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2021-06-02
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.
2021-05-25
Diao, Yiqing, Ye, Ayong, Cheng, Baorong, Zhang, Jiaomei, Zhang, Qiang.  2020.  A Dummy-Based Privacy Protection Scheme for Location-Based Services under Spatiotemporal Correlation. 2020 International Conference on Networking and Network Applications (NaNA). :443—447.
The dummy-based method has been commonly used to protect the users location privacy in location-based services, since it can provide precise results and generally do not rely on a third party or key sharing. However, the close spatiotemporal correlation between the consecutively reported locations enables the adversary to identify some dummies, which lead to the existing dummy-based schemes fail to protect the users location privacy completely. To address this limit, this paper proposes a new algorithm to produce dummy location by generating dummy trajectory, which naturally takes into account of the spatiotemporal correlation all round. Firstly, the historical trajectories similar to the user's travel route are chosen as the dummy trajectories which depend on the distance between two trajectories with the help of home gateway. Then, the dummy is generated from the dummy trajectory by taking into account of time reachability, historical query similarity and the computation of in-degree/out-degree. Security analysis shows that the proposed scheme successfully perturbs the spatiotemporal correlation between neighboring location sets, therefore, it is infeasible for the adversary to distinguish the users real location from the dummies. Furthermore, extensive experiments indicate that the proposal is able to protect the users location privacy effectively and efficiently.
2021-05-03
Sohail, Muhammad, Zheng, Quan, Rezaiefar, Zeinab, Khan, Muhammad Alamgeer, Ullah, Rizwan, Tan, Xiaobin, Yang, Jian, Yuan, Liu.  2020.  Triangle Area Based Multivariate Correlation Analysis for Detecting and Mitigating Cache Pollution Attacks in Named Data Networking. 2020 3rd International Conference on Hot Information-Centric Networking (HotICN). :114–121.
The key feature of NDN is in-network caching that every router has its cache to store data for future use, thus improve the usage of the network bandwidth and reduce the network latency. However, in-network caching increases the security risks - cache pollution attacks (CPA), which includes locality disruption (ruining the cache locality by sending random requests for unpopular contents to make them popular) and False Locality (introducing unpopular contents in the router's cache by sending requests for a set of unpopular contents). In this paper, we propose a machine learning method, named Triangle Area Based Multivariate Correlation Analysis (TAB-MCA) that detects the cache pollution attacks in NDN. This detection system has two parts, the triangle-area-based MCA technique, and the threshold-based anomaly detection technique. The TAB-MCA technique is used to extract hidden geometrical correlations between two distinct features for all possible permutations and the threshold-based anomaly detection technique. This technique helps our model to be able to distinguish attacks from legitimate traffic records without requiring prior knowledge. Our technique detects locality disruption, false locality, and combination of the two with high accuracy. Implementation of XC-topology, the proposed method shows high efficiency in mitigating these attacks. In comparison to other ML-methods, our proposed method has a low overhead cost in mitigating CPA as it doesn't require attackers' prior knowledge. Additionally, our method can also detect non-uniform attack distributions.
2021-04-27
Zhang, M., Chen, Y., Huang, J..  2020.  SE-PPFM: A Searchable Encryption Scheme Supporting Privacy-Preserving Fuzzy Multikeyword in Cloud Systems. IEEE Systems Journal. :1–9.
Cloud computing provides an appearing application for compelling vision in managing big-data files and responding queries over a distributed cloud platform. To overcome privacy revealing risks, sensitive documents and private data are usually stored in the clouds in a cipher-based manner. However, it is inefficient to search the data in traditional encryption systems. Searchable encryption is a useful cryptographic primitive to enable users to retrieve data in ciphertexts. However, the traditional searchable encryptions provide lower search efficiency and cannot carry out fuzzy multikeyword queries. To solve this issue, in this article, we propose a searchable encryption that supports privacy-preserving fuzzy multikeyword search (SE-PPFM) in cloud systems, which is built by asymmetric scalar-product-preserving encryptions and Hadamard product operations. In order to realize the functionality of efficient fuzzy searches, we employ Word2vec as the primitive of machine learning to obtain a fuzzy correlation score between encrypted data and queries predicates. We analyze and evaluate the performance in terms of token of multikeyword, retrieval and match time, file retrieval time and matching accuracy, etc. The experimental results show that our scheme can achieve a higher efficiency in fuzzy multikeyword ciphertext search and provide a higher accuracy in retrieving and matching procedure.
Yu, X., Li, T., Hu, A..  2020.  Time-series Network Anomaly Detection Based on Behaviour Characteristics. 2020 IEEE 6th International Conference on Computer and Communications (ICCC). :568–572.
In the application scenarios of cloud computing, big data, and mobile Internet, covert and diverse network attacks have become a serious problem that threatens the security of enterprises and personal information assets. Abnormal network behaviour detection based on network behaviour characteristics has become an important means to protect network security. However, existing frameworks do not make full use of the characteristics of the correlation between continuous network behaviours, and do not use an algorithm that can process time-series data or process the original feature set into time-series data to match the algorithm. This paper proposes a time-series abnormal network behaviour detection framework. The framework consists of two parts: an algorithm model (DBN-BiGRU) that combines Deep Belief Network (DBN) and Bidirectional Gated Recurrent Unit (BiGRU), and a pre-processing scheme that processes the original feature analysis files of CICIDS2017 to good time-series data. This detection framework uses past and future behaviour information to determine current behaviours, which can improve accuracy, and can adapt to the large amount of existing network traffic and high-dimensional characteristics. Finally, this paper completes the training of the algorithm model and gets the test results. Experimental results show that the prediction accuracy of this framework is as high as 99.82%, which is better than the traditional frameworks that do not use time-series information.
2021-03-30
Zhang, R., Cao, Z., Wu, K..  2020.  Tracing and detection of ICS Anomalies Based on Causality Mutations. 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC). :511—517.

