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Zhao, Qian, Wang, Shengjin.  2020.  Real-time Face Tracking in Surveillance Videos on Chips for Valuable Face Capturing. 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE). :281–284.
Face capturing is a task to capture and store the "best" face of each person passing by the monitor. To some extent, it is similar to face tracking, but uses a different criterion and requires a valuable (i.e., high-quality and recognizable) face selection procedure. Face capturing systems play a critical role in public security. When deployed on edge devices, it is capable of reducing redundant storage in data center and speeding up retrieval of a certain person. However, high computation complexity and high repetition rate caused by ID switch errors are major challenges. In this paper, we propose a novel solution to constructing a real-time low-repetition face capturing system on chips. First, we propose a two-stage association algorithm for memory-efficient and accurate face tracking. Second, we propose a fast and reliable face quality estimation algorithm for valuable face selection. Our pipeline runs at over 20fps on Hisiv 3559A SoC with a single NNIE device for neural network inference, while achieving over 95% recall and less than 0.4 repetition rate in real world surveillance videos.
Biroon, Roghieh A., Pisu, Pierluigi, Abdollahi, Zoleikha.  2020.  Real-time False Data Injection Attack Detection in Connected Vehicle Systems with PDE modeling. 2020 American Control Conference (ACC). :3267—3272.
Connected vehicles as a promising concept of Intelligent Transportation System (ITS), are a potential solution to address some of the existing challenges of emission, traffic congestion as well as fuel consumption. To achieve these goals, connectivity among vehicles through the wireless communication network is essential. However, vehicular communication networks endure from reliability and security issues. Cyber-attacks with purposes of disrupting the performance of the connected vehicles, lead to catastrophic collision and traffic congestion. In this study, we consider a platoon of connected vehicles equipped with Cooperative Adaptive Cruise Control (CACC) which are subjected to a specific type of cyber-attack namely "False Data Injection" attack. We developed a novel method to model the attack with ghost vehicles injected into the connected vehicles network to disrupt the performance of the whole system. To aid the analysis, we use a Partial Differential Equation (PDE) model. Furthermore, we present a PDE model-based diagnostics scheme capable of detecting the false data injection attack and isolating the injection point of the attack in the platoon system. The proposed scheme is designed based on a PDE observer with measured velocity and acceleration feedback. Lyapunov stability theory has been utilized to verify the analytically convergence of the observer under no attack scenario. Eventually, the effectiveness of the proposed algorithm is evaluated with simulation study.
Sun, Mingjing, Zhao, Chengcheng, He, Jianping.  2020.  Privacy-Preserving Correlated Data Publication with a Noise Adding Mechanism. 2020 IEEE 16th International Conference on Control Automation (ICCA). :494—499.
The privacy issue in data publication is critical and has been extensively studied. However, most of the existing works assume the data to be published is independent, i.e., the correlation among data is neglected. The correlation is unavoidable in data publication, which universally manifests intrinsic correlations owing to social, behavioral, and genetic relationships. In this paper, we investigate the privacy concern of data publication where deterministic and probabilistic correlations are considered, respectively. Specifically, (ε,δ)-multi-dimensional data-privacy (MDDP) is proposed to quantify the correlated data privacy. It characterizes the disclosure probability of the published data being jointly estimated with the correlation under a given accuracy. Then, we explore the effects of deterministic correlations on privacy disclosure. For deterministic correlations, it is shown that the successful disclosure rate with correlations increases compared to the one without knowing the correlation. Meanwhile, a closed-form solution of the optimal disclosure probability and the strict bound of privacy disclosure gain are derived. Extensive simulations on a real dataset verify our analytical results.
Silitonga, Arthur, Becker, Juergen.  2020.  Security-driven Cross-Layer Model Description of a HW/SW Framework for AP MPSoC-based Computing Device. 2020 IEEE International Systems Conference (SysCon). :1—8.

