Verification Algorithm Research Articles

LAVA: Log Authentication and Verification Algorithm

Log files provide essential information regarding the actions of processes in critical computer systems. If an attacker modifies log entries, then critical digital evidence is lost. Therefore, many algorithms for secure logging have been devised, each achieving different security goals under different assumptions. We analyze these algorithms and identify their essential security features. Within a common system and attacker model, we integrate these algorithms into a single (parameterizable) “meta” algorithm called LAVA that possesses the union of the security features and can be parameterized to yield the security features of former algorithms. We present a security and efficiency analysis and provide a Python module that can be used to provide secure logging for forensics and incident response.

End-to-End Verification and Dosimetric Comparative Study of Brainlab Monte Carlo and Eclipse Acuros XB Photon Dose Calculation Algorithm for VMAT SRS Planning

We commissioned the Brainlab Monte Carlo Dose Calculation Algorithm for VMAT SRS planning and verified it by measurements and comparisons with two other algorithms: Pencil beam and Eclipse Acuros. The end-to-end verification and dosimetric comparative study results are reported in this abstract. Brainlab Elements Cranial SRS (v3.0) provides single lesion planning using VMAT optimization, thus allowing dose modulation with MLC leaf positioning, dose rate, and gantry speed. We commissioned and performed quality assurance for the available dose calculation algorithms, including Pencil Beam (PB) algorithm with only reference beam data (RBM PB) and PB with full beam data (Elements PB), and Monte Carlo (MC) algorithm XVMC. Calculation between the two were compared to each other and to Acuros XB dose calculation algorithm by Varian. Open field MLC, static cone, dynamic MLC, and conical cone plans were calculated with each algorithm. IBA CC01 ionization chamber was used for each measurement, and small field correction factors as determined in TRT-483 were applied. End-to-end tests were performed using an anthropomorphic stereotactic end-to-end verification (STEEV) phantom from CIRS and a phantom/diode array, StereoPHAN/SRSMapCHECK from SunNuclear. Each phantom was CT scanned with 0.5 mm slice thickness. Four clinical plans were optimized in Brainlab Elements Treatment Planning System (TPS). Each plan was calculated with the following algorithms: Elements PB, RBM PB, MC, and Acuros XB with 1 mm dose grid size. The delivered dose was measured with a SunNuclear SRSMapCHECK diode array detector. The calculated dose was compared with the measured dose by Gamma analysis with DTA 1%/1mm, 2%/1mm, and 3%/1mm. The results of point dose measurements for field size larger than 1 × 1 cm2 among measured dose, dose calculated by pencil beam algorithms, and dose calculated by Monte Carlo algorithm were in excellent agreement (1.45% ± 0.7%). For the small rectangular field sizes (0.4 × 1, 0.2 × 1 cm2), Monte Carlo calculation showed superior accuracy and robustness to pencil beam algorithm when compared with measured dose, with percent dose difference 13% and -2.2%, respectively. Monte Carlo calculation have higher gamma results compared with pencil beam algorithm due more accurate calculations with heterogenous materials. Acuros XB showed comparable accuracy with MC in terms of gamma passing rate. The SRS MapCHECK measurement agrees dosimetrically with the plans calculated by Pencil Beam algorithm and the Monte Carlo algorithm XVMC by Brainlab, as well as the Acuros XB dose calculation algorithm by Varian. Point dose calculation for small field dosimetry and gamma analysis using SRS MapCHECK both showed that Monte Carlo algorithm is more robust and is superior in regions of heterogenous materials. Therefore, we recommend that Monte Carlo algorithm for dose calculation be used in clinical SRS and SRT cases.

MKSS: An Effective Multi-authority Keyword Search Scheme for edge–cloud collaboration

The rapid development of cloud computing motivates Internet of Things users to outsource their data to enjoy value-added cloud services: data storage, data sharing, etc. To provide reliable but searchable results, searchable encryption (e.g., Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS)) become an area of active research. One major drawback of existing CP-ABKS designs lies in the private key being generated and distributed by a trusted Central Authority (CA), which is a single point of failure/attack. Another limitation is that the cloud server may return forged or incomplete query results to the data user. This paper proposes a novel Multi-authority Keyword Search Scheme for edge–cloud collaboration (MKSS). To protect the security of the private key, we design a private key generation algorithm based on secure multi-party computation. To preserve the integrity of search results, we construct an efficient verification algorithm that detects forged or incomplete results. We evaluate the security and performance of our protocol using real-world datasets to demonstrate robustness and efficiency. Our construction advances the existing body of research towards secure and efficient keyword search protocols in cloud systems.

