Visible to the public Biblio

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Zhang, J., Liao, Y., Zhu, X., Wang, H., Ding, J..  2020.  A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics. IEEE Signal Processing Letters. 27:276—280.
This letter presents a novel median filtering forensics approach, based on a convolutional neural network (CNN) with an adaptive filtering layer (AFL), which is built in the discrete cosine transform (DCT) domain. Using the proposed AFL, the CNN can determine the main frequency range closely related with the operational traces. Then, to automatically learn the multi-scale manipulation features, a multi-scale convolutional block is developed, exploring a new multi-scale feature fusion strategy based on the maxout function. The resultant features are further processed by a convolutional stream with pooling and batch normalization operations, and finally fed into the classification layer with the Softmax function. Experimental results show that our proposed approach is able to accurately detect the median filtering manipulation and outperforms the state-of-the-art schemes, especially in the scenarios of low image resolution and serious compression loss.
Morozov, M. Y., Perfilov, O. Y., Malyavina, N. V., Teryokhin, R. V., Chernova, I. V..  2020.  Combined Approach to SSDF-Attacks Mitigation in Cognitive Radio Networks. 2020 Systems of Signals Generating and Processing in the Field of on Board Communications. :1–4.
Cognitive radio systems aim to solve the issue of spectrum scarcity through implementation of dynamic spectrum management and cooperative spectrum access. However, the structure of such systems introduced unique types of vulnerabilities and attacks, one of which is spectrum sensing data falsification attack (SSDF). In such attacks malicious users provide incorrect observations to the fusion center of the system, which may result in severe quality of service degradation and interference for licensed users. In this paper we investigate this type of attacks and propose a combined approach to their mitigation. On the first step a reputational method is used to isolate the initially untrustworthy nodes, on the second step specialized q-out-of-m fusion rule is utilized to mitigate the remains of attack. In this paper we present theoretical analysis of the proposed combined method.
Han, Z., Wang, F., Li, Z..  2020.  Research on Nearest Neighbor Data Association Algorithm Based on Target “Dynamic” Monitoring Model. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:665–668.
In order to solve the problem that the Nearest Neighbor Data Association (NNDA) algorithm cannot detect the “dynamic” change of the target, this paper proposes the nearest neighbor data association algorithm based on the Targets “Dynamic” Monitoring Model (TDMM). Firstly, the gate searching and updating of targets are completed based on TDMM, then the NNDA algorithm is utilized to achieve the data association of targets to realize track updating. Finally, the NNDA algorithm based on TDMM is realized by simulation. The experimental results show that the algorithm proposed can achieve “dynamic” monitoring in multi-target data association, and have more obvious advantages than Multiple Hypothesis Tracking (MHT) in timeliness and association performance.
Kowalski, P., Zocholl, M., Jousselme, A.-L..  2020.  Explainability in threat assessment with evidential networks and sensitivity spaces. 2020 IEEE 23rd International Conference on Information Fusion (FUSION). :1—8.
One of the main threats to the underwater communication cables identified in the recent years is possible tampering or damage by malicious actors. This paper proposes a solution with explanation abilities to detect and investigate this kind of threat within the evidence theory framework. The reasoning scheme implements the traditional “opportunity-capability-intent” threat model to assess a degree to which a given vessel may pose a threat. The scenario discussed considers a variety of possible pieces of information available from different sources. A source quality model is used to reason with the partially reliable sources and the impact of this meta-information on the overall assessment is illustrated. Examples of uncertain relationships between the relevant variables are modelled and the constructed model is used to investigate the probability of threat of four vessels of different types. One of these cases is discussed in more detail to demonstrate the explanation abilities. Explanations about inference are provided thanks to sensitivity spaces in which the impact of the different pieces of information on the reasoning are compared.
Figueiredo, N. M., Rodríguez, M. C..  2020.  Trustworthiness in Sensor Networks A Reputation-Based Method for Weather Stations. 2020 International Conference on Omni-layer Intelligent Systems (COINS). :1–6.
Trustworthiness is a soft-security feature that evaluates the correct behavior of nodes in a network. More specifically, this feature tries to answer the following question: how much should we trust in a certain node? To determine the trustworthiness of a node, our approach focuses on two reputation indicators: the self-data trust, which evaluates the data generated by the node itself taking into account its historical data; and the peer-data trust, which utilizes the nearest nodes' data. In this paper, we show how these two indicators can be calculated using the Gaussian Overlap and Pearson correlation. This paper includes a validation of our trustworthiness approach using real data from unofficial and official weather stations in Portugal. This is a representative scenario of the current situation in many other areas, with different entities providing different kinds of data using autonomous sensors in a continuous way over the networks.
Huang, Y., Wang, Y..  2019.  Multi-format speech perception hashing based on time-frequency parameter fusion of energy zero ratio and frequency band variance. 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). :243—251.