The algorithm of causal anomaly detection in industrial control physics is proposed to determine the normal cloud line of industrial control system so as to accurately detect the anomaly. In this paper, The causal modeling algorithm combining Maximum Information Coefficient and Transfer Entropy was used to construct the causal network among nodes in the system. Then, the abnormal nodes and the propagation path of the anomaly are deduced from the structural changes of the causal network before and after the attack. Finally, an anomaly detection algorithm based on hybrid differential cumulative is used to identify the specific anomaly data in the anomaly node. The stability of causality mining algorithm and the validity of locating causality anomalies are verified by using the data of classical chemical process. Experimental results show that the anomaly detection algorithm is better than the comparison algorithm in accuracy, false negative rate and recall rate, and the anomaly location strategy makes the anomaly source traceable.

2021-03-29
Alamri, M., Mahmoodi, S..  2020.  Facial Profiles Recognition Using Comparative Facial Soft Biometrics. 2020 International Conference of the Biometrics Special Interest Group (BIOSIG). :1—4.

This study extends previous advances in soft biometrics and describes to what extent soft biometrics can be used for facial profile recognition. The purpose of this research is to explore human recognition based on facial profiles in a comparative setting based on soft biometrics. Moreover, in this work, we describe and use a ranking system to determine the recognition rate. The Elo rating system is employed to rank subjects by using their face profiles in a comparative setting. The crucial features responsible for providing useful information describing facial profiles have been identified by using relative methods. Experiments based on a subset of the XM2VTSDB database demonstrate a 96% for recognition rate using 33 features over 50 subjects.