MMDS: A secure and verifiable multimedia data search scheme for cloud-assisted edge computing

With the rapid development and popularity of Internet of Things (IoT) technology, IoT devices can generate MultiMedia Big Data (MMBD) as multimedia devices. MMBD is often encrypted and stored on an untrusted cloud server. Searchable encryption is an effective way which can finish a controllable keyword search of ciphertext. However, there are two limitations to these existing works. On the one hand, it is hard to independently protect the data privacy of each IoT device. On the other hand, when faced with an untrusted cloud server, it is difficult to implement more flexible fine-grained verification for search results. To overcome these limitations, we propose a secure and verifiable MultiMedia Data Search (MMDS) scheme for cloud-assisted edge computing. To protect the data of IoT devices independently, we designed a secure, flexible, and efficient keyword search mechanism based on bilinear pairings. To achieve a more flexible and practical search result verification mechanism, we design a fine-grained verification algorithm combining blockchain and hashing techniques. We have conducted performance evaluations and security proof and analysis for MMDS scheme. Finally, we applied MMDS scheme to solve real problems in intelligent multimedia systems. Security proof and analysis prove the security of MMDS. Performance analyses and evaluations further confirm that MMDS is efficient and feasible for practical applications.

A Stronger Secure Ciphertext Fingerprint-Based Commitment Scheme for Robuster Verifiable OD-CP-ABE in IMCC

Outsourced Decryption Attribute-based Encryption (OD-ABE) is emerging as a promising cryptographic tool to provide efficient fine-grained access control for data accessing and sharing in cloud-assisted Intelligent Internet of Mobile Things (IIoMT). Decryption verification is an essential property of OD-ABE to enable the mobile user to verify the precision of the decryption data. Unfortunately, the most representative verification (commitment) algorithms have various security flaws. In this paper, we first indicate that the two state-of-art key-based commitment schemes are vulnerable to “Commitment Extract(Decrypt)-then-Reuse Attack” and “Commitment Impersonation Attack” which demolish the unforgeability of the commitment. Then to cover all the existing attacks to commitment algorithms, we re-define a robuster verifiable security model for verifiable OD-ABE. Subsequently, we invent a ciphertext fingerprint (CTfp) based commitment scheme and give rigorous proof to the proposed commitment scheme including binding, hiding, unforgeability, and non-repudiation (traceability) in the random oracle. Next, we apply our CTfp-based commitment to the widely used OD-ABE schemes to provide them robuster verifiability. Finally, the theoretical comparison and simulation experiments are presented to show our new type of commitment algorithm is more secure and practical.

Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT

The development of Internet of Things (IoT) makes human life more intelligent, and the interconnection of all things has become a reality. However, the surge in the number of devices and centralized management brings severe challenges to IoT, such as single point of failure, poor security, privacy leakage, and low reliability. Due to the decentralization, verifiability and privacy protection of blockchain federated learning, some blockchain federated learning schemes have been proposed to solve these problems, but bring new challenges, such as device privacy leakage and heavy security computing load. In this paper, we propose a new decentralized, secure and verifiable consortium blockchain federated learning privacy protection scheme, named LPBFL, which realizes lightweight computing while ensuring the privacy of the local model and dataset of the device. To achieve lightweight privacy protection, LPBFL adopts the Paillier encryption and the newly designed lightweight digital signature and batch verification algorithm. Additionally, considering that devices upload invalid or even toxic local models intentionally or unintentionally, we design a device reputation selection mechanism to make blockchain federated learning more efficient. Finally, the theoretical analysis proves the security of LPBFL and verifies the unforgeability of the proposed digital signature. Comprehensive comparisons and extensive experiments demonstrate that our LPBFL has significant advantages in multiple aspects.

