The low-frequency radiated sound field can be effectively controlled through the adaptive active control method in theory. However, its application in underwater radiated noise control is not wide. In the active control system, especially the multi-channel feedback system, the step size has a very tremendous influence on the performance of the adaptive filter. If the step size is set unreasonably, the calculation results will not converge. The appropriate step size varies from case to case. For simple cases, the empirical value can be adopted to set the step size. When the numerical difference between channels is large, and when the control physical quantity such as sound pressure changes greatly with time, an determined step length can t meet the control requirements. In particular, it is difficult to choose the step size when the accurate reference signal cannot be obtained. The application of adaptive active methods in underwater noise control is limited to some extent by this problem. To solve this problem, this essay carried out the research of Filtered-X Least Mean Squares (FxLMS) algorithm based on variable step size, and carried out the corresponding numerical analysis and pool experiment to verify the feasibility of applying to underwater noise control.

The process of classifying audio data into several classes or categories is referred to as audio classification. The purpose of speaker recognition, one particular use of audio classification, is to recognize a person based on the characteristics of their speech. The phrase "voice recognition" refers to both speaker and speech recognition tasks. Speaker verification systems have grown significantly in popularity recently for a variety of uses, such as security measures and individualized help. Computers that have been taught to recognize individual voices can swiftly translate speech or confirm a speaker’s identification as part of a security procedure by identifying the speaker. Four decades of research have gone into speaker recognition, which is based on the acoustic characteristics of speech that differ from person to person. Some systems use auditory input from those seeking entry, just like fingerprint sensors match input fingerprint markings with a database or photographic attendance systems map inputs to a database. Personal assistants, like Google Home, for example, are made to limit access to those who have been given permission. Even under difficult circumstances, these systems must correctly identify or recognize the speaker. This research proposes a strong deep learning-based speaker recognition solution for audio categorization. We suggest self-augmenting the data utilizing four key noise aberration strategies to improve the system’s performance. Additionally, we conduct a comparison study to examine the efficacy of several audio feature extractors. The objective is to create a speaker identification system that is extremely accurate and can be applied in practical situations.

The goal of this project is to use hardware components built-in manufacturing faults as mobile phone IDs. We assessed the applicability of several I/O-related cell phone components, including sensors. Through this process, the focus was on creating hardware issue samples that could then be categorised using the device s speaker and microphone. In our technique, an audio sample was created by playing a known audio file via a mobile phone s speakers and then recording the sound using the same device. The impact of important variables on sample accuracy was examined using a variety of different sample groups. After collecting the samples, the frequency responses were extracted and classified. Data were categorised using a variety of classifiers, with certain label and sample group configurations achieving an accuracy of over 94.4\%. The conclusions of this article suggest that speaker and mike production faults may be exploited for device authentication.

In this paper, we present a platform based on a modular approach coupled with powerful algorithms for accurate detection and identification of chemical compounds. The system relies on multi-SAW (Surface Acoustic Wave) sensors that are functionalized differently, resulting in multi-responses that collectively constitute a fingerprint of the chemical compound. A prototype has been developed and the overall system, including the design of SAW module, the acquisition system, learning algorithms and online recognition of various compounds, has been tested and validated. The results showed a reliable and accurate system with a perfect score of 100\% recognition of DMMP.

This study presents a novel method of authentication in digital environment in which each element of authentication is linked to one another. Having multiple factors to authenticate and deriving co-relations among these increases the safety and security of the device. Types of behavioral and acoustic patterns which are to be considered are GPS, accelerometer, microphone \& speaker fingerprint, lip \& tongue movement sensing and pinna shape sensing. Pattern data from different sensors is compared and cross checked. Having multiple factors to authenticate and deriving co-relations among these increases the security of device. The main advantage of multi factor behavioral authentication is that the verification is done dynamically and continuously to provide real time security. All authentication activities are carried out in the background without the user being interrupted. Furthermore, because these authentication approaches do not involve the user, the user experience is enhanced along with the security of the device.

