Improving adversarial resilience for anomaly detection in the heterogeneous internet of things through ensemble models

Recent research endeavours are capitalizing on state of the art technologies to build a scalable Internet of Things (IoT). Envisioned as a technology to integrate the best of Wireless Sensor Networks and RFID systems, there is much promise for a global network of objects that are identifiable, track-able, and harmoniously informing. However, the realization of an IoT framework is hindered by many factors, the most pressing of which is attributed to the integration of these heterogeneous nodes and devices. A considerable subset of these nodes undergoes movement and dynamically enters and leaves the network backbone/topology. Routing packets and inter-nodal communication has received little attention; mainly due to the sheer reliance on the Internet as a backbone. However, spatially correlated entities in the IoT, and those which most often interact, would pose a significant overhead of communication if all intermediate packets need to be routed over distant backhauls. In remedy, we present a Pruned Adaptive IoT Routing (PAIR) protocol that selectively establishes routes of communication between IoT nodes. Since nodes in the IoT belong to different owners, we also introduce a pricing model to cater for the exchange of monetary costs by intermediate nodes to utilize their relaying resources. We also establish a cap on inter-nodal routing to dynamically utilize the Internet backbone if the source to destination distance surpasses a preset (case optimized) threshold. The PAIR routing protocol is elaborated upon, building upon the detailed system model presented in this paper. We finally present a use case to demonstrate the utility and practicality of PAIR in the heterogeneous IoT as it scales.

Effective scheduling of limited communication resources is one of the critical methods for data transmission in the Internet of things. However, the time slot utilization rate of many existing resource scheduling methods of Internet of things is not high. This article proposes a new efficient resource scheduling based on routing tree and detection matrix for Internet of things. In heterogeneous Internet of things, according to the different working modes and functions, the nodes are divided into Internet of things devices, routing nodes, and base station. We use time slot multiplexing to improve the time slot utilization of continuous transmission in Internet of things. First, the time slot allocation table in a round is obtained by the time slot scheduling based on the routing tree. Then, the collision matrix and the transmission matrix are established based on the time slot allocation table in a round. Finally, the minimum time slot scheduling in continuous rounds is determined based on the routing tree and the detection matrix. The experimental results show that the resource scheduling based on routing tree and detection matrix effectively improves the utilization of time slots and improves the throughput of the Internet of things.

Heterogeneous Internet of Things (IoT) and multi-access mobile edge computing (MA-MEC) are believed as supporting technologies for building a smart city. The advancement and flourish of IoT are facilitating the entry of human society into the Internet of Everything era, which lay the foundation of the smart city. To address the conflict between computation capability and low-cost mobile devices in IoT, the MA-MEC is available for supporting the resource-limited and computation-sensitive services and applications by computation offloading and distributed content delivery/caching. However, deploying cloud computing capability within the radio access network may face serious security threats, which stem from not only the existing technologies and networks but also the MA-MEC-based IoT itself. Therefore, in this paper, the solutions to address the security threats are investigated from physical layer perspectives, since physical layer security technologies have the advantages of achieving perfect secrecy, low-computational complexity, and resource consumption, and good adaptation for channel changes. Specifically, we investigate the secure wiretap coding, resource allocation, signal processing, and multi-node cooperation, along with physical layer key generation and authentication, to cope with the emerging security challenges. Finally, the paper is concluded with some possible future research directions.

The emergence of various smart services delivered by heterogeneous Internet of Things (IoT) devices has made daily human-life easy and comfortable. IoT devices have brought enormous convenience to various applications, no matter the IoT systems include homogeneous devices like in most sensor networks or heterogeneous devices like in smart homes or smart business applications. However, several known communication infrastructures of IoT systems are at risk to various security attacks and threats. The practice of discovering uncommon occurrences of conventional behaviors is known as anomaly detection. It is an essential tool for detecting fraud as well as network intrusion. In this work, we provide an anomaly-based model on the Extended Isolation Forest method. In our work, the available dataset ’UNSW_2018_IoT_Botnet_Final_10_best_Testing’ has been used for the experiment. Performance indicators, including accuracy, precision, recall, and F1-Score, are used to validate the performance of our suggested system. We get an Accuracy Score of 93% and F1-Score of 96% through the experiment. In addition, the most important top 12 features have a more substantial impact on correct prediction for anomaly identification and have also been identified in this study.KeywordsAnomaly detectionIsolation forestExtended isolation forestIot securityFeature set

