DA code. To our knowledge, no approach supports the SA code generation. In this paper, we propose DeviceTalk, an LCNC environment for the DA and the SA code development. DeviceTalk automatically generates the code for IoT devices to speed up the software development in the device domain for a distributed intelligent system. We propose the DeviceTalk architecture, design and implementation of the code generation mechanism for the IoT devices. Then, we show how a developer can use the DeviceTalk Graphical User Interface (GUI) to exercise LCNC development of the device software.

This dissertation proposes a solution to the growing concern about the security of Internet of Things (IoT) devices in home or business environments. Entitled MAIOS (Multi Accountability IoT Oriented Switch), the system offers an SDN (Software Defined Networking) switch architecture, which provides effective segmentation for IoT devices, increasing their protection against cyber threats. The research begins by contextualizing the exponential increase in IoT devices and the security challenges associated with them. MAIOS addresses this issue through a centralized control layer, which allows efficient management of IoT devices and the implementation of dynamic security policies, which can be applied from an SDN control plane application layer. The system was designed considering functional, non-functional requirements and deployment scenarios, either in home or business environments. To increase usability, an intuitive interface was developed for users, facilitating the configuration and monitoring of IoT devices. The solution is based on open source technologies such as pfSense proxy/firewall and Home Assistant to provide functionalities such as firewall, IoT control and real-time traffic monitoring. The effectiveness of MAIOS is evaluated through security and performance tests in simulated environments, which demonstrated its ability to identify and mitigate cyber threats, providing robust security for IoT devices. This dissertation offers a contribution to the field of IoT security, presenting a practical, affordable and effective solution to protect devices in residential and business networks. MAIOS represents an important advance in ensuring digital security in an increasingly connected world.