2021-03-17
Soliman, H. M..  2020.  An Optimization Approach to Graph Partitioning for Detecting Persistent Attacks in Enterprise Networks. 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1—6.
Advanced Persistent Threats (APTs) refer to sophisticated, prolonged and multi-step attacks, planned and executed by skilled adversaries targeting government and enterprise networks. Attack graphs' topologies can be leveraged to detect, explain and visualize the progress of such attacks. However, due to the abundance of false-positives, such graphs are usually overwhelmingly large and difficult for an analyst to understand. Graph partitioning refers to the problem of reducing the graph of alerts to a set of smaller incidents that are easier for an analyst to process and better represent the actual attack plan. Existing approaches are oblivious to the security-context of the problem at hand and result in graphs which, while smaller, make little sense from a security perspective. In this paper, we propose an optimization approach allowing us to generate security-aware partitions, utilizing aspects such as the kill chain progression, number of assets involved, as well as the size of the graph. Using real-world datasets, the results show that our approach produces graphs that are better at capturing the underlying attack compared to state-of-the-art approaches and are easier for the analyst to understand.
2021-03-01
Zhang, Y., Groves, T., Cook, B., Wright, N. J., Coskun, A. K..  2020.  Quantifying the impact of network congestion on application performance and network metrics. 2020 IEEE International Conference on Cluster Computing (CLUSTER). :162–168.
In modern high-performance computing (HPC) systems, network congestion is an important factor that contributes to performance degradation. However, how network congestion impacts application performance is not fully understood. As Aries network, a recent HPC network architecture featuring a dragonfly topology, is equipped with network counters measuring packet transmission statistics on each router, these network metrics can potentially be utilized to understand network performance. In this work, by experiments on a large HPC system, we quantify the impact of network congestion on various applications' performance in terms of execution time, and we correlate application performance with network metrics. Our results demonstrate diverse impacts of network congestion: while applications with intensive MPI operations (such as HACC and MILC) suffer from more than 40% extension in their execution times under network congestion, applications with less intensive MPI operations (such as Graph500 and HPCG) are mostly not affected. We also demonstrate that a stall-to-flit ratio metric derived from Aries network counters is positively correlated with performance degradation and, thus, this metric can serve as an indicator of network congestion in HPC systems.
2021-02-15
Karthikeyan, S. Paramasivam, El-Razouk, H..  2020.  Horizontal Correlation Analysis of Elliptic Curve Diffie Hellman. 2020 3rd International Conference on Information and Computer Technologies (ICICT). :511–519.
The world is facing a new revolutionary technology transition, Internet of things (IoT). IoT systems requires secure connectivity of distributed entities, including in-field sensors. For such external devices, Side Channel Analysis poses a potential threat as it does not require complete knowledge about the crypto algorithm. In this work, we perform Horizontal Correlation Power Analysis (HCPA) which is a type of Side Channel Analysis (SCA) over the Elliptic Curve Diffie Hellman (ECDH) key exchange protocol. ChipWhisperer (CW) by NewAE Technologies is an open source toolchain which is utilized to perform the HCPA by using CW toolchain. To best of our knowledge, this is the first attempt to implemented ECDH on Artix-7 FPGA for HCPA. We compare our correlation results with the results from AES -128 bits provided by CW. Our point of attack is the Double and Add algorithm which is used to perform Scalar multiplication in ECC. We obtain a maximum correlation of 7% for the key guess using the HCPA. We also discuss about the possible cause for lower correlation and few potentials ways to improve it. In Addition to HCPA we also perform Simple Power Analysis (SPA) (visual) for ECDH, to guess the trailing zeros in the 128-bit secret key for different power traces.
Zhu, L., Zhou, X., Zhang, X..  2020.  A Reversible Meaningful Image Encryption Scheme Based on Block Compressive Sensing. 2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP). :326–330.
An efficient and reversible meaningful image encryption scheme is proposed in this paper. The plain image is first compressed and encrypted simultaneously by Adaptive Block Compressive Sensing (ABCS) framework to create a noise-like secret image. Next, Least Significant Bit (LSB) embedding is employed to embed the secret image into a carrier image to generate the final meaningful cipher image. In this scheme, ABCS improves the compression and efficiency performance, and the embedding and extraction operations are absolutely reversible. The simulation results and security analyses are presented to demonstrate the effectiveness, compression, secrecy of the proposed scheme.
2021-02-08
Qiao, B., Jin, L., Yang, Y..  2016.  An Adaptive Algorithm for Grey Image Edge Detection Based on Grey Correlation Analysis. 2016 12th International Conference on Computational Intelligence and Security (CIS). :470—474.