Audio fingerprinting is the method involved with addressing a sound sign minimally with the aid of isolating vital highlights of the sound substance a part of the good sized makes use of of acoustic fingerprinting includes substance-based sound healing broadcast watching and so forth it lets in gazing the sound free of its arrangement and with out the requirement for metadata it really works by using studying frequency styles and tracking down a fit internal its statistics set of tunes this utility tries to understand the songs through the use of a time-frequency graph primarily based on an audio fingerprint that is known as a spectrogram the software program utilizes a cell phone implicit microphone that assembles a concise instance of a legitimate that is played it analyzes the outside sound and seeks a comparable suit on a database in which thousands and thousands of songs are saved based totally on an acoustic fingerprint when the software reveals a in shape it retrieves records such as the album track name original music and so forth.

Research in underwater communication is rapidly becoming attractive due to its various modern applications. An efficient mechanism to secure such communication is via physical layer security. In this paper, we propose a novel physical layer authentication (PLA) mechanism in underwater acoustic communication networks where we exploit the position/location of the transmitter nodes to achieve authentication. We perform transmitter position estimation from the received signals at reference nodes deployed at fixed positions in a predefined underwater region. We use time of arrival (ToA) estimation and derive the distribution of inherent uncertainty in the estimation. Next, we perform binary hypothesis testing on the estimated position to decide whether the transmitter node is legitimate or malicious. We then provide closed-form expressions of false alarm rate and missed detection rate resulted from binary hypothesis testing. We validate our proposal via simulation results, which demonstrate errors’ behavior against the link quality, malicious node location, and receiver operating characteristic (ROC) curves. We also compare our results with the performance of previously proposed fingerprint mechanisms for PLA in underwater acoustic communication networks, for which we show a clear advantage of using the position as a fingerprint in PLA.

This paper reports the commercialized large area (20×30mm2), multi-functional, thin form-factor, ultrasound fingerprint technology for under display integration in mobile devices. This technology consists of a thin piezoelectric polymer ultrasonic transceiver layer deposited on highly scalable 2D pixel array fabricated using low temperature polysilicon (LTPS) thin film transistors (TFT) circuitry on glass substrate. The technology not only delivers a high quality under display fingerprint scanner for biometric authentication, but also enables multiple value-added features including heart rate monitor, ultrasound based passive stylus, force sensor, and a contact gesture sensor. The large sensing area removes the requirement for accurate finger placement and therefore provides a better user experience for fingerprint authentication. Larger sensing area is also used for multi-finger authentication for enhanced security. Furthermore, the integrated multifunctional sensing enriches the user experience in the scenarios of gaming, education, health indicator monitoring etc.

Partial discharge localization in power transformers is of utmost importance, requiring an effective evaluation method to identify the location of such events precisely. Antenna placement poses challenges within power transformers, as improper positioning can significantly affect localization precision. This paper introduces an evaluation of the fingerprinting method for ultra-high frequency partial discharge localization. The fingerprinting method, commonly employed in wireless localization systems, is utilized to assess the accuracy of partial discharge localization. The proposed method leverages fingerprinting analysis and received signal strength to evaluate partial discharge events in power transformers. Experimental partial discharge measurements are conducted on a power transformer model provided by Tesla Power Company. The results include the average received signal strength at each measurement position and the distance error of the partial discharge location determined using the fingerprinting method. This research contributes to assessing partial discharge in power transformers, offering valuable insights for enhancing their health and performance evaluation.

The two-factor authentication (2FA) has become pervasive as the mobile devices become prevalent. Existing 2FA solutions usually require some form of user involvement, which could severely affect user experience and bring extra burdens to users. In this work, we propose a secure 2FA that utilizes the individual acoustic fingerprint of the speaker/microphone on enrolled device as the second proof. The main idea behind our system is to use both magnitude and phase fingerprints derived from the frequency response of the enrolled device by emitting acoustic beep signals alternately from both enrolled and login devices and receiving their direct arrivals for 2FA. Given the input microphone samplings, our system designs an arrival time detection scheme to accurately identify the beginning point of the beep signal from the received signal. To achieve a robust authentication, we develop a new distance mitigation scheme to eliminate the impact of transmission distances from the sound propagation model for extracting stable fingerprint in both magnitude and phase domain. Our device authentication component then calculates a weighted correlation value between the device profile and fingerprints extracted from run-time measurements to conduct the device authentication for 2FA. Our experimental results show that our proposed system is accurate and robust to both random impersonation and Man-in-the-middle (MiM) attack across different scenarios and device models.

