CoAP-DoS: An IoT Network Intrusion Data Set

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.

The explosive proliferation of Internet of Things (IoT) devices is generating an incomprehensible amount of data. Machine learning plays an imperative role in aggregating this data and extracting valuable information for improving operational and decision-making processes. In particular, emerging machine intelligence platforms that host pre-trained machine learning models are opening up new opportunities for IoT industries. While those platforms facilitate customers to analyze IoT data and deliver faster and accurate insights, end users and machine learning service providers (MLSPs) have raised concerns regarding security and privacy of IoT data as well as the pre-trained machine learning models for certain applications such as healthcare, smart energy, etc. In this paper, we propose a cloud-assisted, privacypreserving machine learning classification scheme over encrypted data for IoT devices. Our scheme is based on a three-party model coupled with a two-stage decryption Paillier-based cryptosystem, which allows a cloud server to interact with MLSPs on behalf of the resource-constrained IoT devices in a privacy-preserving manner, and shift load of computation-intensive classification operations from them. The detailed security analysis and the extensive simulations with different key lengths and number of features and classes demonstrate that our scheme can effectively reduce the overhead for IoT devices in machine learning classification applications.

Cognitive radio (CR) is considered as a key enabling communication technology that offers efficient wireless connectivity to Internet of Things (IoT) devices. Its integration with the future 5G architecture is expected to advance the IoT paradigm. Security attacks can severely degrade the performance of such CR‐based IoT (CRIoT) networks. The effect of security attacks can be reduced by implementing defensive approaches. However, such solutions come at the expense of degrading spectrum efficiency and consuming more network resources. In this paper, we investigate the spectrum sharing and access problem in a multidevice single‐transceiver CRIoT network with time‐critical applications under jamming attacks. Our main goal is to maximize the number of simultaneously served IoT devices over all available idle channels while ensuring delay requirement, hardware, link quality, security attacks, and spectrum utilization constraints. This problem is formulated as a total unimodular binary linear programming, which is shown to be solvable in polynomial time. Unlike most of previous security‐aware channel assignment solutions that conduct the channel assignment sequentially, our solution simultaneously provides secured distributed channel‐assignment decisions for multiple CRIoT links (batching method). Batching can be realized through an admission control stage for CR IoT devices to announce their control packets. Simulation results reveal that our solution significantly improves network performance compared to previous security‐aware schemes.

The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data exchange, revolutionizing various industries. However, the inherent vulnerabilities in traditional centralized transaction systems pose significant security challenges, particularly when dealing with sensitive data generated by IoT devices. This paper introduces an ICAA (Integrity Consensus Authorization Algorithm) for securing transaction records over the IoT network by leveraging Decentralized Distributed Ledger Technology (DDL), integrating the PICA (Proof-of-Integrity Consensus Algorithm) and CTAP (Context-Aware Transaction Authorization Protocol). The proposed system addresses the limitations of centralized architectures by employing a decentralized ledger, ensuring transparency, immutability, and tamper-resistant transaction records. The Proof-of-Integrity Consensus Algorithm enhances the security of the network by validating and confirming transactions based on the integrity of the data stored in the distributed ledger. This consensus mechanism minimizes the risk of fraudulent activities and unauthorized modifications, making it well-suited for the dynamic and distributed nature of IoT environments. Furthermore, the integration of the Context-Aware Transaction Authorization Protocol enhances the adaptability of the system to the diverse contexts in which IoT devices operate. The synergy between the Proof-of-Integrity Consensus Algorithm and the Context-Aware Transaction Authorization Protocol creates a comprehensive and secure framework for managing transaction records in IoT networks. . The proposed HGGC is 5.026% better than the ECMQV-MAC, 0.4215% better than QKD, and 0.0843% better than OTP in the nodes 200. The proposed model contributes to the establishment of a trustworthy and resilient infrastructure for the IoT, laying the foundation for secure and transparent transactions in the connected world.

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.

In the Internet of Things (IoT) technology, there are several issues to overcome, including network security. New attacks have been developed to target vulnerabilities in IoT devices, and the Internet of Things on a huge scale will exacerbate present network threats. Machine learning is becoming increasingly used in applications such as traffic classification and intrusion detection. This paper offers a technique for detecting network fault severity and identifying Internet of Things (IoT) devices based on several attributes. By pushing information to the network area, the proposed framework captures different features from each network flow in order to identify the source of traffic and the type of traffic generated is used to detect different network attacks. Different machine learning algorithms are tested to find the better suited algorithm that deliver best results. The following are some of the examples: Random Forest (RF) algorithm, decision tree, SVM, etc. After completing the experimentation, it was found that the random forest classifier and decision tree are the best performing ML algorithms to classify the network severity with an accuracy of about 96.98% and GNB is found as the least performing machine learning model with an accuracy of about 54.41%.

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.

Edge computing provides cloud services at the edge of the network for Internet of Things (IoT) devices. It aims to address low latency of the network and alleviates data processing of the cloud. This “cloud-edge-device” paradigm brings convenience as well as challenges for location-privacy protection of the IoT. In the edge computing environment, the fixed edge equipment supplies computing services for adjacent IoT devices. Therefore, edge computing suffers location leakage as the connection and authentication records imply the location of IoT devices. This article focuses on the location awareness in the edge computing environment. We adopt the “deniability” of authentication to prevent location leakage when IoT devices connect to the edge nodes. In our solution, an efficient deniable authentication based on a two-user ring signature is constructed. The robustness of authentication makes the fixed edge equipment accept the legal end devices. Besides, the deniability of authentication cannot convince any third party that the fact of this authentication occurred as communication transcript is no longer an evidence for this connection. Therefore, it handles the inherent location risk in edge computing. Compared to efficient deniable authentications, our protocol saves 10.728% and 14.696% computational cost, respectively.

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.

The proliferation of Internet of Things (IoT) devices has introduced unprecedented connectivity and convenience but also heightened the vulnerability to botnet attacks. There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This project presents a straightforward approach to detect botnet activity within IoT networks through the utilization of machine learning techniques. By analyzing network traffic patterns and employing unsupervised learning algorithms, we demonstrate an effective method to identify and mitigate botnet threats in IoT environments. By this project we intend to offer a valuable contribution in enhancing the security of IoT ecosystem. Key Word: Internet of Things(IoT),cybersecurity, botnet attacks, machine learning(ML),UNSW-NB15 dataset, exploratory data analysis, XgBoost

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.

Internet of Things (IoT) have opened the door to a world of unlimited possibilities for implementations in varied sectors in society, but it also has many challenges. One of those challenges is security and privacy. IoT devices are more susceptible to security threats and attacks. Due to constraints of the IoT devices such as area, power, memory, etc., there is a lack of security solutions that are compatible with IoT devices and applications, which is leading this world of securely connected things to the “internet of insecure things.” A promising solution to this problem is going beyond the standard or classical techniques to implementing the security solutions in the hardware of the IoT device. The integration of emerging technologies in IoT networks, such as machine learning, blockchain, fog/edge/cloud computing, and quantum computing have added more vulnerable points in the network. This paper introduces a comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how emerging technologies such as machine learning and blockchain are integrated in IoT, challenges resulted from this integration, and potential solutions to these challenges. The paper utilizes the 4-layer IoT architecture as a reference to identify security issues with corresponding solutions.

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.

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.