In the original algorithm for grey correlation analysis, the detected edge is comparatively rough and the thresholds need determining in advance. Thus, an adaptive edge detection method based on grey correlation analysis is proposed, in which the basic principle of the original algorithm for grey correlation analysis is used to get adaptively automatic threshold according to the mean value of the 3×3 area pixels around the detecting pixel and the property of people's vision. Because the false edge that the proposed algorithm detected is relatively large, the proposed algorithm is enhanced by dealing with the eight neighboring pixels around the edge pixel, which is merged to get the final edge map. The experimental results show that the algorithm can get more complete edge map with better continuity by comparing with the traditional edge detection algorithms.

2021-02-01
Mahmood, Z. H., Ibrahem, M. K..  2020.  A Noise-Free Homomorphic Encryption based on Chaotic System. 2020 1st. Information Technology To Enhance e-learning and Other Application (IT-ELA. :132–137.
Fully homomorphic encryption (FHE) was one of the most prominent research topics of the last ten years. And it is considered as a major cryptographic tool in a secure and reliable cloud computing environment. The reason behind that because it allows computations over encrypted data, without decrypting the original message. This paper developed a new symmetric (FHE) algorithm based on Enhanced Matrix Operation for Randomization and Encryption (EMORE) algorithm using a chaotic system. The proposed algorithm was considered a noise-free algorithm. It generates the ciphertext in a floating-point number's format, overcomes the problem of plaintext ring and modular arithmetic operation in EMORE by the hardness of a chaotic system, and provides another level of security in terms of randomness properties, sensitivity to the initial condition, and large key size (\textbackslashtextgreater2100) of a chaotic system. Besides that, the proposed algorithm provides the confidentiality and privacy of outsourced data computing through homomorphism property of it. By using both numerical and statistical tests, these tests proved that the proposed algorithm has positive randomness properties and provide secure and reliable encryption (through encryption-decryption time, key sensitivity, keyspace, and correlation). Finally, the simulation results show that the execution time of the proposed algorithm is faster about 7.85 times than the EMORE algorithm.
2021-01-25
Feng, Y., Sun, G., Liu, Z., Wu, C., Zhu, X., Wang, Z., Wang, B..  2020.  Attack Graph Generation and Visualization for Industrial Control Network. 2020 39th Chinese Control Conference (CCC). :7655–7660.
Attack graph is an effective way to analyze the vulnerabilities for industrial control networks. We develop a vulnerability correlation method and a practical visualization technology for industrial control network. First of all, we give a complete attack graph analysis for industrial control network, which focuses on network model and vulnerability context. Particularly, a practical attack graph algorithm is proposed, including preparing environments and vulnerability classification and correlation. Finally, we implement a three-dimensional interactive attack graph visualization tool. The experimental results show validation and verification of the proposed method.
2021-01-22
Xu, H., Jiang, H..  2019.  An Image Encryption Schema Based on Hybrid Optimized Chaotic System. 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). :784–788.

The purpose of this paper is to improve the safety of chaotic image encryption algorithm. Firstly, to achieve this goal, it put forward two improved chaotic system logistic and henon, which covered an promoted henon chaotic system with better probability density, and an 2-dimension logistic chaotic system with high Lyapunov exponents. Secondly, the chaotic key stream was generated by the new 2D logistic chaotic system and optimized henon mapping, which mixed in dynamic proportions. The conducted sequence has better randomness and higher safety for image cryptosystem. Thirdly, we proposed algorithm takes advantage of the compounded chaotic system Simulation experiment results and security analysis showed that the proposed scheme was more effective and secure. It can resist various typical attacks, has high security, satisfies the requirements of image encryption theoretical.

