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Yuying Wang, Xingshe Zhou.  2014.  Spatio-temporal semantic enhancements for event model of cyber-physical systems. Signal Processing, Communications and Computing (ICSPCC), 2014 IEEE International Conference on. :813-818.

The newly emerging cyber-physical systems (CPS) discover events from multiple, distributed sources with multiple levels of detail and heterogeneous data format, which may not be compare and integrate, and turn to hardly combined determination for action. While existing efforts have mainly focused on investigating a uniform CPS event representation with spatio-temporal attributes, in this paper we propose a new event model with two-layer structure, Basic Event Model (BEM) and Extended Information Set (EIS). A BEM could be extended with EIS by semantic adaptor for spatio-temporal and other attribution enhancement. In particular, we define the event process functions, like event attribution extraction and composition determination, for CPS action trigger exploit the Complex Event Process (CEP) engine Esper. Examples show that such event model provides several advantages in terms of extensibility, flexibility and heterogeneous support, and lay the foundations of event-based system design in CPS.
 

Yuxi Liu, Hatzinakos, D..  2014.  Human acoustic fingerprints: A novel biometric modality for mobile security. Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. :3784-3788.

Recently, the demand for more robust protection against unauthorized use of mobile devices has been rapidly growing. This paper presents a novel biometric modality Transient Evoked Otoacoustic Emission (TEOAE) for mobile security. Prior works have investigated TEOAE for biometrics in a setting where an individual is to be identified among a pre-enrolled identity gallery. However, this limits the applicability to mobile environment, where attacks in most cases are from imposters unknown to the system before. Therefore, we employ an unsupervised learning approach based on Autoencoder Neural Network to tackle such blind recognition problem. The learning model is trained upon a generic dataset and used to verify an individual in a random population. We also introduce the framework of mobile biometric system considering practical application. Experiments show the merits of the proposed method and system performance is further evaluated by cross-validation with an average EER 2.41% achieved.

Yuxi Liu, Hatzinakos, D..  2014.  Earprint: Transient Evoked Otoacoustic Emission for Biometrics. Information Forensics and Security, IEEE Transactions on. 9:2291-2301.

Biometrics is attracting increasing attention in privacy and security concerned issues, such as access control and remote financial transaction. However, advanced forgery and spoofing techniques are threatening the reliability of conventional biometric modalities. This has been motivating our investigation of a novel yet promising modality transient evoked otoacoustic emission (TEOAE), which is an acoustic response generated from cochlea after a click stimulus. Unlike conventional modalities that are easily accessible or captured, TEOAE is naturally immune to replay and falsification attacks as a physiological outcome from human auditory system. In this paper, we resort to wavelet analysis to derive the time-frequency representation of such nonstationary signal, which reveals individual uniqueness and long-term reproducibility. A machine learning technique linear discriminant analysis is subsequently utilized to reduce intrasubject variability and further capture intersubject differentiation features. Considering practical application, we also introduce a complete framework of the biometric system in both verification and identification modes. Comparative experiments on a TEOAE data set of biometric setting show the merits of the proposed method. Performance is further improved with fusion of information from both ears.

Yusuf, S. E., Ge, M., Hong, J. B., Alzaid, H., Kim, D. S..  2017.  Evaluating the Effectiveness of Security Metrics for Dynamic Networks. 2017 IEEE Trustcom/BigDataSE/ICESS. :277–284.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

Yusuf, S. E., Ge, M., Hong, J. B., Alzaid, H., Kim, D. S..  2017.  Evaluating the Effectiveness of Security Metrics for Dynamic Networks. 2017 IEEE Trustcom/BigDataSE/ICESS. :277–284.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

Yusuf, S. E., Ge, M., Hong, J. B., Alzaid, H., Kim, D. S..  2017.  Evaluating the Effectiveness of Security Metrics for Dynamic Networks. 2017 IEEE Trustcom/BigDataSE/ICESS. :277–284.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

Yusof, M., Saudi, M. M., Ridzuan, F..  2017.  A New Mobile Botnet Classification Based on Permission and API Calls. 2017 Seventh International Conference on Emerging Security Technologies (EST). :122–127.