In order to solve the problems of the existing speech content authentication algorithm, such as single format, ununiversal algorithm, low security, low accuracy of tamper detection and location in small-scale, a multi-format speech perception hashing based on time-frequency parameter fusion of energy zero ratio and frequency band bariance is proposed. Firstly, the algorithm preprocesses the processed speech signal and calculates the short-time logarithmic energy, zero-crossing rate and frequency band variance of each speech fragment. Then calculate the energy to zero ratio of each frame, perform time- frequency parameter fusion on time-frequency features by mean filtering, and the time-frequency parameters are constructed by difference hashing method. Finally, the hash sequence is scrambled with equal length by logistic chaotic map, so as to improve the security of the hash sequence in the transmission process. Experiments show that the proposed algorithm is robustness, discrimination and key dependent.

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.
Islam, M. M., Karmakar, G., Kamruzzaman, J., Murshed, M..  2019.  Measuring Trustworthiness of IoT Image Sensor Data Using Other Sensors’ Complementary Multimodal Data. 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). :775–780.
Trust of image sensor data is becoming increasingly important as the Internet of Things (IoT) applications grow from home appliances to surveillance. Up to our knowledge, there exists only one work in literature that estimates trustworthiness of digital images applied to forensic applications, based on a machine learning technique. The efficacy of this technique is heavily dependent on availability of an appropriate training set and adequate variation of IoT sensor data with noise, interference and environmental condition, but availability of such data cannot be assured always. Therefore, to overcome this limitation, a robust method capable of estimating trustworthy measure with high accuracy is needed. Lowering cost of sensors allow many IoT applications to use multiple types of sensors to observe the same event. In such cases, complementary multimodal data of one sensor can be exploited to measure trust level of another sensor data. In this paper, for the first time, we introduce a completely new approach to estimate the trustworthiness of an image sensor data using another sensor's numerical data. We develop a theoretical model using the Dempster-Shafer theory (DST) framework. The efficacy of the proposed model in estimating trust level of an image sensor data is analyzed by observing a fire event using IoT image and temperature sensor data in a residential setup under different scenarios. The proposed model produces highly accurate trust level in all scenarios with authentic and forged image data.
Qian, Y..  2019.  Research on Trusted Authentication Model and Mechanism of Data Fusion. 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). :479–482.
Firstly, this paper analyses the technical foundation of single sign-on solution of unified authentication platform, and analyses the advantages and disadvantages of each solution. Secondly, from the point of view of software engineering, such as function requirement, performance requirement, development mode, architecture scheme, technology development framework and system configuration environment of the unified authentication platform, the unified authentication platform is analyzed and designed, and the database design and system design framework of the system are put forward according to the system requirements. Thirdly, the idea and technology of unified authentication platform based on JA-SIG CAS are discussed, and the design and implementation of each module of unified authentication platform based on JA-SIG CAS are analyzed, which has been applied in ship cluster platform.
Ramapatruni, S., Narayanan, S. N., Mittal, S., Joshi, A., Joshi, K..  2019.  Anomaly Detection Models for Smart Home Security. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :19–24.
Recent years have seen significant growth in the adoption of smart homes devices. These devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized movements, and smoke sensors can detect potential fire accidents. However, many recent examples have shown that they open up a new cyber threat surface. There have been several recent examples of smart devices being hacked for privacy violations and also misused so as to perform DDoS attacks. In this paper, we explore the application of big data and machine learning to identify anomalous activities that can occur in a smart home environment. A Hidden Markov Model (HMM) is trained on network level sensor data, created from a test bed with multiple sensors and smart devices. The generated HMM model is shown to achieve an accuracy of 97% in identifying potential anomalies that indicate attacks. We present our approach to build this model and compare with other techniques available in the literature.
Huyck, P..  2019.  Safe and Secure Data Fusion — Use of MILS Multicore Architecture to Reduce Cyber Threats. 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC). :1–9.
Data fusion, as a means to improve aircraft and air traffic safety, is a recent focus of some researchers and system developers. Increases in data volume and processing needs necessitate more powerful hardware and more flexible software architectures to satisfy these needs. Such improvements in processed data also mean the overall system becomes more complex and correspondingly, resulting in a potentially significantly larger cyber-attack space. Today's multicore processors are one means of satisfying the increased computational needs of data fusion-based systems. When coupled with a real-time operating system (RTOS) capable of flexible core and application scheduling, large cabinets of (power hungry) single-core processors may be avoided. The functional and assurance capabilities of such an RTOS can be critical elements in providing application isolation, constrained data flows, and restricted hardware access (including covert channel prevention) necessary to reduce the overall cyber-attack space. This paper examines fundamental considerations of a multiple independent levels of security (MILS) architecture when supported by a multicore-based real-time operating system. The paper draws upon assurance activities and functional properties associated with a previous Common Criteria evaluation assurance level (EAL) 6+ / High-Robustness Separation Kernel certification effort and contrast those with activities performed as part of a MILS multicore related project. The paper discusses key characteristics and functional capabilities necessary to achieve overall system security and safety. The paper defines architectural considerations essential for scheduling applications on a multicore processor to reduce security risks. For civil aircraft systems, the paper discusses the applicability of the security assurance and architecture configurations to system providers looking to increase their resilience to cyber threats.
Xie, Kun, Li, Xiaocan, Wang, Xin, Xie, Gaogang, Xie, Dongliang, Li, Zhenyu, Wen, Jigang, Diao, Zulong.  2019.  Quick and Accurate False Data Detection in Mobile Crowd Sensing. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :2215—2223.

