IoT-based Devices Research Articles

ENHANCING THE RESILIENCE OF IOT NETWORKS: STRATEGIES AND MEASURES FOR MITIGATING DDOS ATTACKS

The Internet of Things (IoTs) are emerging and become a vital need in our daily routine. The privacy protection and insecurity of these IoT-based devices face many challenges. Distributed denial of service (DDoS) attacks in IoT networks become a significant growing challenge that is addressed in this research. The resilience and strategy for IoT devices due to distributed denial of service (DDoS) attacks assess current security measures by proposing modern procedures to upgrade the strength of IoT frameworks. This article proposes a mechanism that mitigiates the effects of DDoS attacks in IoTs, that cause significant destruction to existing systems. Utilizing secondary data from Kaggle, the machine is trained and tested. Our proposed approach incorporates descriptive statistics, correlations, t-tests, chi-square tests, and regression analyses to supply a systematic understanding of IoT security by critically analyzing the existing variants of numerous DDoS attacks, Security issues in IoTs, and creation of them in Botnets or zombies. Our findings show that the proposed security techniques are viable and detection rates correlate with security viability. The proposed model asses various network threat and cybersecurity arrangements for mitigating DDoS attacks in IoT’s and outperforms the previously implemented Web Application Firewall (WAF), Bot Mitigation, Resource Prioritisation, and Content Delivery Networks (CDNs)based DDoS mitigation techniques by 80.5%, 88%, 86% in terms of effectiveness, T-test, chi test, and correlation.

A predictive model for the estimation of industrial [formula omitted] emissions for IoT-based devices

The paper is devoted to solving the problem, associated with increasing the forecasting accuracy of pollutant concentration level based on PM2.5 dust. The LANN model (LANN – Lagrange powered Artificial Neuron Network), proposed in the paper, allows you to take into account the mutual influence of pollution sources on the concentration level at monitoring points, which, on the one hand, allows to manage the volume of emissions from industrial enterprises in such a way as to avoid penalties and, on the other hand, allows the regulatory agencies to determine points for environmental control, at which permissible standards will be most likely exceeded. The suggested solution makes it possible to use low-cost IoT-based air quality monitoring devices, which were primarily considered not applicable due to their low accuracy and exposure to the influence of short-term stochastic factors; besides, no models were available, which would be capable to provide the necessary accuracy and forecast horizon on the base of IoT data. At the same time, the obtained model has low requirements for computing resources compared to dispersion models and is dependent on the accuracy of weather information; besides, the model allows you to take into account trends and obtain better forecast values in terms of accuracy than the models, that use regression methods, neural network models and ML models, stochastic models and their ensembles. The application of the model improves well-known pollutant dispersion estimation calculation techniques through the use of measurement data flow and dynamic adaptation to changing conditions The model was trained on the dataset over the period 2022-2023 collected from 5 monitoring devices, which were installed in the catchment area of the group, incl. 13 industrial points of PM2.5 pollutant sources.

The IoT-Based Early Warning System for Detecting High Tide Floods (ROB-EWS) in Tambak Lorok, Semarang Indonesia

Background and Objectives: The Tambaklorok fishing village area, located on the northern coast of Semarang City, Indonesia, is susceptible to sea level fluctuations, which often lead to tidal flooding and seawater encroaching into residential areas. Consequently, a seawater flood detector (rob) is needed for effective disaster mitigation for affected communities. This study aims to implement a disaster mitigation system by installing an IoT-based Seawater Flood (Rob) Early Warning System to reduce disaster risks and their impacts on the community. Methods: The IoT-based Early Warning System is designed to detect floods by monitoring sea level height parameters. Results: IoT-based devices measure sea level using ultrasonic sensors, with the readings processed by a microprocessor. The data is then periodically analyzed and transmitted to an application in real-time. This technology allows for real-time information dissemination through web applications and alerts in the form of sirens. These warnings enable residents to evacuate and move to safer areas. The siren alerts are categorized into two levels: Alert 1 indicates that seawater is approaching the land boundary, while Alert 2 signals that water has begun to enter residential areas. Additionally, designated evacuation zones will guide affected communities to safer areas. Conclusion: The installation of an IoT-based Early Warning System delivers real-time information to help mitigate the impact of rising sea levels (rob) in the Tambak Lorok fishing village community.

