Biblio

This paper emphasizes the implementation of a conditioning circuit specific for an inductive or capacitive sensor. There are some inductive sensors, such as the proximity sensor, for which the inductance is dependent with the distance, or capacitive sensors strongly dependent with the humidity, distance, etc. This category of sensors is suitable for AC domain excitation from the measurement procedure point of view. Taking into consideration the fabrication technology, the measured physical quantity is being encoded as frequency or amplitude. To generate a sinusoidal signal with constant frequency and amplitude, the Colpitts or Hartley oscillators can be used [1], [2]. But the novelty of this paper is a different approach which reveals a microcontroller-based technology where the LC circuit works in an oscillating regime even though there is an underdamped oscillation behavior. For the oscillations’ occurrence, there will be a periodical energy injection using a driving source. One of the main advantages of the mentioned circuit is the small component number. The central unit of the embedded system will fulfil two functions: maintains the oscillating regime and measures the amplitude or frequency of the output signal. In this way, the built embedded system will be robust and easy to use due to its software configuration capabilities. As a plus, such a system can measure additional sensors used in environment parameters’ compensating procedure.

In some single-phase systems, power decoupling is necessary to balance the difference between constant power at load side and double-frequency ripple power at AC side. The application of active power decoupling methods aim to smooth this power oscillatory component, but, in general, these methods require the addition of many semiconductor devices and/or energy storage components, which is not lined up with achieving low cost, high efficiency and high power quality. This paper presents the analysis of a new single-phase rectifier based on zeta topology with power decoupling function and power factor correction using only two active switches and without extra reactive components. Its behavior is based on three stages of operation in a switching period, such that the power oscillating component is stored in one of the inherent zeta inductor. The theoretical foundation that justifies its operation is presented, as well as the simulation and experimental results to validate the applied concepts.

In this paper, we investigated a self-oscillating ring system with variation of the delay time, which demonstrates the phenomenon of laminar chaos. The presence of laminar chaos is demonstrated for various laws of time delay variation - sinusoidal, sawtooth, and triangular. The behavior of coupled systems with laminar chaos and diffusive coupling is investigated. The presence of synchronous behavior is shown.

This paper presents the results of a multifaceted study of the behavior of a novel hydrokinetic energy harvester that utilizes vertical oscillations. Unlike traditional rotating turbines used in hydrokinetic energy, this particular device utilizes the fluid structure interactions of vortex-induced-vibration and gallop. Due to the unique characteristics of this vertical motion, a thorough examination of the proposed system was conducted via a three-pronged approach of simulation, emulation, and field testing. Using a permanent magnet synchronous generator as the electrical power generation source, an electrical power conversion system was simulated, emulated, and tested to achieve appropriate power smoothing for use in microgrid systems present in many Alaskan rural locations.

For systems with order of dynamics higher than two and oscillating loads with low damping, a non-collocation of the sensing and control can deteriorate robustness of the feedback and, in worst case, even bring it to instability. Furthermore, for a contactless sensing of the oscillating mechanical load, like in the system under investigation, the control structure is often restricted to the single proportional feedback only. This paper proposes a novel robust feedback control scheme for a low-damped fourth-order system using solely the measured load displacement. For reference tracking, the loop shaping design relies on a band reject filter, while the plant uncertainties are used as robustness measure for determining the feedback gain. Since prime uncertainties are due to the stiffness of elastic link, correspondingly connecting spring, and due to the gain of actuator transducer, the loop sensitivity function with additive plant variation is used for robustness measure. In order to deal with unknown disturbances, which are inherently exciting the load oscillations independently of the loop shaping performance, an output delay-based compensator is proposed as a second control-degree-of-freedom. That one requires an estimate of the load oscillation frequency only and does not affect the shaped open-loop behavior, correspondingly sensitivity function. An extensive numerical setup of the modeled system, a two-mass oscillator with contactless sensing of the load under gravity and low damping of the connecting spring, is used for the control evaluation and assessment of its robustness.

The electron mobility μ exhibits oscillatory behavior with gate electric field F in an asymmetrically doped double V-shaped AlxGa1-xAs quantum well field effect transistor structure. By changing F, single-double-single subband occupancy of the system is obtained. We show that μ oscillates within double subband occupancy as a function of F near resonance of subband states due to the relocation of subband wave functions between the wells through intersubband effects.