Implementation of Internet-of-Things (IoT) can take place in many applications, for instance, automobiles, and industrial automation. We generally view the role of an Electronic Control Unit (ECU) or industrial network node that is occupied and interconnected in many different configurations in a vehicle or a factory. This condition may raise the occurrence of problems related to security issues, such as unauthorized access to data or components in ECUs or industrial network nodes. In this paper, we propose a hardware (HW)/software (SW) framework having integrated security extensions complemented with various security-related features that later can be implemented directly from the framework to All Programmable Multiprocessor System-on-Chip (AP MPSoC)-based ECUs. The framework is a software-defined one that can be configured or reconfigured in a higher level of abstraction language, including High-Level Synthesis (HLS), and the output of the framework is hardware configuration in multiprocessor or reconfigurable components in the FPGA. The system comprises high-level requirements, covert and side-channel estimation, cryptography, optimization, artificial intelligence, and partial reconfiguration. With this framework, we may reduce the design & development time, and provide significant flexibility to configure/reconfigure our framework and its target platform equipped with security extensions.

Xie, J., She, H., Chen, X., Zhang, H., Niu, Y..  2020.  Test Method for Automatic Detection Capability of Civil Aviation Security Equipment Using Bayesian Estimation. 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT. :831–835.
There are a lot of emerging security equipment required to be tested on detection rate (DR) and false alarm rate (FAR) for prohibited items. This article imports Bayesian approach to accept or reject DR and FAR. The detailed quantitative predictions can be made through the posterior distribution obtained by Markov chain Monte Carlo method. Based on this, HDI + ROPE decision rule is established. For the tests that need to make early decision, HDI + ROPE stopping rule is presented with biased estimate value, and criterial precision rule is presented with unbiased estimate value. Choosing the stopping rule according to the test purpose can achieve the balance of efficiency and accuracy.
Xu, Z., Easwaran, A..  2020.  A Game-Theoretic Approach to Secure Estimation and Control for Cyber-Physical Systems with a Digital Twin. 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS). :20–29.
Cyber-Physical Systems (CPSs) play an increasingly significant role in many critical applications. These valuable applications attract various sophisticated attacks. This paper considers a stealthy estimation attack, which aims to modify the state estimation of the CPSs. The intelligent attackers can learn defense strategies and use clandestine attack strategies to avoid detection. To address the issue, we design a Chi-square detector in a Digital Twin (DT), which is an online digital model of the physical system. We use a Signaling Game with Evidence (SGE) to find the optimal attack and defense strategies. Our analytical results show that the proposed defense strategies can mitigate the impact of the attack on the physical estimation and guarantee the stability of the CPSs. Finally, we use an illustrative application to evaluate the performance of the proposed framework.
Rahmati, A., Moosavi-Dezfooli, S.-M., Frossard, P., Dai, H..  2020.  GeoDA: A Geometric Framework for Black-Box Adversarial Attacks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :8443–8452.
Adversarial examples are known as carefully perturbed images fooling image classifiers. We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, each of them returning the top-1 label of the classifier. Our framework is based on the observation that the decision boundary of deep networks usually has a small mean curvature in the vicinity of data samples. We propose an effective iterative algorithm to generate query-efficient black-box perturbations with small p norms which is confirmed via experimental evaluations on state-of-the-art natural image classifiers. Moreover, for p=2, we theoretically show that our algorithm actually converges to the minimal perturbation when the curvature of the decision boundary is bounded. We also obtain the optimal distribution of the queries over the iterations of the algorithm. Finally, experimental results confirm that our principled black-box attack algorithm performs better than state-of-the-art algorithms as it generates smaller perturbations with a reduced number of queries.
Kornaropoulos, E. M., Papamanthou, C., Tamassia, R..  2020.  The State of the Uniform: Attacks on Encrypted Databases Beyond the Uniform Query Distribution. 2020 IEEE Symposium on Security and Privacy (SP). :1223–1240.