A systematic review on elliptic curve cryptography algorithm for internet of things: Categorization, application areas, and security

Cyberattacks on the Internet of Things (IoT) may be avoided by implementing cybersecurity protocols on IoT devices, platform packages, and Internet tools. A significant problem is developing a simple security mechanism suitable for resource-constrained IoT devices. Elliptic curve cryptography (ECC) is extensively utilized and well-suited for use in low-resource environments like smartphone devices. ECC utilizes short crypto keys with the same security strength as conventional cryptosystems, decreasing power consumption and enhancing device efficiency. This study comprehensively evaluates ECC data privacy and security techniques in IoT-based devices. The research also looked into other cryptography protocols that ECC may use to increase security and performance. The inclusion procedures were papers published between 2018 and 2023, written in English, and published in a peer-reviewed scholarly outlet. This research eventually reviewed 61 suitable publications in total. Compared to other cryptographic methods, the study discovered that ECC has received greater attention or is frequently employed in IoT-based systems. However, additional issues and concerns must be addressed, including efficient IoT device security, maintaining privacy, and device security against vulnerability assaults such as man-in-the-middle attacks. For the efficient security of the IoT-based system, an improved ECC-based algorithm should be developed to enhance the security and privacy issues of the IoT system.

Developing a hybrid feature selection method to detect botnet attacks in IoT devices

The Internet of Things, or IoT, is an important technology applied in various applications such as smart homes and innovative healthcare. Due to its architecture, IoT-based devices suffer from various security challenges, most commonly, botnet attacks. This article aims to develop a hybrid feature selection method to find the most influential features based on three feature selection methods, correlation, generalized normal distribution optimization, and lasso, to detect botnet attacks in IoT devices. The UNSW-NB15 dataset is used to assess the proposed system. Several classification models including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and bagging are utilized for the classification purpose. The proposed system was evaluated using several performance metrics. The results showed the correlation feature selection method had the most accurate botnet attack detection rate. RF also outperformed other models with a 95.11% detection rate in binary classification and 83.96% in multi-classification. On the other hand, results showed that the proposed hybrid method outperformed the feature selection methods with an increase of about 3% in both classifications. The AdaBoost model achieved an accuracy of 99.28% with binary classification by using 18 features, and the RF model achieved an accuracy of 86.62% with multi-classification by using 22 features. The robustness and efficacy of the proposed approach were demonstrated by comparing the study's results with several other studies that have used the same dataset. The results of the study can be implemented in real applications to detect network interference of a dynamic nature in real-time and assist intrusion detection systems (IDS) in addressing these attacks.

Sintesis polianilin (PANi) dengan metode polimerisasi interfasial sebagai bahan dasar pembuatan sensor gas amonia (NH3)

One of the main components in IoT-based devices is sensors. Polyaniline is a conductive polymer that has great potential to be developed as an active layer for chemical sensors because it has high sensitivity, is easy to synthesize and its electrical conductivity can be adjusted. This study aims to synthesize and characterize polyaniline using the interfacial polymerization method. In this study, polyaniline was synthesized by varying the aniline concentration of 2, 3, and 5 M, then its functional groups were characterized using FTIR and its conductivity and sensitivity were measured with an LCR meter. The result shows that polyaniline is in the form of emeraldine salt which is characterized by the formation of absorption bands at concentrations of 2, 3, and 5 M, respectively, 1,141.86 cm-1, 1,139 cm-1 and 1,139.93 cm-1. The highest conductivity was obtained at a concentration of 2 M, namely 4.9x10-5 and a sensitivity of 82.8% for an ammonia concentration of 100 ppm. So it can be concluded that the polyaniline obtained is a form of semiconducting emeraldine salt.