Currently, mobile botnet attacks have shifted from computers to smartphones due to its functionality, ease to exploit, and based on financial intention. Mostly, it attacks Android due to its popularity and high usage among end users. Every day, more and more malicious mobile applications (apps) with the botnet capability have been developed to exploit end users' smartphones. Therefore, this paper presents a new mobile botnet classification based on permission and Application Programming Interface (API) calls in the smartphone. This classification is developed using static analysis in a controlled lab environment and the Drebin dataset is used as the training dataset. 800 apps from the Google Play Store have been chosen randomly to test the proposed classification. As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. The experimental result shows that the Random Forest Algorithm has achieved the highest detection accuracy of 99.4% with the lowest false positive rate of 16.1% as compared to other machine learning algorithms. This new classification can be used as the input for mobile botnet detection for future work, especially for financial matters.

Yusheng, W., Kefeng, F., Yingxu, L., Zenghui, L., Ruikang, Z., Xiangzhen, Y., Lin, L..  2017.  Intrusion Detection of Industrial Control System Based on Modbus TCP Protocol. 2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS). :156–162.

Modbus over TCP/IP is one of the most popular industrial network protocol that are widely used in critical infrastructures. However, vulnerability of Modbus TCP protocol has attracted widely concern in the public. The traditional intrusion detection methods can identify some intrusion behaviors, but there are still some problems. In this paper, we present an innovative approach, SD-IDS (Stereo Depth IDS), which is designed for perform real-time deep inspection for Modbus TCP traffic. SD-IDS algorithm is composed of two parts: rule extraction and deep inspection. The rule extraction module not only analyzes the characteristics of industrial traffic, but also explores the semantic relationship among the key field in the Modbus TCP protocol. The deep inspection module is based on rule-based anomaly intrusion detection. Furthermore, we use the online test to evaluate the performance of our SD-IDS system. Our approach get a low rate of false positive and false negative.

Yunfeng Zhu, Lee, P.P.C., Yinlong Xu, Yuchong Hu, Liping Xiang.  2014.  On the Speedup of Recovery in Large-Scale Erasure-Coded Storage Systems. Parallel and Distributed Systems, IEEE Transactions on. 25:1830-1840.

Modern storage systems stripe redundant data across multiple nodes to provide availability guarantees against node failures. One form of data redundancy is based on XOR-based erasure codes, which use only XOR operations for encoding and decoding. In addition to tolerating failures, a storage system must also provide fast failure recovery to reduce the window of vulnerability. This work addresses the problem of speeding up the recovery of a single-node failure for general XOR-based erasure codes. We propose a replace recovery algorithm, which uses a hill-climbing technique to search for a fast recovery solution, such that the solution search can be completed within a short time period. We further extend the algorithm to adapt to the scenario where nodes have heterogeneous capabilities (e.g., processing power and transmission bandwidth). We implement our replace recovery algorithm atop a parallelized architecture to demonstrate its feasibility. We conduct experiments on a networked storage system testbed, and show that our replace recovery algorithm uses less recovery time than the conventional recovery approach.
 

Yun Shen, Thonnard, O..  2014.  MR-TRIAGE: Scalable multi-criteria clustering for big data security intelligence applications. Big Data (Big Data), 2014 IEEE International Conference on. :627-635.

Security companies have recently realised that mining massive amounts of security data can help generate actionable intelligence and improve their understanding of Internet attacks. In particular, attack attribution and situational understanding are considered critical aspects to effectively deal with emerging, increasingly sophisticated Internet attacks. This requires highly scalable analysis tools to help analysts classify, correlate and prioritise security events, depending on their likely impact and threat level. However, this security data mining process typically involves a considerable amount of features interacting in a non-obvious way, which makes it inherently complex. To deal with this challenge, we introduce MR-TRIAGE, a set of distributed algorithms built on MapReduce that can perform scalable multi-criteria data clustering on large security data sets and identify complex relationships hidden in massive datasets. The MR-TRIAGE workflow is made of a scalable data summarisation, followed by scalable graph clustering algorithms in which we integrate multi-criteria evaluation techniques. Theoretical computational complexity of the proposed parallel algorithms are discussed and analysed. The experimental results demonstrate that the algorithms can scale well and efficiently process large security datasets on commodity hardware. Our approach can effectively cluster any type of security events (e.g., spam emails, spear-phishing attacks, etc) that are sharing at least some commonalities among a number of predefined features.
 

