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Selective fuzzy ensemble learner for cognitive detection of bio-identifiable modality spoofing in MCPS 20

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Selective fuzzy ensemble learner for cognitive detection of ...

20th IEEE International Conference on Advanced Communication technology, ICACT 2018

作者： Mowla, Nishat Doh, Inshil Chae, Kijoon Department of Computer Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Cyber Security Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9791188428007

Biometric features are widely used for user authentication and equally important to national and global technology systems. Various forms of biometric features, such as face, iris, fingerprint, are commonly used while more recently palm, vein and gait are also getting attention. Simultaneously various spoofing approaches have also been developed over time, which are capable of failing traditional biometric detection systems. Image synthesis with play-doh, gelatin, ecoflex etc. are some of the ways used in spoofing bio-identifiable property. Success of traditional detection systems are related to custom tailored solutions where feature engineering for each attack type must be developed. This is not a feasible process when we consider countless attack possibilities. Also, a slight change in the attack can cause the whole system to be redesigned and therefore becomes a limiting constraint. The recent success of machine learning inspires this paper to explore weak and strong learners with ensemble learning approaches using AdaBoost. Therefore, the paper proposes a selective ensemble fuzzy learner approach using Ada Boost, feature selection and combination of weak and strong learners to enhance the detection of bio-identifiable modality spoofing. Our proposal is verified on real dataset, LiveDet 2015, with a focus on fingerprint modality spoofing detection that can be used for authentication in Medical cyber Physical Systems (MCPS). © 2018 Global IT Research Institute (GiRI).

关键词： Biometrics

LR-RRA-CCA secure functional encryption for randomized functionalities from trapdoor HPS and LAF

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Science China(Information Sciences) 2018年第5期61卷 291-305页

作者： Huige WANG Kefei CHEN Baodong QIN Ziyuan HU Department of Network Engineering Anhui Science and Technology University Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Mathematics Hangzhou Normal University Westone Cryptologic Research Center National Engineering Laboratory for Wireless Security Xi'an University of Posts and Telecommunications State Key Laboratory of Cryptology

Original public-key encryption is viewed as a tool to encrypt point-to-point *** data is aimed at a particular user,where a sender encrypts a message x under a specified public key P *** this case,only the owner of the(unique)secret key corresponding to P K can decrypt the resulting ciphertext to recover the message ***,on some occasions,encryption is necessary to implement more fine-grained control of the encrypted *** example,the sender

关键词： LAF CCA RRA

A customized educational booster for online students in cybersecurity education 11

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A customized educational booster for online students in cybe...

11th International Conference on computer Supported Education, CSEDU 2019

作者： Rahouti, Mohamed Xiong, Kaiqi Department of Electrical Engineering University of South Florida TampaFL33620 United States Cyber Florida University of South Florida TampaFL33620 United States Department of Mathematics and Statistics University of South Florida TampaFL33620 United States Intelligent Computer Networking and Security Lab University of South Florida TampaFL33620 United States

ISBN: (纸本)9789897583674

Real-world lab experiments have been an integral part of computer science and engineering curriculums. However, human and computing resources as well as financial support to courses may be limited at many universities ranging from small to large universities and from liberal arts colleges to top research universities as there is a dramatic increase of student enrollments in computer science and engineering for the past ten years. Nowadays, like many other universities nation wide, University of South Florida (USF) in collaboration with the cyber Florida center offers online cyber security degrees as well as certificates. Throughout such programs, online students are expected to acquire knowledge via an interdisciplinary set of core courses prior to taking a deep dive into one of four following concentrations: cyber intelligence, digital forensics, information assurance and computer security fundamentals. However, when it comes to training student with real-world cyber security labs, both instructors and students face various challenges with regard to resources and virtual environment for exploring and running a broad range of security experiments and tests. In order to achieve the goal of our funded NSF project, in this paper we will discuss our teaching contributions to the development of a broad range of cyber security labs, facilitation of applied cryptography learning through experimental modules, and our customized virtual machine that fits the needs of various online computer networking and security courses. Specifically, we will first present our methodology for the design of our experimental modules and then present in details our pre-built Linux-based portable virtual machine. Those learning and experimental modules have been developed at different levels to meet the need of students with different academic and industrial backgrounds. Copyright © 2019 by SCITEPRESS – Science and technology Publications, Lda. All rights reserved.