With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.

Zhou, Xingyu, Li, Yi, Barreto, Carlos A., Li, Jiani, Volgyesi, Peter, Neema, Himanshu, Koutsoukos, Xenofon.  2019.  Evaluating Resilience of Grid Load Predictions under Stealthy Adversarial Attacks. 2019 Resilience Week (RWS). 1:206–212.
Recent advances in machine learning enable wider applications of prediction models in cyber-physical systems. Smart grids are increasingly using distributed sensor settings for distributed sensor fusion and information processing. Load forecasting systems use these sensors to predict future loads to incorporate into dynamic pricing of power and grid maintenance. However, these inference predictors are highly complex and thus vulnerable to adversarial attacks. Moreover, the adversarial attacks are synthetic norm-bounded modifications to a limited number of sensors that can greatly affect the accuracy of the overall predictor. It can be much cheaper and effective to incorporate elements of security and resilience at the earliest stages of design. In this paper, we demonstrate how to analyze the security and resilience of learning-based prediction models in power distribution networks by utilizing a domain-specific deep-learning and testing framework. This framework is developed using DeepForge and enables rapid design and analysis of attack scenarios against distributed smart meters in a power distribution network. It runs the attack simulations in the cloud backend. In addition to the predictor model, we have integrated an anomaly detector to detect adversarial attacks targeting the predictor. We formulate the stealthy adversarial attacks as an optimization problem to maximize prediction loss while minimizing the required perturbations. Under the worst-case setting, where the attacker has full knowledge of both the predictor and the detector, an iterative attack method has been developed to solve for the adversarial perturbation. We demonstrate the framework capabilities using a GridLAB-D based power distribution network model and show how stealthy adversarial attacks can affect smart grid prediction systems even with a partial control of network.
Simpson, Oluyomi, Sun, Yichuang.  2019.  A Stochastic based Physical Layer Security in Cognitive Radio Networks: Cognitive Relay to Fusion Center. 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC). :1—7.
Cognitive radio networks (CRNs) are found to be, without difficulty wide-open to external malicious threats. Secure communication is an important prerequisite for forthcoming fifth-generation (5G) systems, and CRs are not exempt. A framework for developing the accomplishable benefits of physical layer security (PLS) in an amplify-and-forward cooperative spectrum sensing (AF-CSS) in a cognitive radio network (CRN) using a stochastic geometry is proposed. In the CRN the spectrum sensing data from secondary users (SU) are collected by a fusion center (FC) with the assistance of access points (AP) as cognitive relays, and when malicious eavesdropping SU are listening. In this paper we focus on the secure transmission of active APs relaying their spectrum sensing data to the FC. Closed expressions for the average secrecy rate are presented. Analytical formulations and results substantiate our analysis and demonstrate that multiple antennas at the APs is capable of improving the security of an AF-CSSCRN. The obtained numerical results also show that increasing the number of FCs, leads to an increase in the secrecy rate between the AP and its correlated FC.
Shi, Yang, Zhang, Qing, Liang, Jingwen, He, Zongjian, Fan, Hongfei.  2019.  Obfuscatable Anonymous Authentication Scheme for Mobile Crowd Sensing. IEEE Systems Journal. 13:2918—2929.