Since the deployment of the Internet of Things (IoT), it has transformed everyday life by enabling intelligent environments that improve efficiency and automate services in domains such as agriculture, healthcare, smart cities, and industry. However, the rapid proliferation of IoT devices has introduced significant security challenges, largely driven by the heterogeneity of devices, resource constraints, and the increasing exposure of network communications. This work proposes a lightweight security protection mechanism for IoT networks based on trust modelling. The proposed approach integrates machine learning techniques to evaluate IoT node behavior using network-layer (Layer 3) traffic features under different labeling granularities, including binary, categorical, and subcategorical classifications. By focusing on network-layer observations, the model remains applicable across heterogeneous IoT devices while preserving a low computational footprint. In addition, the Common Vulnerability Scoring System (CVSS) is incorporated as a standardized vulnerability severity metric, enabling the integration of probabilistic security evidence with contextual information about potential impact. This combination allows the estimation of trust to reflect not only the likelihood of anomalous behavior but also its associated severity. Experimental evaluation was conducted using a representative IoT traffic dataset, multiple preprocessing strategies, and several classical machine learning models. The results demonstrate that aggregating traffic-based intrusion detection outputs with vulnerability severity metrics enables a more robust, flexible, and interpretable trust estimation process. This approach supports the early identification of potentially compromised nodes while maintaining scalability and efficiency, making it suitable for deployment in heterogeneous IoT environments.

The Internet of Things (IoT) has significant importance in the beyond fifth generation (B5G) communication systems. However, the IoT is vulnerable to disasters because the network is mains powered and the devices are delicate. In this paper, an unmanned aerial vehicle (UAV) is utilized to assist with emergency communications in a heterogeneous IoT (Het-IoT) and a distributed nonorthogonal multiple access (NOMA) scheme is proposed without the requirement of successive interference cancelation (SIC). In order to accommodate the communications of the surviving users and IoT devices efficiently, a multiobjective resource allocation (MORA) scheme is proposed for the UAV-assisted Het-IoT. At first, the original MORA problem is formulated and decoupled with the user power initialization. Then, based on a reweighted message-passing algorithm (ReMPA), the subchannels are assigned to the devices and the users in a stepwise manner. Finally, the transmitting power of the users and the devices is jointly fine-tuned using an iterative access control scheme. The simulation results confirm that the distributed SIC-free NOMA (DSF-NOMA) based on the MORA scheme provides satisfactory communication performances for the users and the devices with a tradeoff between the two subsystems. Compared with the traditional orthogonal frequency division multiple access (OFDMA) scheme and the MORA scheme with random subchannel assignment, the proposed DSF-NOMA-based MORA scheme yields better performances in terms of the sum rate of the users and the access ratio of the devices.

Advanced digital signatures for preserving privacy and trust management in hierarchical heterogeneous IoT: Taxonomy, capabilities, and objectives

Machine-to-Machine (M2M) communications in the Internet of Things (IoT) which are built over the wireless spectrum can easily be intercepted or eavesdropped by adversaries. While there are various solutions for enhancing the security of communications in IoT, they may incur significant overhead to the network. In this paper, we introduce several security methods at the communication protocol level, in which are based on the Constraint Application Protocol (CoAP), Datagram Transport Layer Security (DTLS), and Advanced Encryption Standard (AES). Additionally, we introduce a light-weight M2M communicating integrity approach with CoAP and bloom filter, as well as the decentralized Virtual Private Network (VPN). Via extensive performance evaluation, we validate the efficacy of our approach with respect to the impact on the overall network performance of the heterogeneous Internet of Things (HIoT) and provide some recommendations when our approach is adopted.

In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, etc.), the secure management of biometric data while respecting the users’ privacy, and the design of appropriate user interaction with facial verification mechanisms for all kinds of users. We analyze different approaches to solving all these challenges and propose a knowledge-driven methodology for the automated deployment of DNN-based FR solutions in IoT devices, with the secure management of biometric data, and real-time feedback for improved interaction. We provide some practical examples and experimental results with state-of-the-art DNNs for FR in Intel’s and NVIDIA’s hardware platforms as IoT devices.

The Internet of Things (IoT) is a new heterogeneous system integrated by the various end users (sensors and terminals) with different technologies. However, the limiting factor is bandwidth in the IoT due to the exploding end users and the network bandwidth requirements. A novel IoT model, which integrates the power-line carrier (PLC) and the wireless network (WN), is proposed to solve the bandwidth problem from the architecture, especially in the areas lacking network facilities. In addition, we exploit an effective virtual layer (EVL) which allows the different end users to access the system model seamlessly. Then, the attractor selection algorithm based on Markov chain (MASA) is employed to select an optimal path among the PLC or WN. The simulation results demonstrate that the proposed system model has the smaller average queuing delay than other algorithms and makes the model more stable and robust.