Women Safety Device

Women’ssafety has been highlighted as one of the major concerns of any society where several women are dealing with various safety issues like harassment, rape, molestation, and domestic violence due to different social or cultural reasons. Internet of Things (IoT) is becoming a promising technology to support day-to-day concerns and provide support in handling various affairs. Many IoT-based devices have been introduced by the community to help women deal with their potential safety threats. This study presents a systematic literature review of research studies exhibiting the IoT devicesfor women’s safety, the main features these devices offer as well as the wearable, sensors used. Key Words: Women safety, GPS, GSM, Push Button

DDOS Threats and Preventive Measures in IOT-Based Devices

This study explores the ever-changing landscape of IoT security in depth, with a specific emphasis given to thwarting the growing menace of Distributed Denial of Service (DDoS hereafter) assaults. A thorough literature assessment explores the sizable adoption of IoT era and associated security challenges, especially when it comes to DDoS assaults and their variations. The paper highlights the human implications of DDoS assaults throughout application, network, and transport layers, emphasizing the want for modern security features. Various studies make contributions precious insights into securing IoT devices, the potential role of block chain technology, and prevention techniques for home environments. The paper proposes a multifaceted defense mechanism, akin to fortifications in a virtual town, leveraging advanced detection techniques and continuous monitoring to limit the impact of DDoS attacks. In end, it advocates for a proactive technique, suggesting future studies instructions together with fortifying utility layers, exploring cloud computing implementations, and enhancing user awareness to ensure the resilience of IoT networks inside the face of evolving cyber threats.

Securing Smart Agriculture: Proposed Hybrid Meta-Model and Certificate-based Cyber Security Approaches

The Internet of Things is a decentralized network of physically connected devices that communicate with other systems and devices over the internet. As the number of IoT-based devices continues to grow at an exponential rate, this technology has the potential to improve nearly every aspect of daily life, from smart networks and transportation to home automation and agriculture. However, the absence of adequate security measures on all levels of the IoT poses a significant security risk, with the potential for cyber-attacks and data theft. While scholars have suggested various security measures, there are still gaps that need to be addressed. In this study, we analyzed previous research and proposed metamodels for security, IoT, and machine learning. We then proposed a new IoT-based smart agriculture model with integrated security measures to mitigate cyber- attacks and increase agricultural output. Our model takes into account the unique features of the smart farming domain and offers a framework for securing IoT devices in this specific application area. Moreover, in order to mitigate a range of cyber security attacks across various layers of IoT, we introduced two certificate-based schemes named CBHA and SCKA for smart agriculture. A comparative analysis of their security with existing literature demonstrates their superior robustness against diverse attacks. Additionally, security testing utilizing scyther affirms the resilience and security of both CBHA and SCKA, establishing them as viable options for ensuring security in smart agriculture

IoMT based smart healthcare system to control outbreaks of the COVID-19 pandemic

The COVID-19 pandemic caused millions of infections and deaths globally requiring effective solutions to fight the pandemic. The Internet of Things (IoT) provides data transmission without human intervention and thus mitigates infection chances. A road map is discussed in this study regarding the role of IoT applications to combat COVID-19. In addition, a real-time solution is provided to identify and monitor COVID-19 patients. The proposed framework comprises data collection using IoT-based devices, a health or quarantine center, a data warehouse for artificial intelligence (AI)-based analysis, and healthcare professionals to provide treatment. The efficacy of several machine learning models is also analyzed for the prediction of the severity level of COVID-19 patients using real-time IoT data and a dataset named ‘COVID Symptoms Checker’. The proposed ensemble model combines random forest and extra tree classifiers using a soft voting criterion and achieves superior results with a 0.922 accuracy score. The use of IoT applications is found to support medical professionals in investigating the features of the contagious disease and support managing the COVID pandemic more efficiently.