Mobile crowd sensing (MCS) is a rapidly developing technique for information collection from the users of mobile devices. This technique deals with participants' personal information such as their identities and locations, thus raising significant security and privacy concerns. Accordingly, anonymous authentication schemes have been widely considered for preserving participants' privacy in MCS. However, mobile devices are easy to lose and vulnerable to device capture attacks, which enables an attacker to extract the private authentication key of a mobile application and to further invade the user's privacy by linking sensed data with the user's identity. To address this issue, we have devised a special anonymous authentication scheme where the authentication request algorithm can be obfuscated into an unintelligible form and thus the authentication key is not explicitly used. This scheme not only achieves authenticity and unlinkability for participants, but also resists impersonation, replay, denial-of-service, man-in-the-middle, collusion, and insider attacks. The scheme's obfuscation algorithm is the first obfuscator for anonymous authentication, and it satisfies the average-case secure virtual black-box property. The scheme also supports batch verification of authentication requests for improving efficiency. Performance evaluations on a workstation and smart phones have indicated that our scheme works efficiently on various devices.

Zhang, Yueqian, Kantarci, Burak.  2019.  Invited Paper: AI-Based Security Design of Mobile Crowdsensing Systems: Review, Challenges and Case Studies. 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE). :17—1709.
Mobile crowdsensing (MCS) is a distributed sensing paradigm that uses a variety of built-in sensors in smart mobile devices to enable ubiquitous acquisition of sensory data from surroundings. However, non-dedicated nature of MCS results in vulnerabilities in the presence of malicious participants to compromise the availability of the MCS components, particularly the servers and participants' devices. In this paper, we focus on Denial of Service attacks in MCS where malicious participants submit illegitimate task requests to the MCS platform to keep MCS servers busy while having sensing devices expend energy needlessly. After reviewing Artificial Intelligence-based security solutions for MCS systems, we focus on a typical location-based and energy-oriented DoS attack, and present a security solution that applies ensemble techniques in machine learning to identify illegitimate tasks and prevent personal devices from pointless energy consumption so as to improve the availability of the whole system. Through simulations, we show that ensemble techniques are capable of identifying illegitimate and legitimate tasks while gradient boosting appears to be a preferable solution with an AUC performance higher than 0.88 in the precision-recall curve. We also investigate the impact of environmental settings on the detection performance so as to provide a clearer understanding of the model. Our performance results show that MCS task legitimacy decisions with high F-scores are possible for both illegitimate and legitimate tasks.
Chen, Yanping, Ma, Long, Xia, Hong, Gao, Cong, Wang, Zhongmin, Yu, Zhong.  2019.  Trust-Based Distributed Kalman Filter Estimation Fusion under Malicious Cyber Attacks. 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :2255—2260.

