Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment

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.

<span lang="EN-US">With the proliferation of internet of things (IoT) devices, ensuring the security of these interconnected systems has become a critical concern. Cyberattacks targeting IoT devices pose significant threats to individuals and organizations due to the generation of vast amounts of data across many connected devices, which traditional centralized methods cannot solve. Federated learning (FL) could be a promising solution to mitigate privacy concerns associated with centralized approaches and address cybersecurity concerns. This paper uses FL and deep learning (DL) approaches to cybersecurity in IoT applications. The goal of cyber security is achieved by forming a federation of acquired and shared models at the head of the various participants. We use inception time and multi-head attention (CNN) algorithm based on FL to detect cyber-attacks and avoid data privacy leaks under two distribution modes, namely IID and Non-IID. In contrast, the FedAvg and FedMA algorithms aggregate local model updates. A global model is produced after several communication rounds between the IoT devices and the model parameter server. Cyber threats are simulated using edge-IIoT datasets. Experiment results show that the federated inception model's best global accuracy was 93, 91%, and 93, 49% using multi-head attention.</span>

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.

Investigating the learning approaches of students in nursing education

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.

Extensive internet of things (IoT) devices being in-fected by malware are an increasingly important viable objective in IoT cyberattacks e.g., botnet, virus, trojan, etc. The botnets got leverage from unsecured IoT devices (e.g., CCTV, Raspberry Pi, Arduino Uno, ESP8266, etc) that operates by using the Internet traffic. In recent year, the high-profile IoT device’s vendor and the researcher from all over universities are exploring the robustness of IoT devices against botnet attacks. This research uses a deep learning approach to prevent botnet attacks on IoT networks. The deep residual one-dimensional CNN (1DCNN) model as the proposed method is used for botnet traffic detection. Two algorithms are provided: data processing for the N-BaIoT dataset and IoT botnet detection training and testing. For data processing, training, and testing, the datasets were evaluated, and the model was optimized with different optimizers. This research used RMS Prop, ADaDelta, AdaGrad, AdaMax, and Adam as optimizers and the CNN was compared with LSTM, CNN with RNN, and Deep residual 1DCNN, respectively. The results showed that Deep Residual 1DCNN with Adam has the highest training accuracy of 88.67%, 88.67% for validation accuracy, and 88.53%for test accuracy.

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.

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.

Internet of Things (IoT) devices upload their data into the cloud for storage because of their limited resources. However, cloud storage data has been subject to potential integrity threats, and consequently auditing techniques are demanded to ensure the integrity of stored data. Unfortunately, existing auditing approaches require owners to undertake expensive tag calculations, which is unsuitable for resource-constrained IoT devices. To resolve the issue, we present a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> air <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> loud <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> uditing proposal by employing the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> lockchain (FCAB). We combine certificateless signatures with the designed dynamic structure to constructively offload the cost of tag computation from the IoT device to the introduced fog node, significantly reducing the local burden. Considering that fog nodes may behave dishonestly during auditing, FCAB enables the IoT device to verify the audit result's authenticity by extracting reliable checking records from the blockchain, thereby achieving auditing fairness, which ensures that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">honest</i> cloud and fog node will gain the corresponding reward. Finally, FCAB is proved to satisfy tag unforgeability, proof unforgeability, privacy preserving, and auditing fairness. Experiment evaluations affirm that FCAB is computationally and communicationally efficient and retains a smaller and fixed computation locally at the data processing stage (mainly including tag computation) than existing auditing methods.

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.

Fog node (FN) close to Internet of Things (IoT) devices can be exploited to offload the computing task of IoT devices. However, when lots of tasks are simultaneously offloaded from multiple IoT devices, FN can be overloaded. To mitigate this problem, we introduce a DYSON: DelaYed offloading System with Opportunistic fog Node (OFN) such as vehicles and mobile phones which can opportunistically reduce the load of static FN thanks to their mobility. In DYSON, to maximize the offloading effect of OFN, when IoT device cannot use OFN, it delays the task offloading with the expectation of future contacts of OFN. To assess the performance of DYSON, we develop an analytic model for the opportunistic offloading probability that the task can be offloaded to OFN. Based on the analytic model, we derive the optimal delay timer to maximize the opportunistic offloading probability while maintaining the probability that the task cannot be processed within the deadline below a target probability. Extensive simulation results are provided to show the superiority of DYSON.

A Review on Fog Computing: Architecture, Fog with IoT, Algorithms and Research Challenges

The widespread adoption of the Internet of Things (IoT) has raised significant concerns regarding security and privacy. Among these concerns, access control stands out as a matter of paramount importance, generating substantial interest and discourse. Centralized systems, due to their reliance on external sources, often face challenges related to scalability and availability, which can lead to performance issues. This paper introduces an efficient, decentralized, and innovative approach to implementing secure access control systems within IoT frameworks. The proposed solution leverages a multi-agent system integrated with blockchain technology. The central focus of this methodology revolves around the deployment of Blockchain Managers to ensure the security of IoT access control and facilitate secure communication between neighboring IoT devices. An added advantage of this system lies in its establishment of a secure linkage among fog nodes, cloud servers, and 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.