Recent foundational work on leakage-abuse attacks on encrypted databases has broadened our understanding of what an adversary can accomplish with a standard leakage profile. Nevertheless, all known value reconstruction attacks succeed under strong assumptions that may not hold in the real world. The most prevalent assumption is that queries are issued uniformly at random by the client. We present the first value reconstruction attacks that succeed without any knowledge about the query or data distribution. Our approach uses the search-pattern leakage, which exists in all known structured encryption schemes but has not been fully exploited so far. At the core of our method lies a support size estimator, a technique that utilizes the repetition of search tokens with the same response to estimate distances between encrypted values without any assumptions about the underlying distribution. We develop distribution-agnostic reconstruction attacks for both range queries and k-nearest-neighbor (k-NN) queries based on information extracted from the search-pattern leakage. Our new range attack follows a different algorithmic approach than state-of-the-art attacks, which are fine-tuned to succeed under the uniformly distributed queries. Instead, we reconstruct plaintext values under a variety of skewed query distributions and even outperform the accuracy of previous approaches under the uniform query distribution. Our new k-NN attack succeeds with far fewer samples than previous attacks and scales to much larger values of k. We demonstrate the effectiveness of our attacks by experimentally testing them on a wide range of query distributions and database densities, both unknown to the adversary.
Zhang, Y., Liu, Y., Chung, C.-L., Wei, Y.-C., Chen, C.-H..  2020.  Machine Learning Method Based on Stream Homomorphic Encryption Computing. 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). :1–2.
This study proposes a machine learning method based on stream homomorphic encryption computing for improving security and reducing computational time. A case study of mobile positioning based on k nearest neighbors ( kNN) is selected to evaluate the proposed method. The results showed the proposed method can save computational resources than others.
Matern, F., Riess, C., Stamminger, M..  2019.  Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations. 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW). :83—92.
High quality face editing in videos is a growing concern and spreads distrust in video content. However, upon closer examination, many face editing algorithms exhibit artifacts that resemble classical computer vision issues that stem from face tracking and editing. As a consequence, we wonder how difficult it is to expose artificial faces from current generators? To this end, we review current facial editing methods and several characteristic artifacts from their processing pipelines. We also show that relatively simple visual artifacts can be already quite effective in exposing such manipulations, including Deepfakes and Face2Face. Since the methods are based on visual features, they are easily explicable also to non-technical experts. The methods are easy to implement and offer capabilities for rapid adjustment to new manipulation types with little data available. Despite their simplicity, the methods are able to achieve AUC values of up to 0.866.
Khadka, A., Argyriou, V., Remagnino, P..  2020.  Accurate Deep Net Crowd Counting for Smart IoT Video acquisition devices. 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS). :260—264.

A novel deep neural network is proposed, for accurate and robust crowd counting. Crowd counting is a complex task, as it strongly depends on the deployed camera characteristics and, above all, the scene perspective. Crowd counting is essential in security applications where Internet of Things (IoT) cameras are deployed to help with crowd management tasks. The complexity of a scene varies greatly, and a medium to large scale security system based on IoT cameras must cater for changes in perspective and how people appear from different vantage points. To address this, our deep architecture extracts multi-scale features with a pyramid contextual module to provide long-range contextual information and enlarge the receptive field. Experiments were run on three major crowd counting datasets, to test our proposed method. Results demonstrate our method supersedes the performance of state-of-the-art methods.

Cheng, Z., Chow, M.-Y..  2020.  An Augmented Bayesian Reputation Metric for Trustworthiness Evaluation in Consensus-based Distributed Microgrid Energy Management Systems with Energy Storage. 2020 2nd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES). 1:215–220.
Consensus-based distributed microgrid energy management system is one of the most used distributed control strategies in the microgrid area. To improve its cybersecurity, the system needs to evaluate the trustworthiness of the participating agents in addition to the conventional cryptography efforts. This paper proposes a novel augmented reputation metric to evaluate the agents' trustworthiness in a distributed fashion. The proposed metric adopts a novel augmentation method to substantially improve the trust evaluation and attack detection performance under three typical difficult-to-detect attack patterns. The proposed metric is implemented and validated on a real-time HIL microgrid testbed.
Abratkiewicz, K., Gromek, D., Samczynski, P..  2019.  Chirp Rate Estimation and micro-Doppler Signatures for Pedestrian Security Radar Systems. 2019 Signal Processing Symposium (SPSympo). :212—215.