We consider distributed Kalman filter for dynamic state estimation over wireless sensor networks. It is promising but challenging when network is under cyber attacks. Since the information exchange between nodes, the malicious attacks quickly spread across the entire network, which causing large measurement errors and even to the collapse of sensor networks. Aiming at the malicious network attack, a trust-based distributed processing frame is proposed. Which allows neighbor nodes to exchange information, and a series of trusted nodes are found using truth discovery. As a demonstration, distributed Cooperative Localization is considered, and numerical results are provided to evaluate the performance of the proposed approach by considering random, false data injection and replay attacks.

Wang, Chong Xiao, Song, Yang, Tay, Wee Peng.  2018.  PRESERVING PARAMETER PRIVACY IN SENSOR NETWORKS. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). :1316–1320.
We consider the problem of preserving the privacy of a set of private parameters while allowing inference of a set of public parameters based on observations from sensors in a network. We assume that the public and private parameters are correlated with the sensor observations via a linear model. We define the utility loss and privacy gain functions based on the Cramér-Rao lower bounds for estimating the public and private parameters, respectively. Our goal is to minimize the utility loss while ensuring that the privacy gain is no less than a predefined privacy gain threshold, by allowing each sensor to perturb its own observation before sending it to the fusion center. We propose methods to determine the amount of noise each sensor needs to add to its observation under the cases where prior information is available or unavailable.
Wang, Chong Xiao, Song, Yang, Tay, Wee Peng.  2018.  PRESERVING PARAMETER PRIVACY IN SENSOR NETWORKS. 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). :1316–1320.
We consider the problem of preserving the privacy of a set of private parameters while allowing inference of a set of public parameters based on observations from sensors in a network. We assume that the public and private parameters are correlated with the sensor observations via a linear model. We define the utility loss and privacy gain functions based on the Cramér-Rao lower bounds for estimating the public and private parameters, respectively. Our goal is to minimize the utility loss while ensuring that the privacy gain is no less than a predefined privacy gain threshold, by allowing each sensor to perturb its own observation before sending it to the fusion center. We propose methods to determine the amount of noise each sensor needs to add to its observation under the cases where prior information is available or unavailable.
Simpson, Oluyomi, Sun, Yichuang.  2019.  A Stochastic Method to Physical Layer Security of an Amplify-and-Forward Spectrum Sensing in Cognitive Radio Networks: Secondary User to Relay. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :197—202.
In this paper, a framework for capitalizing on the potential benefits of physical layer security in an amplify-and-forward cooperative spectrum sensing (AF-CSS) in a cognitive radio network (CRN) using a stochastic geometry is proposed. In the CRN network the sensing data from secondary users (SUs) are collected by a fusion center (FC) with the help of access points (AP) as relays, and when malicious eavesdropping secondary users (SUs) are listening. We focus on the secure transmission of active SUs transmitting their sensing data to the AP. Closed expressions for the average secrecy rate are presented. Numerical results corroborate our analysis and show that multiple antennas at the APs can enhance the security of the AF-CSS-CRN. The obtained numerical results show that average secrecy rate between the AP and its correlated FC decreases when the number of AP is increased. Nevertheless, we find that an increase in the number of AP initially increases the overall average secrecy rate, with a perilous value at which the overall average secrecy rate then decreases. While increasing the number of active SUs, there is a decrease in the secrecy rate between the sensor and its correlated AP.
Ezick, James, Henretty, Tom, Baskaran, Muthu, Lethin, Richard, Feo, John, Tuan, Tai-Ching, Coley, Christopher, Leonard, Leslie, Agrawal, Rajeev, Parsons, Ben et al..  2019.  Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale. 2019 IEEE High Performance Extreme Computing Conference (HPEC). :1–7.

This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.

Xue, Hong, Wang, Jingxuan, Zhang, Miao, Wu, Yue.  2019.  Emergency Severity Assessment Method for Cluster Supply Chain Based on Cloud Fuzzy Clustering Algorithm. 2019 Chinese Control Conference (CCC). :7108–7114.