The use of IoT-based wearable devices to ensure secure lightweight payments in FinTech applications

Daily digital payments in Financial Technology (FinTech) are growing exponentially. A huge demand is for developing secure, lightweight cryptography protocols for wearable IoT-based devices. The devices hold the consumer information and transit functions in a secure environment to provide authentication and confidentiality using contactless Near-Field Communication (NFC) or Bluetooth technologies. On the other hand, Security breaches have been observed in various dimensions, especially in wearable payment technologies. In this paper, we developed a threat model in the proposed framework and how to mitigate these attacks. This study accepts the three-authentication factor, as biometrics is one of the user’s most vital authentication mechanisms. The scheme uses an “Elliptic Curve Integrated Encryption Scheme (ECIES)”, “Elliptic Curve Digital Signature Algorithm (ECDSA)” and “Advanced Encryption Standard (AES)” to encrypt the messages between the entities to ensure higher security. The security analysis of the proposed scheme is demonstrated through the Real-or-Random oracle model (RoR) and Scyther’s widely accepted model-checking tools. Finally, we present a comparative summary based on security features, communication cost, and computation overhead of existing methods, specifying that the proposed framework is secure and efficient for all kinds of remote and proximity payments, such as mini, macro, and micro-payments, using wearable devices.

Improved Steganography for IoT Network Node Data Security Promoting Secure Data Transmission using Generative Adversarial Networks

An internet of things (IoT) is an intelligent environment such as homes and smart cities of our country, and IoT improves the new technology implementation for home automation. The problem with security in IoT-based devices is that data transmission and signal passing are easily hacked using encryption and decryption methods. The old technology of the Steganography method does not improve the data hidden in images because encryption and decryption use a 1-bit 0.05-bit store, and low ranges hide the information in images, so that information hides out of the size and bits of the image. The hackers easily hack the hide information pixel by pixel or bit by bit in images. So, need for a proposed system, new technology, or methods. The suggested solution improves data concealment in photos by combining CNN’s deep learning techniques with steganography. The secret information these photographs convey can be shared without drawing hackers’ notice. The data is encrypted before being embedded in the image to increase its security. Steganography messages are frequently encrypted using more conventional methods first, after which the encrypted message is added to the cover image in some manner. The previous algorithm of SFNET algorithm architecture has been divided by segment, the segment based on width, height, and depth changes based improve performances. Existing systems of SFNET and SRNET are compared to the fractal net algorithm to improve the performance of 3 to 1 % of the proposed system.

QSMAH: A novel quantum-based secure cryptosystem using mutual authentication for healthcare in the internet of things

Healthcare in IoT provides many benefits, such as real-time data transfer and decision-making based on the information received from the patient's Body Sensor Nodes (BSNs). Many healthcare centers have adopted IoT-based devices such as heart monitoring implants and Electrocardiograms (ECG) for continuous patient monitoring in and out of hospitals. However, due to open Internet connectivity and lack of authentication, these devices may lead to several life-threatening risks. Recent advancements in Quantum Computing threatened the security of classical cryptographic primitives such as the RSA algorithm. The security and privacy of patient data are of utmost importance and are to be protected from existing vulnerabilities and futuristic Quantum attacks. Motivated by the abovementioned issues, we proposed the QSMAH protocol which ensures secure Key Agreement (KA) and Mutual Authentication (MA) based on Quantum Cryptography. Unlike classical authentication schemes, in our protocol, Quantum Teleportation and Quantum Entanglement are effectively utilized for secure data transfer among entities. The security of the proposed QSMAH protocol relies on a Quantum Key and Greenberger–Horne–Zeilinger (GHZ) states to achieve strong authentication. An extensive formal security analysis using BAN logic is provided to prove the goal of our protocol. The simulation of the proposed protocol is provided using Automated Validation of Internet Security Protocols and Applications (AVISPA). The results reflect that the security of our protocol cannot be tampered with by Quantum Shor's and Grover's algorithms. The security analysis proves that QSMAH is also resistant to classical attacks and futuristic Quantum attacks on cryptographic schemes.

Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework.