An efficient broadcast monitoring system is really needed in Myanmar music industry to solve the issues of copyright infringements and illegal benefit-sharing between artists and broadcasting stations. In this paper, a broadcast monitoring system is proposed for Myanmar FM radio stations by utilizing Mel Frequency Cepstral Coefficient (MFCC) based audio fingerprinting. The proposed system is easy to implement and achieves the correct and speedy music identification even for noisy and distorted broadcast audio streams. In this system, we deploy an audio fingerprint database of 4,379 songs and broadcast audio streams of 3 local FM channels of Myanmar to evaluate the performance of the proposed system. Experimental results show that the system achieves reliable performance.

The term "Internet of things (IoT) security" refers to the software industry concerned with protecting the IoT and connected devices. Internet of Things (IoT) is a network of devices connected with computers, sensors, actuators, or users. In IoT, each device has a distinct identity and is required to automatically transmit data over the network. Allowing computers to connect to the Internet exposes them to a number of major vulnerabilities if they are not properly secured. IoT security concerns must be monitored and analyzed to ensure the proper working of IoT models. Protecting personal safety while ensuring accessibility is the main objective of IoT security. This article has surveyed some of the methods and techniques used to secure data. Accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve are the assessment metrics utilized to compare the performance of the existing techniques. Further the utilization of machine learning algorithms like Decision Tree, Random Forest, and ANN tests have resulted in an accuracy of 99.4\%. Despite the results, Random Forest (RF) performs significantly better. This study will help to gain more knowledge on the smart home automation and its security challenges.

Since criminality is rising in the 21st century, people want to secure their property and belongings. So, everyone in this situation needs a secure system with cutting-edge technology. Therefore, a person may go out without worries. This project aims to acquire a home security system that can apply a phone call to the client’s GSM (Global System for Mobile) cell phone device and send a message in the shortest amount of time. Our Home security system has been followed by the latest technology at a low cost. In this study, we used the PIR (Passive Infra-Red) movement sensor, the Arduino sensor as the core for movement identification, and the GSM module for dialing the system user, which was used to develop the hardware for this system. This framework uses the Arduino IDE for Arduino and Putty for participating in programming analysis in the GSM unit. The PIR sensor has a crucial function used in this system for the security of any unauthorized individuals and automatically generates calls when neighboring circles intrude and are detected by the PIR sensor. The Integrated Home Safety framework can promptly examine and sense a human’s movement.

Most proposals for securing control systems are heuristic in nature, and while they increase the protection of their target, the security guarantees they provide are unclear. This paper proposes a new way of modeling the security guarantees of a Cyber-Physical System (CPS) against arbitrary false command attacks. As our main case study, we use the most popular testbed for control systems security. We first propose a detailed formal model of this testbed and then show how the original configuration is vulnerable to a single-actuator attack. We then propose modifications to the control system and prove that our modified system is secure against arbitrary, single-actuator attacks.

The increasing complexity and interconnectedness of Industrial Control Systems (ICSs) necessitate the integration of safety and security measures. Ensuring the protection of both personnel and critical assets has become a necessity. As a result, an integrated risk assessment approach is essential to comprehensively identify and address potential hazards and vulnerabilities. However, the data sources needed for an integrated risk assessment comes in many forms. In this context, Automation Markup Language (AutomationML or AML) emerges as a valuable solution to facilitate data exchange and integration in the risk assessment process. The benefits of utilizing AML include improved interoperability, enhanced documentation, and seamless collaboration between stakeholders. A model, filled with information relevant to integrated risk assessment, is developed to illustrate the effectiveness of AML. Ultimately, this paper showcases how AML serves as a valuable information model in meeting the growing need for comprehensive safety and security risk assessment in ICSs.