This work demonstrates an ultra-compact low power oscillating micromechanical active pixel array based on a 0.35 μm back-end of line (BEOL)-embedded CMOS-MEMS technology. Each pixel consists of a 3-MHz clamped-clamped beam (CCB) MEMS resonator and a power scalable transimpedance amplifier (TIA) that occupies a small area of 70 × 60 μm2 and draws only 85 μW/pixel. The MEMS resonator is placed next to the TIA with less than 10 μm spacing thanks to the well-defined etch stops in the titanium nitride composite (TiN-C) CMOS-MEMS platform. A multiplexing phase-locked loop (PLL)-driven oscillator is employed to demonstrate the chip functionality. In particular, a nonlinear operation of the resonator tank is used to optimize the phase noise (PN) performance and Allan deviation (ADEV) behavior. The ADEV of 420 ppb averaged over best 3-pixels is exhibited based on such a nonlinear vibration operation.

Experimental implementation of phase-locked loop (PLL) with bandpass filter is proposed. Such PLL is noteworthy for neuron-like dynamics. It generates both regular and chaotic spikes and bursts. Previously proposed hardware implementation of this system has significant disadvantage – absence of excitable (non-oscillating) mode that is vital for brain neurons. The proposed electronic neuron-like generator is modified and could be used for hardware implementation of spiking neural networks.

This research covers the problem related to user behavior and its relationship with the protection of computer assets in terms of confidentiality, integrity, and availability. The main objective was to evaluate the relationship between the dimensions of awareness, compliance and appropriation of the information security culture and the asset protection variable, the ISCA diagnostic instrument was applied, and social engineering techniques were incorporated for this process. The results show the levels of awareness, compliance and appropriation of the university that was considered as a case study, these oscillate between the second and third level of four levels. Similarly, the performance regarding asset protection ranges from low to medium. It was concluded that there is a significant relationship between the variables of the investigation, verifying that of the total types of incidents registered in the study case, approximately 69% are associated with human behavior. As a contribution, an information security culture model was formulated whose main characteristic is a complementary diagnostic process between surveys and social engineering techniques, the model also includes the information security management system, risk management and security incident handling as part of the information security culture ecosystem in an enterprise.

Human behavior analysis is the process that consists of activity monitoring and behavior recognition and has become the core component of intelligent applications such as security surveillance and fall detection. Generally, the techniques involved in behavior recognition include sensor and vision-based processing. During the process, the activity information is typically required to ensure a good recognition performance. On the other hand, the privacy issue attracts much attention and requires a limited range of activity monitoring accordingly. We study behavior analysis for such privacy-oriented applications. A local object tracking (LOT) technique based on an infrared sensor array is developed in a limited monitoring range and is further realized to a practical bed-exit system in the clinical test environment. The experimental results show a correct recognition rate of 99% for 6 bedside activities. In addition, 89% of participants in a satisfaction survey agree on its effectiveness.

With the development in IC technology, testing the designs is becoming more and more complex. In the design, process testing consumes 60-80% of the time. The basic testing principle is providing the circuit under test (CUT) with input patterns, observing output responses, and comparing against the desired response called the golden response. As the density of the device are rising leads to difficulty in examining the sub-circuit of the chip. So, testing of design is becoming a time-consuming and costly process. Attaching additional logic to the circuit resolves the issue by testing itself. BIST is a relatively a design for testability technique to facilitate thorough testing of ICs and it comprises the test pattern generator, circuit under test, and output response analyzer. Quick diagnosis and very high fault coverage can be ensured by BIST. As complexity in the circuit is increasing, testing urges TPGs (Test Pattern Generators) to generate the test patterns for the CUT to sensitize the faults. TPGs are vulnerable to malicious activities such as scan-based side-channel attacks. Secret data saved on the chip can be extracted by an attacker by scanning out the test outcomes. These threats lead to the emergence of securing TPGs. This work demonstrates providing a secured test pattern generator for BIST circuits by locking the logic of TPG with a password or key generated by the key generation circuit. Only when the key is provided test patterns are generated. This provides versatile protection to TPG from malicious attacks such as scan-based side-channel attacks, Intellectual Property (IP) privacy, and IC overproduction.