This paper presents the outcomes of a study that examines human interactions and mobility patterns in indoor spaces such as office environments. To this purpose, Bluetooth and IEEE 802.15.4-based fine grained traces have been collected through an extensive experimental measurement campaign in a heterogeneous Internet of Things (IoT) test bed, deployed in a real-life office environment. An initial analysis of these traces provides deeper insights into the encounter characteristics between human-held devices and human-held and static infrastructure devices. The study shows that due to lack of granularity, existing traces and the derived works, often neglect a significant portion of shorter-lived contacts that can be exploited for opportunistic communication. The presented findings also confirm that contact opportunities between mobile devices derived from observations of infrastructure devices can introduce significant errors into the modeling of such encounters. We believe that our traces and the derived models, together with our initial findings, are useful for the research community in supporting the design of future IoT solutions in such environments.

Random linear network coding (RLNC) has the potential to improve the performance of current and future Internet of Things (IoT) communication systems, but is computationally demanding due to matrix multiplications and inversions. Some single-core RLNC implementations achieve already sufficient coding speeds for contemporary multimedia streaming formats. However, advances in multimedia streaming formats and IoT applications will require the exploitation of heterogeneous multicore architectures, which are becoming common for a wide range of IoT nodes, including smartphones. In this paper, we introduce and evaluate efficient RLNC computing strategies for IoT node architectures, including the emerging heterogeneous big.LITTLE multicore architectures with multiple big (fast) cores and multiple LITTLE (slow) cores. In contrast to existing RLNC implementation strategies, we build on and adapt highly optimized dense matrix operations from the high performance computing field to RLNC on heterogeneous multicore IoT nodes. Our approach includes the optimization of RLNC matrix operations through optimized operations on matrix blocks with single instruction multiple data instructions. We schedule block operations on the heterogeneous cores through a directed acyclic graph that avoids artificial synchronization points while ensuring the data dependencies. We examine priority scheduling according to the number of outgoing dependencies of a task and data locality of cached blocks. Our extensive measurements with several heterogeneous big.LITTLE multicore IoT node and smartphone processor boards demonstrate higher RLNC encoding and decoding throughputs than existing approaches. Moreover, our measurements indicate that the utilization of more cores decreases energy consumption, which is an important goal for IoT nodes.

The extensive promotion of Internet of Thing (IoT), provides assorted opportunities and benefits in wide aspect of our life but unfortunately, the IoT is associated with various kinds of vulnerability attacks and anomaly exploits. Security experts indicate voluminous risks enforced by the IoT devices in different aspects. So, the attacks and anomaly detection become a growing concern in this sector. Disparate attacks allied Malicious Control, Denial of Service, Malicious Operations, Scan, Data Type Probing, Wrong Setup and spying becomes the severe cause of IoT system failure. The main objective of these attacks has to steal the confidential information from the system and generates unavailability of the system for authorized users. As compulsion of IoT security, we proposed a novel ensemble hyper-tuned model that automatically and effectively detects IoT sensors attacks and anomalies. This robust model is built on the basis of feature selection and ensemble technique of Machine Learning. The virtual IoT sensor’s environment generates a dataset by Distributed Smart Space Orchestration System (DS2OS), that is used for performing the experiments of attack detection by hyper-tuned Gradient Boosting ensemble algorithm. First, the feature selection process is applied for reducing the dimensions of the dataset, which enrich the environment for attacks and anomaly detection. After that, Gradient Boosting ensemble algorithm is applied with little hyperparameter tuning for getting the best results. Our model is outperformed for detecting the attacks on IoT sensor’s environment and effectiveness of this model is measured in terms of Accuracy = 99.40%, Precision = 99%, Recall = 99%, F1-Score = 99%. whereas, the ROC-AUC curve and confusion matrix is also generated for predicting the efficiency of our ensemble model.

In 5G mobile technology, the expansion of Internet of Things (IoT) has created a huge need for a wide variety of latency, dependability, and energy efficiency requirements, etc… Spectrum efficiency (SE) of such large scale network needs to be improved with an economical power consumption. The non-orthogonal multiple access (NOMA) technique is utilized to enhance system efficiency (SE) by merging several users in the same frequency. An energy efficient (EE) resource allocation (RA) problem has been formulated for NOMA based heterogeneous IoT networks. Using the examining technique of Cognitive Radio (CR) Network, a stepwise RA scheme is assigned for IoT Users (IoTUs) and Mobile Users (MUs) with the mutual interference management. Later, to find a solution quickly and flawlessly, a deep recurrent neural network (RNN) based mechanism has been proposed. Furthermore, to systemize the approach of heterogeneous users, a rate and precedence demands based scheduling method has been implemented. Extensive results demonstrate that the deep learning based framework performs better than traditional RA methods in terms of computational complexity. On comparing with the prevailing OFDMA technique, the NOMA system with the imperfect SIC provides an acceptable performance on the EE at the cost of low EE and high power consumption.

Pedestrian safety using the Internet of Things and sensors: Issues, challenges, and open problems