Aiming at the composite uncertainty characteristics and high-dimensional data stream characteristics of the evaluation index with both ambiguity and randomness, this paper proposes a emergency severity assessment method for cluster supply chain based on cloud fuzzy clustering algorithm. The summary cloud model generation algorithm is created. And the multi-data fusion method is applied to the cloud model processing of the evaluation indexes for high-dimensional data stream with ambiguity and randomness. The synopsis data of the emergency severity assessment indexes are extracted. Based on time attenuation model and sliding window model, the data stream fuzzy clustering algorithm for emergency severity assessment is established. The evaluation results are rationally optimized according to the generalized Euclidean distances of the cluster centers and cluster microcluster weights, and the severity grade of cluster supply chain emergency is dynamically evaluated. The experimental results show that the proposed algorithm improves the clustering accuracy and reduces the operation time, as well as can provide more accurate theoretical support for the early warning decision of cluster supply chain emergency.

Zhang, Chiyu, Hwang, Inseok.  2019.  Decentralized Multi-Sensor Scheduling for Multi-Target Tracking and Identity Management. 2019 18th European Control Conference (ECC). :1804–1809.
This paper proposes a multi-target tracking and identity management method with multiple sensors: a primary sensor with a large detection range to provide the targets' state estimates, and multiple secondary sensors capable of recognizing the targets' identities. Each of the secondary sensors is assigned to a sector of the operation area; a secondary sensor decides which target in its assigned sector to be identified and controls itself to identify the target. We formulate the decision-making process as an optimization problem to minimize the uncertainty of the targets' identities subject to the sensor dynamic constraints. The proposed algorithm is decentralized since the secondary sensors only communicate with the primary sensor for the target information, and need not to synchronize with each other. By integrating the proposed algorithm with the existing multi-target tracking algorithms, we develop a closed-loop multi-target tracking and identity management algorithm. The effectiveness of the proposed algorithm is demonstrated with illustrative numerical examples.
Yuan, Jie, Li, Xiaoyong.  2018.  A Reliable and Lightweight Trust Computing Mechanism for IoT Edge Devices Based on Multi-Source Feedback Information Fusion. IEEE Access. 6:23626–23638.
The integration of Internet of Things (IoT) and edge computing is currently a new research hotspot. However, the lack of trust between IoT edge devices has hindered the universal acceptance of IoT edge computing as outsourced computing services. In order to increase the adoption of IoT edge computing applications, first, IoT edge computing architecture should establish efficient trust calculation mechanism to alleviate the concerns of numerous users. In this paper, a reliable and lightweight trust mechanism is originally proposed for IoT edge devices based on multi-source feedback information fusion. First, due to the multi-source feedback mechanism is used for global trust calculation, our trust calculation mechanism is more reliable against bad-mouthing attacks caused by malicious feedback providers. Then, we adopt lightweight trust evaluating mechanism for cooperations of IoT edge devices, which is suitable for largescale IoT edge computing because it facilitates low-overhead trust computing algorithms. At the same time, we adopt a feedback information fusion algorithm based on objective information entropy theory, which can overcome the limitations of traditional trust schemes, whereby the trust factors are weighted manually or subjectively. And the experimental results show that the proposed trust calculation scheme significantly outperforms existing approaches in both computational efficiency and reliability.
Sohu, Izhar Ahmed, Ahmed Rahimoon, Asif, Junejo, Amjad Ali, Ahmed Sohu, Arsalan, Junejo, Sadam Hussain.  2019.  Analogous Study of Security Threats in Cognitive Radio. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). :1-4.

Utilization of Wireless sensor network is growing with the development in modern technologies. On other side electromagnetic spectrum is limited resources. Application of wireless communication is expanding day by day which directly threaten electromagnetic spectrum band to become congested. Cognitive Radio solves this issue by implementation of unused frequency bands as "White Space". There is another important factor that gets attention in cognitive model i.e: Wireless Security. One of the famous causes of security threat is malicious node in cognitive radio wireless sensor networks (CRWSN). The goal of this paper is to focus on security issues which are related to CRWSN as Fusion techniques, Co-operative Spectrum sensing along with two dangerous attacks in CR: Primary User Emulation (PUE) and Spectrum Sensing Data Falsification (SSDF).