Rapid development of wireless technologies has driven the evolution of smart grid application. In smart grid, authentication plays an important role for secure communication between smart meter and service provider. Hence, the design of secure authenticated key agreement schemes has received significant attention from researchers. In these schemes, a trusted third party directly participates in key agreement process. Although, this third party is assumed as trusted, however we cannot reject the possibility that being a third party, it can also be malicious. In the existing works, either the established session key is revealed to the agents of a trusted third party, or a trusted third party agent can impersonate the smart meter and establish a valid session key with the service provider, which is likely to cause security vulnerabilities. Therefore, there is a need to design a secure authentication scheme so that only the deserving entities involved in the communication can establish and know the session key. This paper proposes a new secure authenticated key agreement scheme for smart grid considering the fact that the third party can also be malicious. The security of the proposed scheme has been thoroughly evaluated using an adversary model. Correctness of the scheme has been analyzed using the broadly accepted Burrows-Abadi-Needham (BAN) Logic. In addition, the formal security verification of the proposed scheme has been performed using the widely accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool. Results of this simulation confirm that the proposed scheme is safe. Detailed security analysis shows the robustness of the scheme against various known attacks. Moreover, the comparative performance study of the proposed scheme with other relevant schemes is presented to demonstrate its practicality.

We propose an automated pipeline for analyzing privacy leaks in Android applications. By using a combination of dynamic and static analysis, we validate the results from each other to improve accuracy. Compare to the state-of-the-art approaches, we not only capture the network traffic for analysis, but also look into the data flows inside the application. We particularly focus on the privacy leakage caused by third-party services and high-risk permissions. The proposed automated approach will combine taint analysis, permission analysis, network traffic analysis, and dynamic function tracing during run-time to identify private information leaks. We further implement an automatic validation and complementation process to reduce false positives. A small-scale experiment has been conducted on 30 Android applications and a large-scale experiment on more than 10,000 Android applications is in progress.

Different security issues are a common problem for open source packages archived to and delivered through software ecosystems. These often manifest themselves as software weaknesses that may lead to concrete software vulnerabilities. This paper examines various security issues in Python packages with static analysis. The dataset is based on a snapshot of all packages stored to the Python Package Index (PyPI). In total, over 197 thousand packages and over 749 thousand security issues are covered. Even under the constraints imposed by static analysis, (a) the results indicate prevalence of security issues; at least one issue is present for about 46% of the Python packages. In terms of the issue types, (b) exception handling and different code injections have been the most common issues. The subprocess module stands out in this regard. Reflecting the generally small size of the packages, (c) software size metrics do not predict well the amount of issues revealed through static analysis. With these results and the accompanying discussion, the paper contributes to the field of large-scale empirical studies for better understanding security problems in software ecosystems.

This demo paper presents the technical details and usage scenarios of μSE: a mutation-based tool for evaluating security-focused static analysis tools for Android. Mutation testing is generally used by software practitioners to assess the robustness of a given test-suite. However, we leverage this technique to systematically evaluate static analysis tools and uncover and document soundness issues.μSE's analysis has found 25 previously undocumented flaws in static data leak detection tools for Android.μSE offers four mutation schemes, namely Reachability, Complex-reachability, TaintSink, and ScopeSink, which determine the locations of seeded mutants. Furthermore, the user can extend μSE by customizing the API calls targeted by the mutation analysis.μSE is also practical, as it makes use of filtering techniques based on compilation and execution criteria that reduces the number of ineffective mutations.

Quasi-identifier is an attribute combined with other attributes to identify specific tuples or partial tuples. Improper selection of quasi-identifiers will lead to the failure of current privacy protection anonymization technology. Therefore, in this paper, we propose a method to solve single structured relational data quasi-identifiers based on functional dependency and determines the attribute classification standard. Firstly, the solution scope of quasi-identifier is determined to be all attributes except identity attributes and critical attributes. Secondly, the real data set is used to evaluate the dependency relationship between the indefinite attribute subset and the identity attribute to solve the quasi-identifiers set. Finally, we propose an algorithm to find all quasi-identifiers and experiment on real data sets of different sizes. The results show that our method can achieve better performance on the same dataset.