Yulianto, Arief Dwi, Sukarno, Parman, Warrdana, Aulia Arif, Makky, Muhammad Al.  2019.  Mitigation of Cryptojacking Attacks Using Taint Analysis. 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). :234—238.

Cryptojacking (also called malicious cryptocurrency mining or cryptomining) is a new threat model using CPU resources covertly “mining” a cryptocurrency in the browser. The impact is a surge in CPU Usage and slows the system performance. In this research, in-browsercryptojacking mitigation has been built as an extension in Google Chrome using Taint analysis method. The method used in this research is attack modeling with abuse case using the Man-In-The-Middle (MITM) attack as a testing for mitigation. The proposed model is designed so that users will be notified if a cryptojacking attack occurs. Hence, the user is able to check the script characteristics that run on the website background. The results of this research show that the taint analysis is a promising method to mitigate cryptojacking attacks. From 100 random sample websites, the taint analysis method can detect 19 websites that are infcted by cryptojacking.

Yuliana, Mike, Suwadi, Wirawan.  2020.  Key Rate Enhancement by Using the Interval Approach in Symmetric Key Extraction Mechanism. 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE). :1–6.
Wireless security is confronted with the complexity of the secret key distribution process, which is difficult to implement on an Ad Hoc network without a key management infrastructure. The symmetric key extraction mechanism from a response channel in a wireless environment is a very promising alternative solution with the simplicity of the key distribution process. Various mechanisms have been proposed for extracting the symmetric key, but many mechanisms produce low rates of the symmetric key due to the high bit differences that occur. This led to the fact that the reconciliation phase was unable to make corrections, as a result of which many key bits were lost, and the time required to obtain a symmetric key was increased. In this paper, we propose the use of an interval approach that divides the response channel into segments at specific intervals to reduce the key bit difference and increase the key rates. The results of tests conducted in the wireless environment show that the use of these mechanisms can increase the rate of the keys up to 35% compared to existing mechanisms.
Yüksel, Ömer, den Hartog, Jerry, Etalle, Sandro.  2016.  Reading Between the Fields: Practical, Effective Intrusion Detection for Industrial Control Systems. Proceedings of the 31st Annual ACM Symposium on Applied Computing. :2063–2070.

Detection of previously unknown attacks and malicious messages is a challenging problem faced by modern network intrusion detection systems. Anomaly-based solutions, despite being able to detect unknown attacks, have not been used often in practice due to their high false positive rate, and because they provide little actionable information to the security officer in case of an alert. In this paper we focus on intrusion detection in industrial control systems networks and we propose an innovative, practical and semantics-aware framework for anomaly detection. The network communication model and alerts generated by our framework are userunderstandable, making them much easier to manage. At the same time the framework exhibits an excellent tradeoff between detection rate and false positive rate, which we show by comparing it with two existing payload-based anomaly detection methods on several ICS datasets.

Yugha, R., Chithra, S..  2019.  Attribute Based Trust Evaluation for Secure RPL Protocol in IoT Environment. 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN). :1–7.
Internet of Things (IoT) is an advanced automation technology and analytics systems which connected physical objects that have access through the Internet and have their unique flexibility and an ability to be suitable for any environment. There are some critical applications like smart health care system, in which the data collection, sharing and routing through IoT has to be handled in sensitive way. The IPv6 Routing Protocol for LL(Low-power and Lossy) networks (RPL) is the routing protocols to ensure reliable data transfer in 6LOWPAN networks. However, RPL is vulnerable to number of security attacks which creates a major impact on energy consumption and memory requirements which is not suitable for energy constraint networks like IoT. This requires secured RPL protocol to be used for critical data transfer. This paper introduces a novel approach of combining a lightweight LBS (Location Based Service) authentication and Attribute Based Trust Evaluation (ABTE). The algorithm has been implemented for smart health care system and analyzed how its perform in the RPL protocol for IoT constrained environments.
Yufei Gu, Yangchun Fu, Prakash, A., Zhiqiang Lin, Heng Yin.  2014.  Multi-Aspect, Robust, and Memory Exclusive Guest OS Fingerprinting. Cloud Computing, IEEE Transactions on. 2:380-394.

Precise fingerprinting of an operating system (OS) is critical to many security and forensics applications in the cloud, such as virtual machine (VM) introspection, penetration testing, guest OS administration, kernel dump analysis, and memory forensics. The existing OS fingerprinting techniques primarily inspect network packets or CPU states, and they all fall short in precision and usability. As the physical memory of a VM always exists in all these applications, in this article, we present OS-SOMMELIER+, a multi-aspect, memory exclusive approach for precise and robust guest OS fingerprinting in the cloud. It works as follows: given a physical memory dump of a guest OS, OS-SOMMELIER+ first uses a code hash based approach from kernel code aspect to determine the guest OS version. If code hash approach fails, OS-SOMMELIER+ then uses a kernel data signature based approach from kernel data aspect to determine the version. We have implemented a prototype system, and tested it with a number of Linux kernels. Our evaluation results show that the code hash approach is faster but can only fingerprint the known kernels, and data signature approach complements the code signature approach and can fingerprint even unknown kernels.

Yufan Huang, Xiaofan He, Huaiyu Dai.  2015.  Poster: Systematization of Metrics in Intrusion Detection Systems. ACM Proc. Of the Symposium and Bootcamp on the Science of Security (HotSoS), University of Illinois at Urbana-Champaign, IL.
Yufan Huang, Xiaofan He, Huaiyu Dai.  2015.  Poster: Systematization of Metrics in Intrusion Detection Systems. ACM Proc. Of the Symposium and Bootcamp on the Science of Security (HotSoS), University of Illinois at Urbana-Champaign, IL.
Yueying Huang, Jingang Zhang, Houyan Chen.  2014.  On the security of a certificateless signcryption scheme. Electronics, Computer and Applications, 2014 IEEE Workshop on. :664-667.

Signcryption is a cryptographic primitive that simultaneously realizes both the functions of public key encryption and digital signature in a logically single step, and with a cost significantly lower than that required by the traditional “signature and encryption” approach. Recently, an efficient certificateless signcryption scheme without using bilinear pairings was proposed by Zhu et al., which is claimed secure based on the assumptions that the compute Diffie-Hellman problem and the discrete logarithm problem are difficult. Although some security arguments were provided to show the scheme is secure, in this paper, we find that the signcryption construction due to Zhu et al. is not as secure as claimed. Specifically, we describe an adversary that can break the IND-CCA2 security of the scheme without any Unsigncryption query. Moreover, we demonstrate that the scheme is insecure against key replacement attack by describing a concrete attack approach.
 

Yuen, W. P., Chuah, K. B..  2018.  Development of the Customer Centric Data Visibility Framework for the Enhancement of the Trust of SME Customers in Cloud Services. Proceedings of the 6th International Conference on Information and Education Technology. :221–225.
Cloud computing is a pervasive technology and platform in IT for several years. Cloud service providers have developed and offered different service platforms to accommodate different needs of enterprise subscribers. However, there still exists the situation of enterprise customers' hesitation and reluctance to deploy their core applications using cloud service platforms. The term data visibility has been widely used in the IT industry especially from ICT product and solution vendors. However, there is not any practice guideline, nor standard in industry to define this term. This paper defined the characteristic and dimensions of data visibility, from conceptual model to framework architecture of customer centric data visibility (CCDV) on cloud platform. It propose to apply CCDV as reference model or practice guideline on cloud computing service, with enhancement of data visibility which can earn the trust from enterprise customer in adopting public cloud service.
Yueguo Zhang, Lili Dong, Shenghong Li, Jianhua Li.  2014.  Abnormal crowd behavior detection using interest points. Broadband Multimedia Systems and Broadcasting (BMSB), 2014 IEEE International Symposium on. :1-4.

Abnormal crowd behavior detection is an important research issue in video processing and computer vision. In this paper we introduce a novel method to detect abnormal crowd behaviors in video surveillance based on interest points. A complex network-based algorithm is used to detect interest points and extract the global texture features in scenarios. The performance of the proposed method is evaluated on publicly available datasets. We present a detailed analysis of the characteristics of the crowd behavior in different density crowd scenes. The analysis of crowd behavior features and simulation results are also demonstrated to illustrate the effectiveness of our proposed method.