关键词： Virtual reality

Monitoring-based differential privacy mechanism against query-flooding parameter duplication attack

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arXiv 2020年

作者： Yan, Haonan Li, Xiaoguang Li, Hui Li, Jiamin Sun, Wenhai Li, Fenghua State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xidian University Xi’an China department of computer and information technology Purdue University West Lafayette United States State Key Laboratory of Integrated Services Networks Institute of Information Engineering Chinese Academic of Sciences Beijing China

Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries. Though there are some protection options such as differential privacy (DP) and monitoring, which are considered promising techniques to mitigate this attack, we still find that the vulnerability persists. In this paper, we propose an adaptive query-flooding parameter duplication (QPD) attack. The adversary can infer the model information with black-box access and no prior knowledge of any model parameters or training data via QPD. We also develop a defense strategy using DP called monitoring-based DP (MDP) against this new attack. In MDP, we first propose a novel real-time model extraction status assessment scheme called Monitor to evaluate the situation of the model. Then, we design a method to guide the differential privacy budget allocation called APBA adaptively. Finally, all DP-based defenses with MDP could dynamically adjust the amount of noise added in the model response according to the result from Monitor and effectively defends the QPD attack. Furthermore, we thoroughly evaluate and compare the QPD attack and MDP defense performance on real-world models with DP and monitoring protection. Copyright © 2020, The Authors. All rights reserved.

关键词： Budget control

Pinching-Antenna Systems (PASS)-enabled Secure Wireless Communications

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arXiv 2025年

作者： Zhu, Guangyu Mu, Xidong Guo, Li Xu, Shibiao Liu, Yuanwei Al-Dhahir, Naofal Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China National Engineering Research Center for Mobile Internet Security Technology Beijing University of Posts and Telecommunications Beijing100876 China Queen’s University Belfast BelfastBT3 9DT United Kingdom Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

A novel pinching-antenna systems (PASS)-enabled secure wireless communication framework is proposed. By dynamically adjusting the positions of dielectric particles, namely pinching antennas (PAs), along the waveguides, PASS introduces a novel concept of pinching beamforming to enhance the performance of physical layer security. A fundamental PASS-enabled secure communication system is considered with one legitimate user and one eavesdropper. Both single-waveguide and multiple-waveguide scenarios are studied. 1) For the single-waveguide scenario, the secrecy rate (SR) maximization is formulated to optimize the pinching beamforming. A PA-wise successive tuning (PAST) algorithm is proposed, which ensures constructive signal superposition at the legitimate user while inducing a destructive legitimate signal at the eavesdropper. 2) For the multiple-waveguide scenario, artificial noise (AN) is employed to further improve secrecy performance. A pair of practical transmission architectures are developed: waveguide division (WD) and waveguide multiplexing (WM). The key difference lies in whether each waveguide carries a single type of signal or a mixture of signals with baseband beamforming. For the SR maximization problem under the WD case, a two-stage algorithm is developed, where the pinching beamforming is designed with the PAST algorithm and the baseband power allocation among AN and legitimate signals is solved using successive convex approximation (SCA). For the WM case, an alternating optimization algorithm is developed, where the baseband beamforming is optimized with SCA and the pinching beamforming is designed employing particle swarm optimization. Numerical results demonstrate that i) PASS can significantly improve the secrecy performance over conventional antenna systems in both scenarios;ii) the proposed PAST algorithm for the single-waveguide scenario is efficient, especially when the number of PAs is even or large;iii) WM provides higher and more stable perfo

关键词： Particle swarm optimization (PSO)

Targeted Poisoning Attacks on Social Recommender Systems

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Targeted Poisoning Attacks on Social Recommender Systems

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Rui Hu Yuanxiong Guo Miao Pan Yanmin Gong Department of Electrical and Computer Engineering University of Texas at San Antonio San Antonio TX Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio TX Department of Electrical and Computer Engineering University of Houston Houston TX

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

With the popularity of online social networks, social recommendations that rely on ones social connections to make personalized recommendations have become possible. This introduces vulnerabilities for an adversarial party to compromise the recommendations for users by utilizing their social connections. In this paper, we propose the targeted poisoning attack on the factorization-based social recommender system in which the attacker aims to promote an item to a group of target users by injecting fake ratings and social connections. We formulate the optimal poisoning attack as a bi-level program and develop an efficient algorithm to find the optimal attacking strategy. We then evaluate the proposed attacking strategy on real-world dataset and demonstrate that the social recommender system is sensitive to the targeted poisoning attack. We find that users in the social recommender system can be attacked even if they do not have direct social connections with the attacker.