Recommender systems have been proved to be effective techniques to provide users with better experiences. However, when a recommender knows the user's preference characteristics or gets their sensitive information, then a series of privacy concerns are raised. A amount of solutions in the literature have been proposed to enhance privacy protection degree of recommender systems. Although the existing solutions have enhanced the protection, they led to a decrease in recommendation accuracy simultaneously. In this paper, we propose a security-aware hybrid recommendation method by combining the factorization and regression techniques. Specifically, the differential privacy mechanism is integrated into data pre-processing for data encryption. Firstly data are perturbed to satisfy differential privacy and transported to the recommender. Then the recommender calculates the aggregated data. However, applying differential privacy raises utility issues of low recommendation accuracy, meanwhile the use of a single model may cause overfitting. In order to tackle this challenge, we adopt a fusion prediction model by combining linear regression (LR) and matrix factorization (MF) for collaborative recommendation. With the MovieLens dataset, we evaluate the recommendation accuracy and regression of our recommender system and demonstrate that our system performs better than the existing recommender system under privacy requirement.

Ransomware is a major malware attack experienced by large corporations and healthcare services. Ransomware employs the idea of cryptovirology, which uses cryptography to design malware. The goal of ransomware is to extort ransom by threatening the victim with the destruction of their data. Ransomware typically involves a 3-step process: analyzing the victim’s network traffic, identifying a vulnerability, and then exploiting it. Thus, the detection of ransomware has become an important undertaking that involves various sophisticated solutions for improving security. To further enhance ransomware detection capabilities, this paper focuses on an Application Programming Interface (API)-based ransomware detection approach in combination with machine learning (ML) techniques. The focus of this research is (i) understanding the life cycle of ransomware on the Windows platform, (ii) dynamic analysis of ransomware samples to extract various features of malicious code patterns, and (iii) developing and validating machine learning-based ransomware detection models on different ransomware and benign samples. Data were collected from publicly available repositories and subjected to sandbox analysis for sampling. The sampled datasets were applied to build machine learning models. The grid search hyperparameter optimization algorithm was employed to obtain the best fit model; the results were cross-validated with the testing datasets. This analysis yielded a high ransomware detection accuracy of 99.18% for Windows-based platforms and shows the potential for achieving high-accuracy ransomware detection capabilities when using a combination of API calls and an ML model. This approach can be further utilized with existing multilayer security solutions to protect critical data from ransomware attacks.

Security is a most concerning matter for client's data in today's emerging technological world in each field, like banking, management, retail, shopping, communication, education, etc. Arise in cyber-crime due to the black hat community, there is always a need for a better way to secure the client's sensitive information, Security is the key point in online banking as the threat of unapproved online access to a client's data is very significant as it ultimately danger to bank reputation. The more secure and powerful methods can allow a client to work with untrusted parties. Paper is focusing on how secure banking transaction system can work by using homomorphic encryption and steganography techniques. For data encryption NTRU, homomorphic encryption can be used and to hide details through the QR code, a cover image can be embed using steganography techniques.

The paradigm of quantum computation has led to the development of new algorithms as well variations on existing algorithms. In particular, novel cryptographic techniques based upon quantum computation are of great interest. Many classical encryption techniques naturally translate into the quantum paradigm because of their well-structured factorizations and the fact that they can be phased in the form of unitary operators. In this work, we demonstrate a quantum approach to data encryption and decryption based upon the McEliece cryptosystem using Reed-Muller codes. This example is of particular interest given that post-quantum analyses have highlighted this system as being robust against quantum attacks. Finally, in anticipation of quantum computation operating over binary fields, we discuss alternative operator factorizations for the proposed cryptosystem.

We suggest using Fully Homomorphic Encryption (FHE) to be used, not only to keep the privacy of information but also, to verify computations with no additional significant overhead, using only part of the variables length for verification. This method supports the addition of encrypted values as well as multiplication of encrypted values by the addition of their logarithmic representations and is based on a separation between hardware functionalities. The computer/server performs blackbox additions and is based on the separation of server/device/hardware, such as the enclave, that may deal with additions of logarithmic values and exponentiation. The main idea is to restrict the computer operations and to use part of the variable for computation verification (computation fingerprints) and the other for the actual calculation. The verification part holds the FHE value, of which the calculated result is known (either due to computing locally once or from previously verified computations) and will be checked against the returned FHE value. We prove that a server with bit computation granularity can return consistent encrypted wrong results even when the public key is not provided. For the case of computer word granularity the verification and the actual calculation parts are separated, the verification part (the consecutive bits from the LSB to the MSB of the variables) is fixed across all input vectors. We also consider the case of Single Instruction Multiple Data (SIMD) where the computation fingerprints index in the input vectors is fixed across all vectors.