Active pantograph in high-speed railway: Review, challenges, and applications

The pantograph–catenary system (PCS) vibration causes the pantograph–catenary contact force (PCCF) to fluctuate, deteriorating current collection quality and damaging the electrical equipment. The active pantograph is one of the most promising methods for suppressing PCS coupling vibration and improving the current collection quality of high-speed trains. This paper first summarizes the research progress of five critical links in the active pantograph, including PCS modeling, control algorithm, hardware support, and HIL test verification. Then, the impact of the PCS model on numerical simulation accuracy and calculation time, the pros and cons of different control algorithms, and the execution mechanism selection are discussed. Finally, the active pantograph’s future development trend and research direction are prospected, such as model optimization, algorithm design, and signal correction. The analysis results show that establishing the PCS model should consider the representation of system dynamics and calculation efficiency. Control targets need to reflect the current collection quality comprehensively, and the measurement system needs to minimize the effect of locomotive disturbances on the observation results. The actuator structure needs to execute the control commands efficiently and quickly without affecting the operation of the raising mechanism. These factors, together with the real-time control algorithm, determine the practical effectiveness of the active pantograph.

VLSI design and FPGA implementation of state-of-charge and state-of-health estimation for electric vehicle battery management systems

State of charge (SOC) and state of health (SOH) estimation is an important function in electric-vehicle (EV) battery-management systems (BMSs). SOC and SOH are the principal parameters for judging a battery’s state and critical for ensuring its safety and performance. In a conventional EV BMS, SOC and SOH estimation is performed on the master. However, owing to the increasing number of battery cells and computational cost of estimation algorithms, hardware-oriented processing for each single-battery module on the slave is being researched to reduce the master’s burden. Accordingly, this paper describes the hardware-optimized algorithm, VLSI design, and field-programmable gate array (FPGA) implementation verification results of an application-specific integrated circuit in a slave module to estimate SOC and SOH for multiple cells. We introduce an SOC and SOH estimation algorithm with 70% complexity reduction within a 2% error margin based on each cell’s voltage, current, and temperature and design a hardware block and logic circuit to implement the algorithm. An FPGA was utilized to verify the designed logic circuit, revealing a performance time of 0.65μs at 100 MHz. Finally, an FPGA testbed connected to a real battery and analog-front-end board was built to check the real-time performance through a graphical user interface.

Verification and performance comparison of CNN-based algorithms for two-step helmet-wearing detection

Verification and performance comparison of CNN-based algorithms for two-step helmet-wearing detection

Assessment Features of the Non-State Expert’s Opinion in Criminal Proceedings

Assessment Features of the Non-State Expert’s Opinion in Criminal Proceedings

Rapid Fog-Removal Strategies for Traffic Environments.

In a foggy traffic environment, the vision sensor signal of intelligent vehicles will be distorted, the outline of obstacles will become blurred, and the color information in the traffic road will be missing. To solve this problem, four ultra-fast defogging strategies in a traffic environment are proposed for the first time. Through experiments, it is found that the performance of Fast Defogging Strategy 3 is more suitable for fast defogging in a traffic environment. This strategy reduces the original foggy picture by 256 times via bilinear interpolation, and the defogging is processed via the dark channel prior algorithm. Then, the image after fog removal is processed via 4-time upsampling and Gaussian transform. Compared with the original dark channel prior algorithm, the image edge is clearer, and the color information is enhanced. The fast defogging strategy and the original dark channel prior algorithm can reduce the defogging time by 83.93-84.92%. Then, the image after fog removal is inputted into the YOLOv4, YOLOv5, YOLOv6, and YOLOv7 target detection algorithms for detection and verification. It is proven that the image after fog removal can effectively detect vehicles and pedestrians in a complex traffic environment. The experimental results show that the fast defogging strategy is suitable for fast defogging in a traffic environment.

Security Quantification for Discrete Event Systems Based on the Worth of States

This work addresses the problem of quantifying opacity for discrete event systems. We consider a passive intruder who knows the overall structure of a system but has limited observational capabilities and tries to infer the secret of this system based on the captured information flow. Researchers have developed various approaches to quantify opacity to compensate for the lack of precision of qualitative opacity in describing the degree of security of a system. Most existing works on quantifying opacity study specified probabilistic problems in the framework of probabilistic systems, where the behaviors or states of a system are classified as secret or non-secret. In this work, we quantify opacity by a state-worth function, which associates each state of a system with the worth it carries. To this end, we present a novel category of opacity, called worthy opacity, characterizing whether the worth of information exposed to the outside world during the system’s evolution is below a threshold. We first provide an online approach for verifying worthy opacity using the notion of a run matrix proposed in this research. Then, we investigate a class of systems satisfying the so-called 1-cycle returned property and present a worthy opacity verification algorithm for this class. Finally, an example in the context of smart buildings is provided.

A survey on compositional algorithms for verification and synthesis in supervisory control

AbstractThis survey gives an overview of the current research on compositional algorithms for verification and synthesis of modular systems modelled as interacting finite-state machines. Compositional algorithms operate by repeatedly simplifying individual components of a large system, replacing them by smaller so-called abstractions, while preserving critical properties. In this way, the exponential growth of the state space can be limited, making it possible to analyse much bigger state spaces than possible by standard state space exploration. This paper gives an introduction to the principles underlying compositional methods, followed by a survey of algorithmic solutions from the recent literature that use compositional methods to analyse systems automatically. The focus is on applications in supervisory control of discrete event systems, particularly on methods that verify critical properties or synthesise controllable and nonblocking supervisors.

Field Choice Problem in Persistent Homology

This paper tackles the problem of coefficient field choice in persistent homology. When we compute a persistence diagram, we need to select a coefficient field before computation. We should understand the dependence of the diagram on the coefficient field to facilitate computation and interpretation of the diagram. We clarify that the dependence is strongly related to the torsion part of mathbb {Z} relative homology in the filtration. We show the sufficient and necessary conditions of the independence of coefficient field choice. An efficient algorithm is proposed to verify the independence. A slight modification of the standard persistence algorithm gives the verification algorithm. In a numerical experiment with the algorithm, a persistence diagram rarely changes even when the coefficient field changes if we consider a filtration in mathbb {R}^3. The experiment suggests that, in practical terms, changes in the field coefficient will not change persistence diagrams when the data are in mathbb {R}^3.

A Self-Adaptive Double Q-Backstepping Trajectory Tracking Control Approach Based on Reinforcement Learning for Mobile Robots

When a mobile robot inspects tasks with complex requirements indoors, the traditional backstepping method cannot guarantee the accuracy of the trajectory, leading to problems such as the instrument not being inside the image and focus failure when the robot grabs the image with high zoom. In order to solve this problem, this paper proposes an adaptive backstepping method based on double Q-learning for tracking and controlling the trajectory of mobile robots. We design the incremental model-free algorithm of Double-Q learning, which can quickly learn to rectify the trajectory tracking controller gain online. For the controller gain rectification problem in non-uniform state space exploration, we propose an incremental active learning exploration algorithm that incorporates memory playback as well as experience playback mechanisms to achieve online fast learning and controller gain rectification for agents. To verify the feasibility of the algorithm, we perform algorithm verification on different types of trajectories in Gazebo and physical platforms. The results show that the adaptive trajectory tracking control algorithm can be used to rectify the mobile robot trajectory tracking controller’s gain. Compared with the Backstepping-Fractional-Older PID controller and Fuzzy-Backstepping controller, Double Q-backstepping has better robustness, generalization, real-time, and stronger anti-disturbance capability.

The interplay of computational complexity and memory load during quantifier verification

ABSTRACTFormal analysis of the minimal computational complexity of verification algorithms for natural language quantifiers implies that different classes of quantifiers demand the engagement of different cognitive resources for their verification. In particular, sentences containing proportional quantifiers, e.g. “most”, provably require a memory component, whereas non-proportional quantifiers, e.g. “all”, “three”, do not. In an ERP study, we tested whether previously observed differences between these classes were modulated by memory load. Participants performed a picture-sentence verification task while they had to remember a string of 2 or 4 digits to be compared to a second string at the end of a trial. Relative to non-proportional quantifiers, proportional quantifiers elicited a sentence-internal sustained negativity. Additionally, an interaction between Digit-Load and Quantifier-Class was observed at the sentence-final word. Our results suggest that constraints on cognitive resources deployed during human sentence processing and verification are of the same nature as formal constraints on abstract machines.

Drones in Agriculture 4.0

The article is the product of the "Drones in agriculture 4.0" project, drone technology allows strengthening the development of different processes, especially agriculture, for this reason an investigation is carried out in order to use a drone that allows identifying the different pests that affect forages (grass crops). For the development of the research project, there will be different phases such as the process of review and exploration of technology, verification and selection of image processing algorithms and development of a database and interface according to the collection of information, the Waterfall methodology is applied. . In the first phase, work has been done on the state of the art of the different investigations that have been carried out over the years with the use of drones in agriculture, from a national and international level. In addition, in general, the information that has been collected in the different field practices by the drone in the experimental farm El Tibar of the University of Cundinamarca Sectional Ubaté and the Naranjitos de Guachetá farm is presented. Taking into account the above, the results obtained from this first phase are presented by analyzing the photographic evidence of the different effects that pests carry out on forages.