Alghamdi, A. A., Reger, G..  2020.  Pattern Extraction for Behaviours of Multi-Stage Threats via Unsupervised Learning. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1—8.
Detection of multi-stage threats such as Advanced Persistent Threats (APT) is extremely challenging due to their deceptive approaches. Sequential events of threats might look benign when performed individually or from different addresses. We propose a new unsupervised framework to identify patterns and correlations of malicious behaviours by analysing heterogeneous log-files. The framework consists of two main phases of data analysis to extract inner-behaviours of log-files and then the patterns of those behaviours over analysed files. To evaluate the framework we have produced a (publicly available) labelled version of the SotM43 dataset. Our results demonstrate that the framework can (i) efficiently cluster inner-behaviours of log-files with high accuracy and (ii) extract patterns of malicious behaviour and correlations between those patterns from real-world data.
2021-01-18
Huang, Y., Wang, S., Wang, Y., Li, H..  2020.  A New Four-Dimensional Chaotic System and Its Application in Speech Encryption. 2020 Information Communication Technologies Conference (ICTC). :171–175.
Traditional encryption algorithms are not suitable for modern mass speech situations, while some low-dimensional chaotic encryption algorithms are simple and easy to implement, but their key space often small, leading to poor security, so there is still a lot of room for improvement. Aiming at these problems, this paper proposes a new type of four-dimensional chaotic system and applies it to speech encryption. Simulation results show that the encryption scheme in this paper has higher key space and security, which can achieve the speech encryption goal.
2020-12-21
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.
2020-12-02
Jie, Y., Zhou, L., Ming, N., Yusheng, X., Xinli, S., Yongqiang, Z..  2018.  Integrated Reliability Analysis of Control and Information Flow in Energy Internet. 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). :1—9.
In this paper, according to the electricity business process including collecting and transmitting power information and sending control instructions, a coupling model of control-communication flow is built which is composed of three main matrices: control-communication, communication-communication, communication-control incidence matrices. Furthermore, the effective path change between two communication nodes is analyzed and a calculation method of connectivity probability for information network is proposed when considering a breakdown in communication links. Then, based on Bayesian conditional probability theory, the effect of the communication interruption on the energy Internet is analyzed and the metric matrix of controllability is given under communication congestion. Several cases are given in the final of paper to verify the effectiveness of the proposed method for calculating controllability matrix by considering different link interruption scenarios. This probability index can be regarded as a quantitative measure of the controllability of the power service based on the communication transmission instructions, which can be used in the power business decision-making in order to improve the control reliability of the energy Internet.
2020-11-30
Ray, K., Banerjee, A., Mohalik, S. K..  2019.  Web Service Selection with Correlations: A Feature-Based Abstraction Refinement Approach. 2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA). :33–40.
In this paper, we address the web service selection problem for linear workflows. Given a linear workflow specifying a set of ordered tasks and a set of candidate services providing different features for each task, the selection problem deals with the objective of selecting the most eligible service for each task, given the ordering specified. A number of approaches to solving the selection problem have been proposed in literature. With web services growing at an incredible pace, service selection at the Internet scale has resurfaced as a problem of recent research interest. In this work, we present our approach to the selection problem using an abstraction refinement technique to address the scalability limitations of contemporary approaches. Experiments on web service benchmarks show that our approach can add substantial performance benefits in terms of space when compared to an approach without our optimization.
2020-11-20
EVINA, P. A., AYACHI, F. LABBENE, JAIDI, F., Bouhoula, A..  2019.  Enforcing a Risk Assessment Approach in Access Control Policies Management: Analysis, Correlation Study and Model Enhancement. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :1866—1871.
Nowadays, the domain of Information System (IS) security is closely related to that of Risk Management (RM). As an immediate consequence, talking about and tackling the security of IS imply the implementation of a set of mechanisms that aim to reduce or eliminate the risk of IS degradations. Also, the high cadence of IS evolution requires careful consideration of corresponding measures to prevent or mitigate security risks that may cause the degradation of these systems. From this perspective, an access control service is subjected to a number of rules established to ensure the integrity and confidentiality of the handled data. During their lifecycle, the use or manipulation of Access Control Policies (ACP) is accompanied with several defects that are made intentionally or not. For many years, these defects have been the subject of numerous studies either for their detection or for the analysis of the risks incurred by IS to their recurrence and complexity. In our research works, we focus on the analysis and risk assessment of noncompliance anomalies in concrete instances of access control policies. We complete our analysis by studying and assessing the risks associated with the correlation that may exist between different anomalies. Indeed, taking into account possible correlations can make a significant contribution to the reliability of IS. Identifying correlation links between anomalies in concrete instances of ACP contributes in discovering or detecting new scenarios of alterations and attacks. Therefore, once done, this study mainly contributes in the improvement of our risk assessment model.
2020-11-04
Khalid, F., Hanif, M. A., Rehman, S., Ahmed, R., Shafique, M..  2019.  TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks. 2019 IEEE 25th International Symposium on On-Line Testing and Robust System Design (IOLTS). :188—193.