Recently, the manufacturing industry is changing into a smart manufacturing era with the development of 5G, artificial intelligence, and cloud computing technologies. As a result, Operational Technology (OT), which controls and operates factories, has been digitized and used together with Information Technology (IT). Security is indispensable in the smart manu-facturing industry as a problem with equipment, facilities, and operations in charge of manufacturing can cause factory shutdown or damage. In particular, security is required in smart factories because they implement automation in the manufacturing industry by monitoring the surrounding environment and collecting meaningful information through Industrial IoT (IIoT). Therefore, in this paper, IIoT security proposed in 2022 and recent technology trends are analyzed and explained in order to understand the current status of IIoT security technology in a smart factory environment.

The Internet of Things, or IoT, is a paradigm in which devices interact with the physical world through sensors and actuators, while still communicating with other computers over various types of networks. IoT devices can be found in many environments, often in the hands of non-technical users. This presents unique security concerns, since compromised devices can be used not only for typical objectives like network footholds, but also to cause harm in the real world (for instance, by unlocking the door to a house or changing safety configurations in an industrial control system). This work in progress paper presents a series of laboratory exercises under development at a large Midwestern university that introduces undergraduate cyber security engineering students to the Internet of Things and its (in)security considerations. The labs will be part of a 400-level technical elective course offered to cyber security engineering majors. The design of the labs has been grounded in the experiential learning process. The concepts in each lab module are couched in hands-on activities and integrate real world problems into the laboratory environment. The laboratory exercises are conducted using an Internet testbed and a combination of actual IoT devices and virtualized devices to showcase various IoT environments, vulnerabilities, and attacks.

Anomaly and intrusion detection in industrial cyber-physical systems has attracted a lot of attention in recent years. Deep learning techniques that require huge datasets are actively researched nowadays. The great challenge is that the real data on such systems, especially security-related data, is confidential, and a methodology for dataset generation is required. In this paper, the authors consider this challenge and introduce the methodology of dataset generation for research on the security of industrial water treatment facilities. The authors describe in detail two stages of the proposed methodology: the definition of a technological process and creating a testbed. The paper ends with a conclusion and future work prospects.

The paper proposes an algorithm for verifying the authenticity of automated process control system actuators based on the HART standard, which can act as the main or additional measure of protection against threats to the integrity of the system. The principle of operation of the HART standard is considered, a theoretical algorithm is given, additional technical solutions that increase its reliability are considered, as well as scenarios of possible attacks.

While the introduction of cyber physical systems (CPS) into society is progressing toward the realization of Society 5.0, the threat of cyberattacks on IoT devices(IoT actuators) that have actuator functions to bring about physical changes in the real world among the IoT devices that constitute the CPS is increasing. In order to prepare for unauthorized control of IoT actuators caused by cyberattacks that are evolving daily, such as zero-day attacks that exploit unknown vulnerabilities in programs, it is an urgent issue to strengthen the CPS, which will become the social infrastructure of the future. In this paper, I explain, in particular, the security requirements for IoT actuators that exert physical action as feedback from cyberspace to the physical space, and a security framework for control that changes the real world, based on changes in cyberspace, where attackers are persistently present. And, I propose a security scheme for IoT actuators that integrates a new concept of security known as Zero Trust, as the Zero Trust IoT Security Framework (ZeTiots-FW).

The rapid development in IT and OT system makes interactions among themselves and with humans immerse in the information flows from the physical to cyberspace. The traditional view of cyber-security faces challenges of deliberate cyber-attacks and unpredictable failures. Hence, cyber resilience is a fundamental property that protects critical missions. In this paper, we presented a mission-oriented security framework to establish and enhance cyber-resilience in design and action. The definition of mission-oriented security is given to extend CIA metrics of cyber-security, and the process of mission executions is analyzed to distinguish the critical factors of cyber-resilience. The cascading failures in inter-domain networks and false data injection in the cyber-physical system are analyzed in the case study to demonstrate how the mission-oriented security framework can enhance cyber resilience.

