Performance Evaluation of Machine Learning Based Channel Selection Algorithm Implemented on IoT Sensor Devices in Coexisting IoT Networks

This paper addresses the optimization problems in far-field wireless power transfer systems using Reinforcement Learning techniques. The mobile Radio-Frequency(RF) wireless transmitter can charge the nearby harvested energy enabled Internet of Things(IoT) devices. The wireless transmitter intends to find the optimal path to charge all stationary IoT devices in the shortest time. The Reinforcement Learning is applied to determine both the order of the IoT devices to be charged and the shortest path for the robot to drive from one IoT device to another. Due to the high complexity of the task and the charging environment, the traditional Reinforcement Learning even cannot converge. In order to solve this problem, a Finite State Machine Reinforcement Learning is proposed. At first, a heuristic algorithm is utilized to calculate the optimal path between any two IoT devices. Next, using the idea of the Finite State Machine in designing sequential circuit, the proposed algorithm determines the order of the IoT devices to be charged by setting each IoT device as a particular state. As a result, both the order of the IoT devices and the path from one device to another can be figured out. The numerical results prove the superiority of the Finite State Machine Reinforcement Learning in solving the proposed optimization problems. The real wireless power transfer system is also accomplished in our experimental field.

With the advent of advanced technological developments such as IoT, edge, and fog computing, cyber attacks have become increasingly sophisticated. IoT networks facilitate collaborative and intelligent tasks across various domains, including Industry 4.0, digital healthcare, and home automation. However, the proliferation of IoT devices has raised concerns about severe attacks, particularly those targeting resource constraints such as energy and memory. In response to these challenges, Tiny Machine Learning (TinyML) has emerged as a new research area, focusing on machine learning techniques tailored for embedded and IoT systems. This study proposes an ML detection mechanism designed to categorize and detect resource-constrained attacks in IoT devices. We consider IoT devices to be integral components within the continuum of edge and cloud computing, leveraging EdgeML and CloudML for detection purposes. Our paper conducts a comparative analysis of ML models, with a specific focus on energy consumption and memory usage in IoT applications. We compare various ML methodologies, including cloud-based, edge-based, and device-based strategies for both training and detection. The evaluation encompasses the application of these ML techniques to petite IoT devices, utilizing TinyML, as well as cloud and edge devices. Our findings reveal that the Decision Tree algorithm deployed on smart devices surpasses other approaches in terms of training efficiency, resource utilization, and the ability to detect resource-constrained attacks on IoT devices. We demonstrate a high level of accuracy, exceeding 96.9%, across all presented ML models in detecting resource constraint attacks within IoT systems. In summary, this research serves as a guide for implementing effective security measures in the dynamic landscape of IoT and associated technologies.

This paper presents a proof of concept for selfpowered Internet of Things (IoT) device, which is maintenance free and completely self-sustainable through energy harvesting. These IoT devices can be deployed in large scale and placed anywhere as long as they are in range of a gateway, and as long as there is sufficient light levels for the solar panel, such as indoor lights. A complete IoT device is designed, prototyped and tested. The IoT device can potentially last for more than 5 months (transmission interval of 30 seconds) on the coin cell battery (capacity of 120mAh) without any energy harvesting, sufficiently long for the dark seasons of the year. The sensor node contains ultra-low power sensors for temperature, humidity and light levels, with the possibility of adding several more sensors.

We examine data-intensive real-time applications, such as forest fire detection, medical emergency services, oil pipeline monitoring, etc., that require relatively low response time in processing data from the Internet of Things (IoT) devices. Typically, in such circumstances, the edge computing paradigm is utilised to drastically reduce the processing delay of such applications. However, with the growing IoT devices, the edge device cluster needs to be configured properly such that the real-time requirements are met. Therefore, the cluster configuration must be dynamically adapted to the changing network topology of the edge cluster in order to minimise the observed overall communication delay incurred by edge devices when processing data from IoT devices. To this end, we propose an intelligent assignment of IoT devices to edge devices based on Reinforcement Learning such that communication delay is minimised and none of the edge devices is overloaded. We demonstrate, with some preliminary results, that our algorithm outperforms the state-of-the-art.

Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy harvesting (EH) to provide high-level experiences for computational intensive applications and concurrently to prolong the lifetime of the battery. In this paper, we propose a reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device, and the predicted amount of the harvested energy. This scheme enables the IoT device to optimize the offloading policy without knowledge of the MEC model, the energy consumption model, and the computation latency model. Further, we present a deep RL-based offloading scheme to further accelerate the learning speed. Their performance bounds in terms of the energy consumption, computation latency, and utility are provided for three typical offloading scenarios and verified via simulations for an IoT device that uses wireless power transfer for energy harvesting. Simulation results show that the proposed RL-based offloading scheme reduces the energy consumption, computation latency, and task drop rate, and thus increases the utility of the IoT device in the dynamic MEC in comparison with the benchmark offloading schemes.

DDoS attacks often happen in cloud servers and cause a devastating problem. However, an increasing number of Internet of Things (IoT) devices makes us not ignore the influence of large-scale DDoS attacks from IoT devices. In this paper, we propose a machine learning-based on a multi-layer IoT DDoS attack detection system, including IoT devices, IoT gateways, SDN switches, and cloud servers. Firstly, we build eight smart poles with various sensors on our campus and collect sensor data as our datasets through wireless networks or wired networks. Next, we extract the features based on DDoS attack types. The feature selection can result in high accuracy DDoS attack detection in the real IoT environment. The experimental results show that our multi-layer DDoS detection system can accurately detect DDoS attacks. And the SDN controller can block venomous devices effectively according to blacklists from the results of our IoT DDoS attacks detection system.

Asymmetric cryptography algorithms, still considered the most robust tool available in the cryptography domain. It incorporates intensive modular exponentiation calculations, which entail considerable computing power, memory and storage space. Common IoT device is equipped with limited computing resources and so, for security purposes, it can execute symmetric and limited asymmetric cryptography. In most cases, IoT devices do not have the capacity required for executing massive modular multiplications of numbers of magnitude of 4 K bits and more. This leads to the lack of asymmetric cryptography in the IoT domain resulting with a reduction in security means to cope with the raising security challenges. The security problem increases as the number of IoT devices is expected to significantly grow soon. We propose a new implementation of asymmetric cryptography, which splits its heavy calculations into micro-processes, where each micro-process is distributed to an appropriate IoT device, connected to the network. The accepted result is transferred back to the distributing IoT. Once all the results are accumulated, a consolidation process is activated to generate the final calculation result, which then is fed into the cryptography process, to generate the Encrypted/Decrypted string, proving the ability to maintain asymmetric cryptography also at the IoT level without compromising security.

The Internet of Things models are vulnerable is still wreaking havoc on commercial and critical sectors. The heterogeneity of different devices in IoT and their broad expansion has resulted in the emergence of numerous threats for security and privacy, severely impeding the collection, analysis, and correlation of IoT-centric data. With the fast growth of threats and the diversity of attack, IoT systems face significant hurdles in detecting security flaws and attacks. With the increase in the number of IoT devices, the volume of traffic generated by IoT-based assaults has been steadily increasing. One of the most serious IoT risks is the IoT botnet attack, which tries to commit genuine, efficient, and successful cybercrimes. Botnets have emerged as a key concern in IoT landscape, owing to the significant damages these collections of compromised IoT devices cause. Botnets are probably the most common form of the Internet's harmful traffic. It's dangerous because a large number of infected hosts are working together to focus on a single target. An efficient model to detect attacks in IoT is required so as to take corrective actions at the faster pace. In this paper, Machine learning models including Gaussian Naive Bayes, SVM, Gradient Boosting, Logistic Regression, One vs Rest, Random Forest, Decision tree, KNN and SGD are implemented on KDD CUP99 dataset to detect Denial-of–services attack. Decision tree model is found to be efficient model to classify the attacks at quicker pace and higher accuracy.