Yue, Tongxu, Wang, Chuang, Zhu, Zhi-xiang.  2019.  Hybrid Encryption Algorithm Based on Wireless Sensor Networks. 2019 IEEE International Conference on Mechatronics and Automation (ICMA). :690–694.
Based on the analysis of existing wireless sensor networks(WSNs) security vulnerability, combining the characteristics of high encryption efficiency of the symmetric encryption algorithm and high encryption intensity of asymmetric encryption algorithm, a hybrid encryption algorithm based on wireless sensor networks is proposed. Firstly, by grouping plaintext messages, this algorithm uses advanced encryption standard (AES) of symmetric encryption algorithm and elliptic curve encryption (ECC) of asymmetric encryption algorithm to encrypt plaintext blocks, then uses data compression technology to get cipher blocks, and finally connects MAC address and AES key encrypted by ECC to form a complete ciphertext message. Through the description and implementation of the algorithm, the results show that the algorithm can reduce the encryption time, decryption time and total running time complexity without losing security.
Yue, Lu, Yao, Xiu.  2019.  Sub-Modular Circuit Design for Self-Balancing Series-Connected IGBTs in a Modular Multilevel Converter. 2019 IEEE Applied Power Electronics Conference and Exposition (APEC). :3448–3452.

Series-connected IGBTs, when properly controlled, operate similarly to a single device with a much higher voltage capacity. Integrating series IGBTs into a Modular Multilevel Converter (MMC) can reduce its complexity without compromising the voltage capacity. This paper presents the circuit design on the sub-modular level of a MMC in which all the switching devices are series-connected IGBTs. The voltage sharing among the series IGBTs are regulated in a self-balancing manner. Therefore, no central series IGBT controller is needed, which greatly reduces the sensing and communication complexities, increasing the flexibility and expandability. Hardware experiment results demonstrate that the series IGBTs are able to self-regulate the voltage sharing in a fast and accurate manner and the system can operate similarly to a sub-module in a MMC.

Yue, L., Junqin, H., Shengzhi, Q., Ruijin, W..  2017.  Big Data Model of Security Sharing Based on Blockchain. 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM). :117–121.

The rise of big data age in the Internet has led to the explosive growth of data size. However, trust issue has become the biggest problem of big data, leading to the difficulty in data safe circulation and industry development. The blockchain technology provides a new solution to this problem by combining non-tampering, traceable features with smart contracts that automatically execute default instructions. In this paper, we present a credible big data sharing model based on blockchain technology and smart contract to ensure the safe circulation of data resources.

Yue-Bin Luo, Bao-Sheng Wang, Gui-Lin Cai.  2014.  Effectiveness of Port Hopping as a Moving Target Defense. Security Technology (SecTech), 2014 7th International Conference on. :7-10.

Port hopping is a typical moving target defense, which constantly changes service port number to thwart reconnaissance attack. It is effective in hiding service identities and confusing potential attackers, but it is still unknown how effective port hopping is and under what circumstances it is a viable proactive defense because the existed works are limited and they usually discuss only a few parameters and give some empirical studies. This paper introduces urn model and quantifies the likelihood of attacker success in terms of the port pool size, number of probes, number of vulnerable services, and hopping frequency. Theoretical analysis shows that port hopping is an effective and promising proactive defense technology in thwarting network attacks.
 

Yudin, Oleksandr, Ziubina, Ruslana, Buchyk, Serhii, Frolov, Oleg, Suprun, Olha, Barannik, Natalia.  2019.  Efficiency Assessment of the Steganographic Coding Method with Indirect Integration of Critical Information. 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT). :36—40.
The presented method of encoding and steganographic embedding of a series of bits for the hidden message was first developed by modifying the digital platform (bases) of the elements of the image container. Unlike other methods, steganographic coding and embedding is accomplished by changing the elements of the image fragment, followed by the formation of code structures for the established structure of the digital representation of the structural elements of the image media image. The method of estimating quantitative indicators of embedded critical data is presented. The number of bits of the container for the developed method of steganographic coding and embedding of critical information is estimated. The efficiency of the presented method is evaluated and the comparative analysis of the value of the embedded digital data in relation to the method of weight coefficients of the discrete cosine transformation matrix, as well as the comparative analysis of the developed method of steganographic coding, compared with the Koch and Zhao methods to determine the embedded data resistance against attacks of various types. It is determined that for different values of the quantization coefficient, the most critical are the built-in containers of critical information, which are built by changing the part of the digital video data platform depending on the size of the digital platform and the number of bits of the built-in container.