A new approach to micro-Doppler signal analysis is presented in this article. Novel chirp rate estimators in the time-frequency domain were used for this purpose, which provided the chirp rate of micro-Doppler signatures, allowing the classification of objects in the urban environment. As an example verifying the method, a signal from a high-resolution radar with a linear frequency modulated continuous wave (FMCW) recording an echo reflected from a pedestrian was used to validate the proposed algorithms for chirp rate estimation. The obtained results are plotted on saturated accelerograms, giving an additional parameter dedicated for target classification in security systems utilizing radar sensors for target detection.

Allig, C., Leinmüller, T., Mittal, P., Wanielik, G..  2019.  Trustworthiness Estimation of Entities within Collective Perception. 2019 IEEE Vehicular Networking Conference (VNC). :1–8.
The idea behind collective perception is to improve vehicles' awareness about their surroundings. Every vehicle shares information describing its perceived environment by means of V2X communication. Similar to other information shared using V2X communication, collective perception information is potentially safety relevant, which means there is a need to assess the reliability and quality of received information before further processing. Transmitted information may have been forged by attackers or contain inconsistencies e.g. caused by malfunctions. This paper introduces a novel approach for estimating a belief that a pair of entities, e.g. two remote vehicles or the host vehicle and a remote vehicle, within a Vehicular ad hoc Network (VANET) are both trustworthy. The method updates the belief based on the consistency of the data that both entities provide. The evaluation shows that the proposed method is able to identify forged information.
Xie, Y., Bodala, I. P., Ong, D. C., Hsu, D., Soh, H..  2019.  Robot Capability and Intention in Trust-Based Decisions Across Tasks. 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). :39—47.

In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots - inferred capability and intention - and their relationship to overall trust and eventual decisions. In particular, we examine delegation situations characterized by uncertainty, and explore how inferred capability and intention are applied across different tasks. We develop an online survey where human participants decide whether to delegate control to a simulated UAV agent. Our study shows that human estimations of robot capability and intent correlate strongly with overall self-reported trust. However, overall trust is not independently sufficient to determine whether a human will decide to trust (delegate) a given task to a robot. Instead, our study reveals that estimations of robot intention, capability, and overall trust are integrated when deciding to delegate. From a broader perspective, these results suggest that calibrating overall trust alone is insufficient; to make correct decisions, humans need (and use) multi-faceted mental models when collaborating with robots across multiple contexts.

Gwak, B., Cho, J., Lee, D., Son, H..  2018.  TARAS: Trust-Aware Role-Based Access Control System in Public Internet-of-Things. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :74–85.
Due to the proliferation of Internet-of-Things (IoT) environments, humans working with heterogeneous, smart objects in public IoT environments become more popular than ever before. This situation often requires to establish trust relationships between a user and a smart object for their secure interactions, but without the presence of prior interactions. In this work, we are interested in how a smart object can grant an access right to a human user in the absence of any prior knowledge in which some users may be malicious aiming to breach security goals of the IoT system. To solve this problem, we propose a trust-aware, role-based access control system, namely TARAS, which provides adaptive authorization to users based on dynamic trust estimation. In TARAS, for the initial trust establishment, we take a multidisciplinary approach by adopting the concept of I-sharing from psychology. The I-sharing follows the rationale that people with similar roles and traits are more likely to respond in a similar way. This theory provides a powerful tool to quickly establish trust between a smart object and a new user with no prior interactions. In addition, TARAS can adaptively filter malicious users out by revoking their access rights based on adaptive, dynamic trust estimation. Our experimental results show that the proposed TARAS mechanism can maximize system integrity in terms of correctly detecting malicious or benign users while maximizing service availability to users particularly when the system is fine-tuned based on the identified optimal setting in terms of an optimal trust threshold.
Zhu, S., Chen, H., Xi, W., Chen, M., Fan, L., Feng, D..  2019.  A Worst-Case Entropy Estimation of Oscillator-Based Entropy Sources: When the Adversaries Have Access to the History Outputs. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :152—159.