Bitcoin and other digital cryptocurrencies have de-veloped rapidly in recent years. To reduce hardware and power costs, many criminals use the botnet to infect other hosts to mine cryptocurrency for themselves, which has led to the proliferation of mining botnets and is referred to as cryptojacking. At present, the mechanisms specific to cryptojacking detection include host-based, Deep Packet Inspection (DPI) based, and dynamic network characteristics based. Host-based detection requires detection installation and running at each host, and the other two are heavyweight. Besides, DPI-based detection is a breach of privacy and loses efficacy if encountering encrypted traffic. This paper de-signs a lightweight cryptojacking traffic detection method based on network behavior features for an ISP, without referring to the payload of network traffic. We set up an environment to collect cryptojacking traffic and conduct a cryptojacking traffic study to obtain its discriminative network traffic features extracted from only the first four packets in a flow. Our experimental study suggests that the machine learning classifier, random forest, based on the extracted discriminative network traffic features can accurately and efficiently detect cryptojacking traffic.

In this paper, we present the Cloud Property Graph (CloudPG), which bridges the gap between static code analysis and runtime security assessment of cloud services. The CloudPG is able to resolve data flows between cloud applications deployed on different resources, and contextualizes the graph with runtime information, such as encryption settings. To provide a vendorand technology-independent representation of a cloud service's security posture, the graph is based on an ontology of cloud resources, their functionalities and security features. We show, using an example, that our CloudPG framework can be used by security experts to identify weaknesses in their cloud deployments, spanning multiple vendors or technologies, such as AWS, Azure and Kubernetes. This includes misconfigurations, such as publicly accessible storages or undesired data flows within a cloud service, as restricted by regulations such as GDPR.

Multimedia Internet of Things (IoT) devices, especially, the smartphones are embedded with sensors including Global Positioning System (GPS), barometer, microphone, accelerometer, etc. These sensors working together, present a fairly complete picture of the citizens' daily activities, with implications for their privacy. With the internet, Citizens in Smart Cities are able to perform their daily life activities online with their connected electronic devices. But, unfortunately, computer hackers tend to write automated malicious applications to attack websites on which these citizens perform their activities. These security threats sometime put their private information at risk. In order to prevent these security threats on websites, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) are generated, as a form of security mechanism to protect the citizens' private information. But with the advancement of deep learning, text-based CAPTCHAs can sometimes be vulnerable. As a result, it is essential to conduct performance evaluation on the CAPTCHAs that are generated before they are deployed on multimedia web applications. Therefore, this work proposed an optimal and light-weight Convolutional Neural Network (CNN) to solve both numerical and alpha-numerical complex text-based CAPTCHAs simultaneously. The accuracy of the proposed CNN model has been accelerated based on Cyclical Learning Rates (CLRs) policy. The proposed CLR-CNN model achieved a high accuracy to solve both numerical and alpha-numerical text-based CAPTCHAs of 99.87% and 99.66%, respectively. In real-time, we observed that the speed of the model has increased, the model is lightweight, stable, and flexible as compared to other CAPTCHA solving techniques. The result of this current work will increase awareness and will assist multimedia security Researchers to continue and develop more robust text-based CAPTCHAs with their security mechanisms capable of protecting the private information of citizens in Smart Cities.

Nowadays, with the continuous increase in oil prices and the worldwide shift towards clean energy, all-electric vehicles are booming. Thence, these vehicles need widespread charging systems operating securely and reliably. Consequently, these charging systems need the most robust cybersecurity measures and strong authentication mechanisms to protect its user. This paper presents a new security scheme leveraging human biometrics in terms of iris recognition to defend against multiple types of cyber-attacks such as fraudulent identities, man-in-the-middle attacks, or unauthorized access to electric vehicle charging stations. Fundamentally, the proposed scheme implements a security mechanism based on the inherently unique characteristics of human eye biometric. The objective of the proposed scheme is to enhance the security of electric vehicle charging stations by using a low-cost and efficient authentication using k-Nearest Neighbours (KNN), which is a lightweight encryption algorithm.We tested our system on high-quality images obtained from the standard IITD iris database to search over the encrypted database and authenticate a legitimate user. The results showed that our proposed technique had minimal communication and computation overhead, which is quite suitable for the resource-limited charging station devices. Furthermore, we proved that our scheme outperforms other existing techniques.