Commercial Internet Of Things Devices Research Articles

A Systematic Investigation on Botnet Intrusion Detection Using Various Machine Learning Techniques

The Internet of Things (IoT) is growing rapidly in an exponential manner due to its versatility in technology. This has led to many challenges in securing the IoT environment. Devices in IoT environments are vulnerable to various cyberattacks. Botnet-based attacks are predominant and widespread in nature. Due to insufficient memory and computational power, the IoT environment cannot handle the botnet attack that affects security. Identifying intrusions in IoT environments is another challenge for researchers. Finding unknown patterns in the data generated through IoT networks helps improve security in the IoT environment. Machine learning (ML) is a platform that helps identify patterns in the provided data. In this study, we present our research on classifying incoming data from the IoT as malicious or benign using machine learning techniques. We propose an ML-based botnet attack detection framework for nine commercial IoT devices that primarily target BASHLITE and Mirai botnet attacks. Rigorous pragmatic research was conducted on the N-BaIoT dataset, which was extracted from realtime IoT devices connected to a network. Using this framework, the results have been depicted, which can efficiently detect botnet attacks and can also be applied to any other types of attacks.

Living in the Dark: MQTT-Based Exploitation of IoT Security Vulnerabilities in ZigBee Networks for Smart Lighting Control

The Internet of Things (IoT) has provided substantial enhancements to the communication of sensors, actuators, and their controllers, particularly in the field of home automation. Home automation is experiencing a huge rise in the proliferation of IoT devices such as smart bulbs, smart switches, and control gateways. However, the main challenge for such control systems is how to maximize security under limited resources such as low-processing power, low memory, low data rate, and low-bandwidth IoT networks. In order to address this challenge the adoption of IoT devices in automation has mandated the adoption of secure communication protocols to ensure that compromised key security objectives, such as confidentiality, integrity, and availability are addressed. In light of this, this work evaluates the feasibility of MQTT-based Denial of Service (DoS) attacks, Man-in-the-Middle (MitM), and masquerade attacks on a ZigBee network, an IoT standard used in wireless mesh networks. Performed through MQTT, the attacks extend to compromise neighboring Constrained Application Protocol (CoAP) nodes, a specialized service layer protocol for resource-constrained Internet devices. By demonstrating the attacks on an IKEA TRÅDFRI lighting system, the impact of exploiting ZigBee keys, the basis of ZigBee security, is shown. The reduction of vulnerabilities to prevent attacks is imperative for application developers in this domain. Two Intrusion Detection Systems (IDSs) are proposed to mitigate against the proposed attacks, followed by recommendations for solution providers to improve IoT firmware security. The main motivation and purpose of this work is to demonstrate that conventional attacks are feasible and practical in commercial home automation IoT devices, regardless of the manufacturer. Thus, the contribution to the state-of-the-art is the design of attacks that demonstrate how known vulnerabilities can be exploited in commercial IoT devices for the purpose of motivating manufacturers to produce IoT systems with improved security.

An Aggregated Mutual Information Based Feature Selection with Machine Learning Methods for Enhancing IoT Botnet Attack Detection.

Due to the wide availability and usage of connected devices in Internet of Things (IoT) networks, the number of attacks on these networks is continually increasing. A particularly serious and dangerous type of attack in the IoT environment is the botnet attack, where the attackers can control the IoT systems to generate enormous networks of “bot” devices for generating malicious activities. To detect this type of attack, several Intrusion Detection Systems (IDSs) have been proposed for IoT networks based on machine learning and deep learning methods. As the main characteristics of IoT systems include their limited battery power and processor capacity, maximizing the efficiency of intrusion detection systems for IoT networks is still a research challenge. It is important to provide efficient and effective methods that use lower computational time and have high detection rates. This paper proposes an aggregated mutual information-based feature selection approach with machine learning methods to enhance detection of IoT botnet attacks. In this study, the N-BaIoT benchmark dataset was used to detect botnet attack types using real traffic data gathered from nine commercial IoT devices. The dataset includes binary and multi-class classifications. The feature selection method incorporates Mutual Information (MI) technique, Principal Component Analysis (PCA) and ANOVA f-test at finely-granulated detection level to select the relevant features for improving the performance of IoT Botnet classifiers. In the classification step, several ensemble and individual classifiers were used, including Random Forest (RF), XGBoost (XGB), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (k-NN), Logistic Regression (LR) and Support Vector Machine (SVM). The experimental results showed the efficiency and effectiveness of the proposed approach, which outperformed other techniques using various evaluation metrics.