Most of the data manipulation attacks on deep neural networks (DNNs) during the training stage introduce a perceptible noise that can be catered by preprocessing during inference, or can be identified during the validation phase. There-fore, data poisoning attacks during inference (e.g., adversarial attacks) are becoming more popular. However, many of them do not consider the imperceptibility factor in their optimization algorithms, and can be detected by correlation and structural similarity analysis, or noticeable (e.g., by humans) in multi-level security system. Moreover, majority of the inference attack rely on some knowledge about the training dataset. In this paper, we propose a novel methodology which automatically generates imperceptible attack images by using the back-propagation algorithm on pre-trained DNNs, without requiring any information about the training dataset (i.e., completely training data-unaware). We present a case study on traffic sign detection using the VGGNet trained on the German Traffic Sign Recognition Benchmarks dataset in an autonomous driving use case. Our results demonstrate that the generated attack images successfully perform misclassification while remaining imperceptible in both “subjective” and “objective” quality tests.

Al-Far, A., Qusef, A., Almajali, S..  2018.  Measuring Impact Score on Confidentiality, Integrity, and Availability Using Code Metrics. 2018 International Arab Conference on Information Technology (ACIT). :1—9.

Confidentiality, Integrity, and Availability are principal keys to build any secure software. Considering the security principles during the different software development phases would reduce software vulnerabilities. This paper measures the impact of the different software quality metrics on Confidentiality, Integrity, or Availability for any given object-oriented PHP application, which has a list of reported vulnerabilities. The National Vulnerability Database was used to provide the impact score on confidentiality, integrity, and availability for the reported vulnerabilities on the selected applications. This paper includes a study for these scores and its correlation with 25 code metrics for the given vulnerable source code. The achieved results were able to correlate 23.7% of the variability in `Integrity' to four metrics: Vocabulary Used in Code, Card and Agresti, Intelligent Content, and Efferent Coupling metrics. The Length (Halstead metric) could alone predict about 24.2 % of the observed variability in ` Availability'. The results indicate no significant correlation of `Confidentiality' with the tested code metrics.

Zong, P., Wang, Y., Xie, F..  2018.  Embedded Software Fault Prediction Based on Back Propagation Neural Network. 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :553—558.

Predicting software faults before software testing activities can help rational distribution of time and resources. Software metrics are used for software fault prediction due to their close relationship with software faults. Thanks to the non-linear fitting ability, Neural networks are increasingly used in the prediction model. We first filter metric set of the embedded software by statistical methods to reduce the dimensions of model input. Then we build a back propagation neural network with simple structure but good performance and apply it to two practical embedded software projects. The verification results show that the model has good ability to predict software faults.

2020-11-02
Ma, Y., Bai, X..  2019.  Comparison of Location Privacy Protection Schemes in VANETs. 2019 12th International Symposium on Computational Intelligence and Design (ISCID). 2:79–83.
Vehicular Ad-hoc Networks (VANETs) is a traditional mobile ad hoc network (MANET) used on traffic roads and it is a special mobile ad hoc network. As an intelligent transportation system, VANETs can solve driving safety and provide value-added services. Therefore, the application of VANETs can improve the safety and efficiency of road traffic. Location services are in a crucial position for the development of VANETs. VANETs has the characteristics of open access and wireless communication. Malicious node attacks may lead to the leakage of user privacy in VANETs, thus seriously affecting the use of VANETs. Therefore, the location privacy issue of VANETs cannot be ignored. This paper classifies the attack methods in VANETs, and summarizes and compares the location privacy protection techniques proposed in the existing research.