Entropy sources are designed to provide unpredictable random numbers for cryptographic systems. As an assessment of the sources, Shannon entropy is usually adopted to quantitatively measure the unpredictability of the outputs. In several related works about the entropy evaluation of ring oscillator-based (RO-based) entropy sources, authors evaluated the unpredictability with the average conditional Shannon entropy (ACE) of the source, moreover provided a lower bound of the ACE (LBoACE). However, in this paper, we have demonstrated that when the adversaries have access to the history outputs of the entropy source, for example, by some intrusive attacks, the LBoACE may overestimate the actual unpredictability of the next output for the adversaries. In this situation, we suggest to adopt the specific conditional Shannon entropy (SCE) which exactly measures the unpredictability of the future output with the knowledge of previous output sequences and so is more consistent with the reality than the ACE. In particular, to be conservative, we propose to take the lower bound of the SCE (LBoSCE) as an estimation of the worst-case entropy of the sources. We put forward a detailed method to estimate this worst-case entropy of RO-based entropy sources, which we have also verified by experiment on an FPGA device. We recommend to adopt this method to provide a conservative assessment of the unpredictability when the entropy source works in a vulnerable environment and the adversaries might obtain the previous outputs.
Ding, Hongfa, Peng, Changgen, Tian, Youliang, Xiang, Shuwen.  2019.  A Game Theoretical Analysis of Risk Adaptive Access Control for Privacy Preserving. 2019 International Conference on Networking and Network Applications (NaNA). :253–258.

More and more security and privacy issues are arising as new technologies, such as big data and cloud computing, are widely applied in nowadays. For decreasing the privacy breaches in access control system under opening and cross-domain environment. In this paper, we suggest a game and risk based access model for privacy preserving by employing Shannon information and game theory. After defining the notions of Privacy Risk and Privacy Violation Access, a high-level framework of game theoretical risk based access control is proposed. Further, we present formulas for estimating the risk value of access request and user, construct and analyze the game model of the proposed access control by using a multi-stage two player game. There exists sub-game perfect Nash equilibrium each stage in the risk based access control and it's suitable to protect the privacy by limiting the privacy violation access requests.

Sureka, N., Gunaseelan, K..  2019.  Detection Defense against Primary User Emulation Attack in Dynamic Cognitive Radio Networks. 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). 1:505—510.
Cognitive radio is a promising technology that intends on solving the spectrum scarcity problem by allocating free spectrum dynamically to the unlicensed Secondary Users (SUs) in order to establish coexistence between the licensed Primary User (PU) & SUs, without causing any interference to the incumbent transmission. Primary user emulation attack (PUEA) is one such major threat posed on spectrum sensing, which decreases the spectrum access probability. Detection and defense against PUEA is realized using Yardstick based Threshold Allocation technique (YTA), by assigning threshold level to the base station thereby efficiently enhancing the spectrum sensing ability in a dynamic CR network. The simulation is performed using NS2 and analysis by using X-graph. The results shows minimum interference to primary transmissions by letting SUs spontaneously predict the prospective spectrum availability and aiding in effective prevention of potential emulation attacks along with proficient improvement of throughput in a dynamic cognitive radio environment.
Feng, Qi, Huang, Jianjun, Yang, Zhaocheng.  2019.  Jointly Optimized Target Detection and Tracking Using Compressive Samples. IEEE Access. 7:73675–73684.
In this paper, we consider the problem of joint target detection and tracking in compressive sampling and processing (CSP-JDT). CSP can process the compressive samples of sparse signals directly without signal reconstruction, which is suitable for handling high-resolution radar signals. However, in CSP, the radar target detection and tracking problems are usually solved separately or by a two-stage strategy, which cannot obtain a globally optimal solution. To jointly optimize the target detection and tracking performance and inspired by the optimal Bayes joint decision and estimation (JDE) framework, a jointly optimized target detection and tracking algorithm in CSP is proposed. Since detection and tracking are highly correlated, we first develop a measurement matrix construction method to acquire the compressive samples, and then a joint CSP Bayesian approach is developed for target detection and tracking. The experimental results demonstrate that the proposed method outperforms the two-stage algorithms in terms of the joint performance metric.