Botnet Attack Detection by Using CNN-LSTM Model for Internet of Things Applications

The Internet of Things (IoT) has grown rapidly, and nowadays, it is exploited by cyber attacks on IoT devices. An accurate system to identify malicious attacks on the IoT environment has become very important for minimizing security risks on IoT devices. Botnet attacks are among the most serious and widespread attacks, and they threaten IoT devices. Motionless IoT devices have a security weakness due to lack of sufficient memory and computation results for a security platform. In addition, numerous existing systems present themselves for finding unknown patterns from IoT networks to improve security. In this study, hybrid deep learning, a convolutional neural network and long short-term memory (CNN-LSTM) algorithm, was proposed to detect botnet attacks, namely, BASHLITE and Mirai, on nine commercial IoT devices. Extensive empirical research was performed by employing a real N-BaIoT dataset extracted from a real system, including benign and malicious patterns. The experimental results exposed the superiority of the CNN-LSTM model with accuracies of 90.88% and 88.61% in detecting botnet attacks from doorbells (Danminin and Ennio brands), whereas the proposed system achieved good accuracy (88.53%) in identifying botnet attacks from thermostat devices. The accuracies of the proposed system in detecting botnet attacks from security cameras were 87.19%, 89.23%, 87.76%, and 89.64%, with respect to accuracy metrics. Overall, the CNN-LSTM model was successful in detecting botnet attacks from various IoT devices with optimal accuracy.

Botnet Attack Detection Using Local Global Best Bat Algorithm for Industrial Internet of Things

The need for timely identification of Distributed Denial-of-Service (DDoS) attacks in the Internet of Things (IoT) has become critical in minimizing security risks as the number of IoT devices deployed rapidly grows globally and the volume of such attacks rises to unprecedented levels. Instant detection facilitates network security by speeding up warning and disconnection from the network of infected IoT devices, thereby preventing the botnet from propagating and thereby stopping additional attacks. Several methods have been developed for detecting botnet attacks, such as Swarm Intelligence (SI) and Evolutionary Computing (EC)-based algorithms. In this study, we propose a Local-Global best Bat Algorithm for Neural Networks (LGBA-NN) to select both feature subsets and hyperparameters for efficient detection of botnet attacks, inferred from 9 commercial IoT devices infected by two botnets: Gafgyt and Mirai. The proposed Bat Algorithm (BA) adopted the local-global best-based inertia weight to update the bat’s velocity in the swarm. To tackle with swarm diversity of BA, we proposed Gaussian distribution used in the population initialization. Furthermore, the local search mechanism was followed by the Gaussian density function and local-global best function to achieve better exploration during each generation. Enhanced BA was further employed for neural network hyperparameter tuning and weight optimization to classify ten different botnet attacks with an additional one benign target class. The proposed LGBA-NN algorithm was tested on an N-BaIoT data set with extensive real traffic data with benign and malicious target classes. The performance of LGBA-NN was compared with several recent advanced approaches such as weight optimization using Particle Swarm Optimization (PSO-NN) and BA-NN. The experimental results revealed the superiority of LGBA-NN with 90% accuracy over other variants, i.e., BA-NN (85.5% accuracy) and PSO-NN (85.2% accuracy) in multi-class botnet attack detection.

Towards Blockchain-Enabled Security Technique for Industrial Internet of Things Based Decentralized Applications