Smart home monitoring systems via internet of things (IoT) are required for taking care of elders at home. They provide the flexibility of monitoring elders remotely for their families and caregivers. Activities of daily living are an efficient way to effectively monitor elderly people at home and patients at caregiving facilities. The monitoring of such actions depends largely on IoT-based devices, either wireless or installed at different places. This paper proposes an effective and robust layered architecture using multisensory devices to recognize the activities of daily living from anywhere. Multimodality refers to the sensory devices of multiple types working together to achieve the objective of remote monitoring. Therefore, the proposed multimodal-based approach includes IoT devices, such as wearable inertial sensors and videos recorded during daily routines, fused together. The data from these multi-sensors have to be processed through a pre-processing layer through different stages, such as data filtration, segmentation, landmark detection, and 2D stick model. In next layer called the features processing, we have extracted, fused, and optimized different features from multimodal sensors. The final layer, called classification, has been utilized to recognize the activities of daily living via a deep learning technique known as convolutional neural network. It is observed from the proposed IoT-based multimodal layered system's results that an acceptable mean accuracy rate of 84.14% has been achieved.

A comprehensive analysis of the advances in Indian Digital Agricultural architecture

ICT-based interventions such as smart farming and precision agriculture are helping to improve the output of traditional agricultural systems and drive them toward sustainability. Data-driven technologies like remote sensing, smart sensors, and IoT-based devices constructed over AI/ML algorithms have become a fundamental aspect of agriculture that assists farmers with critical decision-making. This revolution is strengthening the farmers in terms of farm management by improving crop yield, pest control, soil health, etc. in real-time. We have thoroughly reviewed the digital adoption in terms of insights into the Indian agriculture sector and presented a comprehensive account of major ICT initiatives undertaken followed by a redundancy analysis as well as its influence on the sector. Unfortunately, while being a significant agrarian country, India's adoption of digital solutions is still in its infancy, as apparent from a close look at important FMIS and key components of recognized solutions used internationally. We found 28 FMIS solutions that are active globally, and have produced a list of 29 local (Indian) applications spread across 23 different agricultural sub-domains. Sadly, a majority among these were not unique and replicated similar features, besides just a few were found to be crop-specific applications. The article is unique in its approach to presenting the tale of digital penetration in the Indian agriculture sector and will be helpful to further Agri-stack vision of India.

Integration of Heterogeneous Sensor Systems for Disaster Responses in Smart Cities: Flooding as an Example

Smart cities represent a new perspective on modern urban development. They involve an information infrastructure environment with application intelligence to improve operational efficiency and welfare effectively. However, understanding how to overcome the barriers of data fragmentation and heterogeneity to exploit the strengths of existing resources and create integration effects remains a key challenge in smart city development. This research focuses on the effective management of heterogeneous sensor systems across different domains to improve quick disaster responses. Metadata serve as the core of this proposed framework, which is designed to not only describe the common and unique characteristics of various IoT-based devices and services, but also to provide necessary information to support the searching, requesting, and updating of required sensors and observation, as well as responding to the upcoming disaster. A workflow consisting of four list types was proposed and used to guide the response procedure. This research specifically aims to enable heterogeneous sensor systems available to all public or private stakeholders to be integrated in a collaborative fashion. While a flooding response was chosen for demonstration in this research, the proposed standard-based framework can be further promoted for other types of smart city applications, not limited to disaster response. The study’s results and implications underscore the importance of effective management of heterogeneous sensor systems and the role of metadata in enabling disaster responses in smart cities.

Localization of sensor nodes in the Internet of Things using fuzzy logic and learning automata

The Internet of Things (IoT) is a future-generation networking environment in which distributed smart objects can communicate directly and create a connection between different types of heterogeneous networks. Knowing the accurate localization of IoT-based devices is one of the most challenging issues in expanding the IoT network performance. This paper was done to propose a new fuzzy type2-based scheme to enhance the position accurateness of sensors deployed in the Internet of Things environments. Our proposed scheme is based on the weighted centralized localization strategy, in which the location of unknown nodes calculates using the fuzzy type-2 system. The flow measurement via the wireless channel to calculate the separation distance between the sensor/anchor nodes is employed as the fuzzy system input. Also, the fuzzy membership functions to better adaptivity of our scheme with lossy IoT environments via learning automata algorithm are tuned. Then, in the proposed method, the fuzzy type-2 calculations are restricted by comparing the received signal strength with a predefined threshold value to extend the network lifetime. The effectiveness of the proposed scheme has been proven through extensive simulation. Based on the simulation results, our scheme, on average, reduced the localization error by 35.9% and 9.5% decreased the energy consumption by 13% and 7.2%, and reduced the convergence rate by 33.1% and 12.37 % compared to the HSPPSO and IMRL methods, respectively.