The last decade has shown that networked cyberphysical systems (NCPS) are the future of critical infrastructure such as transportation systems and energy production. However, they have introduced an uncharted territory of security vulnerabilities and a wider attack surface, mainly due to network openness and the deeply integrated physical and cyber spaces. On the other hand, relying on manual analysis of intrusion detection alarms might be effective in stopping run-of-the-mill automated probes but remain useless against the growing number of targeted, persistent, and often AI-enabled attacks on large-scale NCPS. Hence, there is a pressing need for new research directions to provide advanced protection. This paper introduces a novel security paradigm for emerging NCPS, namely Autonomous CyberPhysical Defense (ACPD). We lay out the theoretical foundations and describe the methods for building autonomous and stealthy cyber-physical defense agents that are able to dynamically hunt, detect, and respond to intelligent and sophisticated adversaries in real time without human intervention. By leveraging the power of game theory and multi-agent reinforcement learning, these selflearning agents will be able to deploy complex cyber-physical deception scenarios on the fly, generate optimal and adaptive security policies without prior knowledge of potential threats, and defend themselves against adversarial learning. Nonetheless, serious challenges including trustworthiness, scalability, and transfer learning are yet to be addressed for these autonomous agents to become the next-generation tools of cyber-physical defense.

Machine-learning-based approaches have emerged as viable solutions for automatic detection of container-related cyber attacks. Choosing the best anomaly detection algorithms to identify such cyber attacks can be difficult in practice, and it becomes even more difficult for zero-day attacks for which no prior attack data has been labeled. In this paper, we aim to address this issue by adopting an ensemble learning strategy: a combination of different base anomaly detectors built using conventional machine learning algorithms. The learning strategy provides a highly accurate zero-day container attack detection. We first architect a testbed to facilitate data collection and storage, model training and inference. We then perform two case studies of cyber attacks. We show that, for both case studies, despite the fact that individual base detector performance varies greatly between model types and model hyperparameters, the ensemble learning can consistently produce detection results that are close to the best base anomaly detectors. Additionally, we demonstrate that the detection performance of the resulting ensemble models is on average comparable to the best-performing deep learning anomaly detection approaches, but with much higher robustness, shorter training time, and much less training data. This makes the ensemble learning approach very appealing for practical real-time cyber attack detection scenarios with limited training data.

Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learningbased solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases the communication overhead, and raises privacy concerns. To address the aforementioned limits, we propose in this paper a novel detection mechanism that leverages the ability of the deep auto-encoder method to detect attacks relying only on the benign network traffic pattern. Using federated learning, the proposed intrusion detection system can be trained with large and diverse benign network traffic, while preserving the CAVs’ privacy, and minimizing the communication overhead. The in-depth experiment on a recent network traffic dataset shows that the proposed system achieved a high detection rate while minimizing the false positive rate, and the detection delay.

An intrusion detection system (IDS) is a crucial software or hardware application that employs security mechanisms to identify suspicious activity in a system or network. According to the detection technique, IDS is divided into two, namely signature-based and anomaly-based. Signature-based is said to be incapable of handling zero-day attacks, while anomaly-based is able to handle it. Machine learning techniques play a vital role in the development of IDS. There are differences of opinion regarding the most optimal algorithm for IDS classification in several previous studies, such as Random Forest, J48, and AdaBoost. Therefore, this study aims to evaluate the performance of the three algorithm models, using the NSL-KDD and UNSW-NB15 datasets used in previous studies. Empirical results demonstrate that utilizing AdaBoost+J48 with NSL-KDD achieves an accuracy of 99.86\%, along with precision, recall, and f1-score rates of 99.9\%. These results surpass previous studies using AdaBoost+Random Tree, with an accuracy of 98.45\%. Furthermore, this research explores the effectiveness of anomaly-based systems in dealing with zero-day attacks. Remarkably, the results show that anomaly-based systems perform admirably in such scenarios. For instance, employing Random Forest with the UNSW-NB15 dataset yielded the highest performance, with an accuracy rating of 99.81\%.