As the Industrial Internet of Things (IIoT) is one of the emerging trends and paradigm shifts to revolutionize the traditional industries with the fourth wave of evolution or transform it into Industry 4.0. This all is merely possible with the sensor-enabled technologies, e.g., wireless sensor networks (WSNs) in various landscapes, where security provisioning is one of the significant challenges for miniaturized power hungry networks. Due to the increasing demand for the commercial Internet of things (IoT) devices, smart devices are also extensively adopted in industrial applications. If these devices are compromising the date/information, then there will be a considerable loss and critical issues, unlike information compromising level by the commercial IoT devices. So emerging industrial processes and smart IoT based methods in medical industries with state-of-the-art blockchain security techniques have motivated the role of secure industrial IoT. Also, frequent changes in android technology have increased the security of the blockchain-based IIoT system management. It is very vital to develop a novel blockchain-enabled cyber-security framework and algorithm for industrial IoT by adopting random initial and master key generation mechanisms over long-range low-power wireless networks for fast encrypted data processing and transmission. So, this paper has three remarkable contributions. First, a blockchain-driven secure, efficient, reliable, and sustainable algorithm is proposed. It can be said that the proposed solution manages keys randomly by introducing the chain of blocks with less power drain, a small number of cores, will slightly more communication and computation bits. Second, an analytic hierarchy process (AHP) based intelligent decision-making approach for the secure, concurrent, interoperable, sustainable, and reliable blockchain-driven IIoT system. AHP based solution helps the industry experts to select the more relevant and critical parameters such as (reliability in-line with a packet loss ratio), (convergence in mapping with delay), and (interoperability in association with throughput) for improving the yield of the product in the industry. Third, sustainable technology-oriented services are supporting to propose the novel cloud-enabled framework for the IIoT platform for regular monitoring of the products in the industry. Moreover, experimental results reveal that proposed approach is a potential candidate for the blockchain-driven IIoT system in terms of reliability, convergence, and interoperability with a strong foundation to predict the techniques and tools for the regulation of the adaptive system from Industry 4.0 aspect.

Dissecting Energy Consumption of NB-IoT Devices Empirically

3GPP has recently introduced NB-IoT, a new mobile communication standard offering a robust and energy-efficient connectivity option to the rapidly expanding market of the Internet-of-Things (IoT) devices. To unleash its full potential, end devices are expected to work in a plug-and-play fashion, with zero or minimal configuration of parameters, still exhibiting excellent energy efficiency. We performed the most comprehensive set of empirical measurements with commercial IoT devices and different operators to date, quantifying the impact of several parameters to energy consumption. Our findings prove that parameters' settings do impact energy consumption, so proper configuration is necessary. We shed light on this aspect by first illustrating how the nominal standard operational modes map into real current consumption patterns of NB-IoT devices. Furthermore, we investigated which device-reported metadata metrics better reflected performance and implemented an algorithm to automatically identify device state in the current time-series logs. We worked with two major western European operators to provide a measurement-driven analysis of energy consumption and network performance of two popular NB-IoT boards under different parameter configurations. We observed that energy consumption is mostly affected by the paging interval in connected state, set by the base station. However, not all operators correctly implement such settings. Furthermore, under the default configuration, energy consumption in not strongly affected by packet size nor by signal quality, unless it is extremely bad. Our observations indicate that simple modifications to the default parameters' settings can yield great energy savings.

Secure Communications for Resource-Constrained IoT Devices.

The importance of securing communications on the Internet of Things (IoT) cannot be overstated. This is especially the case in light of the increasing proliferation of IoT devices and instances, as well as the growing dependence on their usage. Meanwhile, there have recently been mounting concerns over a wide array of vulnerabilities in IoT communications. The objective of this work is to address constraints in IoT devices that are “resource-constrained”, which are devices that are limited in terms of computing, energy, communication, or range capabilities, whether in terms of nominal or temporal limitations. Specifically, we propose a framework for resource-aiding constrained devices to facilitate secure communication. Without loss of generalization, the framework’s viability is illustrated by focusing on a group of security functions that utilize message authentication codes, which is a strongly representative example of resource-intensive security functions. Aspects of the framework are further demonstrated in processing cores commonly used in commercial IoT devices.

Towards occupant activity driven smart buildings via WiFi-enabled IoT devices and deep learning

With the unprecedented advancement of Internet of Things (IoT), automatic occupant activity recognition is becoming realizable for a myriad of emerging applications in smart buildings for energy efficiency and user experience enhancement. Existing activity recognition approaches require either the deployment of extra infrastructure or the cooperation of occupants to carry dedicated devices, which are expensive, intrusive and inconvenient for pervasive implementation. In this paper, we propose DeepHare, a deep learning-based human activity recognition scheme that can automatically identify common activities using only commodity WiFi-enabled IoT devices. We design a novel OpenWrt-based IoT platform to collect Channel State Information (CSI) measurements from commercial IoT devices. Moreover, an innovative deep learning framework, Autoencoder Long-term Recurrent Convolutional Network (AE-LRCN), is proposed. We developed dedicated neural network architectures to sanitize the noise in raw CSI data, extract high-level salient features and reveal the inherent temporal dependencies among data for accurate human activity recognition. All the parameters in AE-LRCN are fine-tuned end-to-end automatically. Extensive experiments are conducted in typical three indoor environments and the experimental results demonstrate that DeepHare outperforms existing methods and achieves a 97.6% activity recognition accuracy without human intervention.