BEAST: Behavior as a Service for Trust management in IoT devices

As the internet becomes intertwined into every aspect of human life, the security of the Internet of Things (IoT) is also becoming increasingly critical. IoT devices are becoming the primary data source for a variety of smart-city applications, where critical decisions are based on this collected data. If malicious actors gain control of and/or tamper with the data being transmitted, the integrity of an entire smart city will be compromised. However, through monitoring IoT devices’ behavior, anomalies can be detected and isolated to avoid any negative impact on decision-making. This behavioral monitoring process will complement traditional trust management approaches, since more accurate trust values can be calculated without the need to rely on a majority consensus. In this work, we present a BEhavior-As-a-Service for Trust management (BEAST) that implements a deep learning-based behavioral model to accurately classify IoT devices’ interactions in the system. Through the implementation of the Elo rating system, these classifications will be presented as a vector of behaviors per device, which dynamically reflects each device’s trust in the system. This work presents an analysis of our methodology as well as a threat model. Using simulations, a real-world use case is presented showing the interactions between IoT-based devices. Our results show that our BEAST model is able to dynamically evaluate each IoT device’s trust, as well as capture and mitigate multiple threats targeting the trust in the system.

An efficient multiple-word embedding-based cross-domain feature extraction and aspect sentiment classification

As the size of the feature space and data size increases, it is difficult to find the essential key features for cross-domain classification problems. Traditional word embedding and feature selection models use limited-sized data and dimensions for feature ranking and classification processes. To overcome these challenges, a hybrid cross-domain classification (CDC) based feature selection has been proposed to improve the efficiency of aspect sentiment classification on large databases. Initially, the crawled data from the Amazon product dataset to the cloud web server. In the data filtering approach, each record is pre-processed for the noise removal process. In the CNN framework, the different word-embedded models are used to ensemble the features from the training data. Here, TF-ID+Word2Vec, Hybrid Glove, and Skipgram are used to select the relevant key features from the training data. On the local side, remote IoT-based devices data distributed processing. For in-depth analysis of the sentiments IOT sensors have been used for automation. The filtered key features are given to the proposed feature extraction measure to select the essential features for the cross-domain classification model, it classifies the positive or negative. The proposed CDC model achieves overall precision and recall is 0.96 and 0.97 respectively. Experimental results proved that the proposed cross-domain feature selection-based classification approach has a better overall true positive than the conventional approaches.

IPoE Enhanced Reliability Model Based on SDH Optical Transmission for Intelligent Power Dispatching

Internet of Things (IoT) technology is one of the more advanced network communication technologies at present, and the IoT-based power dispatching system can improve the operation efficiency of power system. As the number of power users increases dramatically, higher requirements are put forward for the communication and management of IoT-based devices. The authentication method of traditional network structure can no longer adapt to the huge number of clients. For the problem of insufficient reliability of IPoE authentication method at the present stage, this paper proposes a model to enhance the reliability of IPoE authentication method. First, an intelligent scheduling optimization method based on SDH optical transmission technology is designed to complete the optimization of the scheduling network. The time delay of the communication network is reduced, and the reliability of the network is increased. Second, a data online collection module is established to complete the first optimization of the communication network. Finally, SDH optical transmission technology and design communication network terminal are integrated. The second optimization of the communication network is completed. After experimental testing, the proposed model can intelligently optimize the IoT-based power dispatching network. A standardized, manageable, and secure large-scale remote dispatching solution is realized.