Wang, Hui, Yan, Qiurong, Li, Bing, Yuan, Chenglong, Wang, Yuhao.  2019.  Sampling Time Adaptive Single-Photon Compressive Imaging. IEEE Photonics Journal. 11:1–10.
We propose a time-adaptive sampling method and demonstrate a sampling-time-adaptive single-photon compressive imaging system. In order to achieve self-adapting adjustment of sampling time, the theory of threshold of light intensity estimation accuracy is deduced. According to this threshold, a sampling control module, based on field-programmable gate array, is developed. Finally, the advantage of the time-adaptive sampling method is proved experimentally. Imaging performance experiments show that the time-adaptive sampling method can automatically adjust the sampling time for the change of light intensity of image object to obtain an image with better quality and avoid speculative selection of sampling time.
Zhao, Pu, Liu, Sijia, Chen, Pin-Yu, Hoang, Nghia, Xu, Kaidi, Kailkhura, Bhavya, Lin, Xue.  2019.  On the Design of Black-Box Adversarial Examples by Leveraging Gradient-Free Optimization and Operator Splitting Method. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). :121—130.
Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average cases but also in worst cases or adverse situations. Despite the long-term vision, however, existing studies on black-box adversarial attacks are still restricted to very specific settings of threat models (e.g., single distortion metric and restrictive assumption on target model's feedback to queries) and/or suffer from prohibitively high query complexity. To push for further advances in this field, we introduce a general framework based on an operator splitting method, the alternating direction method of multipliers (ADMM) to devise efficient, robust black-box attacks that work with various distortion metrics and feedback settings without incurring high query complexity. Due to the black-box nature of the threat model, the proposed ADMM solution framework is integrated with zeroth-order (ZO) optimization and Bayesian optimization (BO), and thus is applicable to the gradient-free regime. This results in two new black-box adversarial attack generation methods, ZO-ADMM and BO-ADMM. Our empirical evaluations on image classification datasets show that our proposed approaches have much lower function query complexities compared to state-of-the-art attack methods, but achieve very competitive attack success rates.
Taori, Rohan, Kamsetty, Amog, Chu, Brenton, Vemuri, Nikita.  2019.  Targeted Adversarial Examples for Black Box Audio Systems. 2019 IEEE Security and Privacy Workshops (SPW). :15—20.
The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known. In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task. We achieve a 89.25% targeted attack similarity, with 35% targeted attack success rate, after 3000 generations while maintaining 94.6% audio file similarity.
Kobayashi, Hiroyuki.  2019.  CEPHEID: the infrastructure-less indoor localization using lighting fixtures' acoustic frequency fingerprints. IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. 1:6842–6847.
This paper deals with a new indoor localization scheme called “CEPHEID” by using ceiling lighting fixtures. It is based on the fact that each lighting fixture has its own characteristic flickering pattern. Then, the author proposes a technique to identify individual light by using simple instruments and DNN classifier. Thanks to the less requirements for hardware, CEPHEID can be implemented by a few simple discrete electronic components and an ordinary smartphone. A prototype “CEPHEID dongle” is also introduced in this paper. Finally, the validity of the author's method is examined by indoor positioning experiments.
Yang, Xiaoliu, Li, Zetao, Zhang, Fabin.  2018.  Simultaneous diagnosis of multiple parametric faults based on differential evolution algorithm. 2018 Chinese Control And Decision Conference (CCDC). :2781–2786.
This paper addresses analysis and design of multiple fault diagnosis for a class of Lipschitz nonlinear system. In order to automatically estimate multi-fault parameters efficiently, a new method of multi-fault diagnosis based on the differential evolution algorithm (DE) is proposed. Finally, a series of experiments validate the feasibility and effectiveness of the proposed method. The simulation show the high accuracy of the proposed strategies in multiple abrupt faults diagnosis.