Efficient and Flexible Multiauthority Attribute-Based Authentication for IoT Devices

The correctness and reliability of data sources are the keys to the practicality of data collected by IoT devices. Attribute-based signature(ABS) is a cryptographic primitive for users to sign with their own attributes, which can be applied to the authentication process in IoT scenarios. The attribute authority is responsible for issuing the attribute key to the user in ABS. Multiple authorities can complete attribute management tasks to avoid the threat of a single authority. However, attribute authorities need to execute multiple interactions to collaborate to generate attribute keys for users, which brings a large transmission burden. In addition, a lot of resource-constrained terminals in the IoT mostly play the role of signer or verifier in authentication protocols. The signature generation and verification algorithms often have heavy pairing and exponentiation operations. Currently, no ABS scheme takes into account the efficiency of all participating entities simultaneously. In this paper, we present an aggregated anonymous key issue (AAKI) protocol to reduce the transmission burden between multiple authorities. Meanwhile, the non-interactive zero-knowledge proof aggregate exponentiation (NI-ZKPoKAE) protocol is designed to aggregate the transmitted secret values in AAKI. To reduce the computational burden of signers and verifiers, Blakley secret sharing, where the Hadamard matrix is used more efficiently to handle the (n,n)-threshold, is used to construct an efficient and fine-grained multi-authority ABS (EFMA-ABS) scheme. This brings high efficiency to all three types of parties involved in IoT authentication. Our above-mentioned protocols have been proven to be feasible and effective.

Design and verification of DFE re-decision algorithm for PAM6 high-speed transceiver based on FPGA

In this paper, a decision feedback equalizer (DFE) re-decision algorithm for a 6-level pulse-amplitude-modulation (PAM6) high-speed transceiver is proposed. PAM6 is a two-dimensional (2D) modulation. The four outer points are not used in the 2D constellation diagram to reduce the output power. Although the four outer points will not be modulated, they may be encountered during demodulation. This paper considers the above situation and theoretically analyzes the DFE module under the additive white Gaussian noise (AWGN) channel. Compared with the traditional pipelined DFE structure, the proposed algorithm can help solve the four outer points in PAM6. A serializer/deserializer (SerDes) simulation system is built based on a field programmable gate array (FPGA), and the performance of the proposed algorithm is carried out on the simulation system. According to this proposed algorithm, the symbol error rate (SER) of the PAM6 SerDes system can be reduced, and the system performance can be improved. When the DFE tap coefficient is 0.50 and the signal-to-noise ratio (SNR) is 22.0 dB, the proposed algorithm can additionally correct 8.18 % of erroneous symbols. When the DFE tap coefficient is 1.00 and the SNR is 22.0 dB, the proposed algorithm can additionally correct 17.30 % of erroneous symbols.

Secure Your Intention: On Notions of Pre-Opacity in Discrete-Event Systems

This paper investigates an important information-flow security property called opacity in partially-observed discrete-event systems. We consider the presence of a passive intruder (eavesdropper) that knows the dynamic model of the system and can use the generated information-flow to infer some “secret” of the system. A system is said to be opaque if it always holds the plausible deniability for its secret. Existing notions of opacity only consider secret either as currently visiting some secret states or as having visited some secret states in the past. In this paper, we investigate information-flow security from a new angle by considering the secret of the system as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intention</i> to execute some particular behavior of importance in the future. To this end, we propose a new class of opacity called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pre-opacity</i> that characterizes whether or not the intruder can predict the visit of secret states a certain number of steps ahead before the system actually does so. Depending the prediction task of the intruder, we propose two specific kinds of pre-opacity called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-step instant pre-opacity</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-step trajectory pre-opacity</i> to specify this concept. For each notion of pre-opacity, we provide a necessary and sufficient condition as well as an effective verification algorithm. The complexity for the verification of pre-opacity is exponential in the size of the system as we show that pre-opacity is inherently PSPACE-hard. Finally, we generalize our setting to the case where the secret intention of the system is modeled as executing a particular sequence of events rather than visiting a secret state.