With the growth of wireless network technology-based devices, identifying the communication behaviour of wireless connectivity enabled devices, e.g. Internet of Things (IoT) devices, is one of the vital aspects, in managing and securing IoT networks. Initially, devices use frames to connect to the access point on the local area network and then, use packets of typical communication protocols through the access point to communicate over the Internet. Toward this goal, network packet and IEEE 802.11 media access control (MAC) frame analysis may assist in managing IoT networks efficiently, and allow investigation of inclusive behaviour of IoT devices. This paper presents network traffic traces data of D-Link IoT devices from packet and frame levels. Data collection experiment has been conducted in the Network Systems and Signal Processing (NSSP) laboratory at Universiti Brunei Darussalam (UBD). All the required devices, such as IoT devices, workstation, smartphone, laptop, USB Ethernet adapter, and USB WiFi adapter, have been configured accordingly, to capture and store network traffic traces of the 14 IoT devices in the laboratory. These IoT devices were from the same manufacture (D-Link) with different types, such as camera, home-hub, door-window sensor, and smart-plug.

Security risk assessment and prediction are critical for organisations deploying Internet of Things (IoT) devices. An absolute minimum requirement for enterprises is to verify the security risk of IoT devices for the reported vulnerabilities in the National Vulnerability Database (NVD). This paper proposes a novel risk prediction for IoT devices based on publicly available information about them. Our solution provides an easy and cost-efficient solution for enterprises of all sizes to predict the security risk of deploying new IoT devices. After an extensive analysis of the NVD records over the past eight years, we have created a unique, systematic, and balanced dataset for vulnerable IoT devices, including key technical features complemented with functional and descriptive features available from public resources. We then use machine learning classification models such as Gradient Boosting Decision Trees (GBDT) over this dataset and achieve 71% prediction accuracy in classifying the severity of device vulnerability score.KeywordsIoT Security Risk PredictionNational Vulnerability Database (NVD)CVEIoT securityMachine learning

In this modern era, we are habituated to small smart home appliances that are typically known as the Internet of Things (IoT) devices. While these devices make our daily life easier with the help of internet connectivity, they are becoming an easy target for attackers to penetrate various cyber-attacks. Distributed Denial of Service (DDoS) is one of the pernicious attacks that can be attempted easily on IoT devices. To make our home smart, these devices play a vital role and as a security expert, it is our responsibility to ensure the security of these IoT devices. In this paper, we propose a security scheme to ensure the IoT devices' security. With the capability of the ensemble machine learning technique, we designed and implemented an intrusion detection system in securing IoT devices. To utilize the full capability of our security solution, we verified it with various well-known and benchmark datasets including the IoT dataset. The results show the efficiency of our security solution by producing better F-1 score and lower false alarms compared to any existing approaches.

Recently, reinforcement learning has been applied to various fields and shows better performance than humans. In particular, it is attracting attention in the fields of smart factories and robotics that require automatic control without human intervention. In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own IoT devices of the same type. There is no guarantee that the reinforcement learning agent that has learned the optimal control policy using one IoT device will perform optimal control of other IoT devices. Therefore, since reinforcement learning must be performed individually for each IoT device, it takes a lot of time and cost. To solve this problem, we propose a new method of federated reinforcement learning. In the proposed federated reinforcement learning, multiple agents have independent IoT devices, perform learning at the same time, and federate with each other to improve learning performance. Therefore, we apply a new gradient sharing method and transfer learning to reinforcement learning. In addition, Actor-Critic PPO, which shows good performance in reinforcement learning algorithms, is used. And, for smooth learning in the IoT environment where numerous devices exist, we propose an architecture based on Software-Defined Networking. Using multiple rotary inverted pendulum devices interconnected via a SDN, we demonstrate that the proposed federated reinforcement learning scheme can effectively facilitate the learning process for multiple IoT devices.

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.

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.

The need for secure Internet of Things (IoT) devices is growing as IoT\ndevices are becoming more integrated into vital networks. Many systems rely on\nthese devices to remain available and provide reliable service. Denial of\nservice attacks against IoT devices are a real threat due to the fact these low\npower devices are very susceptible to denial-of-service attacks. Machine\nlearning enabled network intrusion detection systems are effective at\nidentifying new threats, but they require a large amount of data to work well.\nThere are many network traffic data sets but very few that focus on IoT network\ntraffic. Within the IoT network data sets there is a lack of CoAP denial of\nservice data. We propose a novel data set covering this gap. We develop a new\ndata set by collecting network traffic from real CoAP denial of service attacks\nand compare the data on multiple different machine learning classifiers. We\nshow that the data set is effective on many classifiers.\n