WiFi-Based Human Identification via Convex Tensor Shapelet Learning

We propose AutoID, a human identification system that leverages the measurements from existing WiFi-enabled Internet of Things (IoT) devices and produces the identity estimation via a novel sparse representation learning technique. The key idea is to use the unique fine-grained gait patterns of each person revealed from the WiFi Channel State Information (CSI) measurements, technically referred to as shapelet signatures, as the "fingerprint" for human identification. For this purpose, a novel OpenWrt-based IoT platform is designed to collect CSI data from commercial IoT devices. More importantly, we propose a new optimization-based shapelet learning framework for tensors, namely Convex Clustered Concurrent Shapelet Learning (C3SL), which formulates the learning problem as a convex optimization. The global solution of C3SL can be obtained efficiently with a generalized gradient-based algorithm, and the three concurrent regularization terms reveal the inter-dependence and the clustering effect of the CSI tensor data. Extensive experiments are conducted in multiple real-world indoor environments, showing that AutoID achieves an average human identification accuracy of 91% from a group of 20 people. As a combination of novel sensing and learning platform, AutoID attains substantial progress towards a more accurate, cost-effective and sustainable human identification system for pervasive implementations.

Brain-computer interface control in a virtual reality environment and applications for the internet of things.

Brain-computer interfaces (BCIs) have enabled individuals to control devices such as spellers, robotic arms, drones, and wheelchairs, but often these BCI applications are restricted to research laboratories. With the advent of virtual reality (VR) systems and the internet of things (IoT) we can couple these technologies to offer real-time control of a user's virtual and physical environment. Likewise, BCI applications are often single-use with user's having no control outside of the restrictions placed upon the applications at the time of creation. Therefore, there is a need to create a tool that allows users the flexibility to create and modularize aspects of BCI applications for control of IoT devices and VR environments. Using a popular video game engine, Unity, and coupling it with BCI2000, we can create diverse applications that give the end-user additional autonomy during the task at hand. We demonstrate the validity of controlling a Unity-based VR environment and several commercial IoT devices via direct neural interfacing processed through BCI2000.

Design and Practical Evaluation of a Family of Lightweight Protocols for Heterogeneous Sensing through BLE Beacons in IoT Telemetry Applications.

The Internet of Things (IoT) involves a wide variety of heterogeneous technologies and resource-constrained devices that interact with each other. Due to such constraints, IoT devices usually require lightweight protocols that optimize the use of resources and energy consumption. Among the different commercial IoT devices, Bluetooth and Bluetooth Low Energy (BLE)-based beacons, which broadcast periodically certain data packets to notify their presence, have experienced a remarkable growth, specially due to their application in indoor positioning systems. This article proposes a family of protocols named Lightweight Protocol for Sensors (LP4S) that provides fast responses and enables plug-and-play mechanisms that allow IoT telemetry systems to discover new nodes and to describe and auto-register the sensors and actuators connected to a beacon. Thus, three protocols are defined depending on the beacon hardware characteristics: LP4S-6 (for resource-constraint beacons), LP4S-X (for more powerful beacons) and LP4S-J (for beacons able to run complex firmware). In order to demonstrate the capabilities of the designed protocols, the most restrictive (LP4S-6) is tested after implementing it for a telemetry application in a beacon based on Eddystone (Google’s open beacon format). Thus, the beacon specification is extended in order to increase its ability to manage unlimited sensors in a telemetry system without interfering in its normal operation with Eddystone frames. The performed experiments show the feasibility of the proposed solution and its superiority, in terms of latency and energy consumption, with respect to approaches based on Generic Attribute Profile (GATT) when multiple users connect to a mote or in scenarios where latency is not a restriction, but where low-energy consumption is essential.