关键词： Recommender systems Social network services Optimization computer security Motion pictures Sparse matrices Information systems

Lightweight security solutions for IoT implementations in space

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Lightweight security solutions for IoT implementations in sp...

Topical Workshop on Internet of Space (TWIOS)

作者： Nikolaos Athanasios Anagnostopoulos Yufan Fan Tolga Arul Ravi Sarangdhar Stefan Katzenbeisser Security Engineering Group Computer Science Department TU Darmstadt Germany Department of Electrical Engineering and Information Technology TU Darmstadt Germany Computer Science Department TU Darmstadt Germany

In recent years, Commercial Off-The-Shelf (COTS) devices have been gaining a foothold in space applications, due to their relatively low cost. Such devices can be used to implement a segment of the Internet of Things (IoT) in space. Nevertheless, as the cost of space missions still remains relatively high and space applications can potentially have significant effects on a global scale, these devices need to be adequately secured. However, in order to keep the competitive advantages of using COTS devices in space applications, security solutions proposed for these devices should be cost-efficient and lightweight, while remaining practical and flexible. To this end, in this work, we examine whether schemes based on physical unclonable functions, which have these characteristics, can be used in the adverse conditions of space. In particular, we show that neither ambient temperature changes nor radiation can thwart the use of intrinsic memory-based PUFs for enhancing the security of IoT devices being employed in space applications.

关键词： Random access memory security Temperature measurement Temperature sensors Temperature dependence Internet of Things

VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT

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arXiv 2020年

作者： Fu, Anmin Zhang, Xianglong Xiong, Naixue Gao, Yansong Wang, Huaqun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Northeastern State University Department of Mathematics and Computer Science TahlequahOK United States Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

Due to the strong analytical ability of big data, deep learning has been widely applied to model the collected data in industrial IoT. However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets. Recently, federated learning has received widespread attention, since it trains a model by only relying on gradient aggregation without accessing training sets. But existing researches reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this paper, we propose the VFL, verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the gradients submitted by the participants. If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL framework with high accuracy and efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Quasi-linear theory of forced magnetic reconnection for the transition from linear to Rutherford regime

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arXiv 2020年

作者： Huang, Wenlong Zhu, Ping School of Computer Science and Technology Anhui Engineering Laboratory for Industrial Internet Intelligent Applications Security Anhui University of Technology Anhui Ma’anshan243002 China International Joint Research Laboratory of Magnetic Confinement Fusion Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering Technology School of Electrical and Electronic Engineering Huazhong University ofScience and Technology Hubei Wuhan430074 China Department of Engineering Physics University of Wisconsin-Madison MadisonWI53706 United States

Using the in-viscid two-field reduced MHD model, a new analytical theory is developed to unify the Hahm-Kulsrud-Taylor (HKT) linear solution and the Rutherford quasi-linear regime. Adopting a quasi-linear approach, we obtain a closed system of equations for plasma response in a static plasma in slab geometry. An integral form of analytical solution is obtained for the forced magnetic reconnection, uniformly valid throughout the entire regimes from the HKT linear solution to the Rutherford quasi-linear solution. In particular, the quasi-linear effect can be described by a single coefficient Ks ∝ S8/5ψc2, where S = ττAR and ψc are the Lunquist number and amplitude of external magnetic perturbation, respectively. The HKT linear solution for response can be recovered when the index Ks → 0. On the other hand, the quasilinear effect plays a key role in the island growth when Ks ∼ 1. Our new analytical solution has also been compared with reduced MHD simulations with agreement. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetoplasma