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REMaDD: Resource-Efficient Malicious Domains Detector in Large-Scale Networks

IEEE ACCESS 2020年 8卷 66327-66337页

作者： Kdosha, Ofir Erets Rosenthal, Gilad Cohen, Kobi Freund, Alon Bartik, Avishay Ron, Aviv Ben Gurion Univ Negev Sch Elect & Comp Engn IL-8410501 Beer Sheva Israel IBM Cyber Secur Ctr Excellence IL-8489325 Beer Sheva Israel

Detecting malicious activities in cyber systems is a major challenge of cybersecurity service providers. Due to the large amount of network traffic, it is often likened to finding a needle in a haystack. Domain name system (DNS) is one of the fundamental protocols of the internet, and therefore it can give a broad view of those malicious activities, which abuse it and leave fingerprints as part of their attack vector. In this collaborative research between Ben-Gurion University, and IBM, a significant performance improvement was achieved in detecting malicious domains as compared to the state-of-the-art software solutions. Specifically, we establish a novel algorithm to detect malicious domains in large-scale DNS traffic, named Resource-Efficient Malicious Domain Detector (REMaDD), with the following desired properties. First, the algorithm does not require prior knowledge on historical malicious activities in its real-time operations. Second, the development used real live streaming data from The Inter-University Computation Center (IUCC), and operated on real-time IBM system. The algorithm is highly computational efficient and satisfies real-time requirements in terms of running time and computational complexity. REMaDD demonstrated strong performance in terms of both detection accuracy and computational efficiency as compared to existing algorithms. Specifically, experimental results on IBM production environment demonstrated that REMaDD achieved 89.4 & x0025;Precision score, and 82.9 & x0025;Recall score. By contrast, the DomainObserver, and *** algorithms achieved only 76.7 & x0025;, 67.2 & x0025;Precision score, and 81.7 & x0025;, 75.3 & x0025;Recall score, respectively.

关键词： Cyber security domain name system (DNS) detection algorithms real-time algorithms

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Making Neighbors Quiet: An Approach to Detect Virtual Resource Contention

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2020年第5期13卷 843-856页

作者： Vallone, Joel Birke, Robert Chen, Lydia Y. IBM Res Zurich Cloud & Comp Infrastruct CH-8803 Ruschlikon Switzerland IBM Res Lab Zurich Cloud Server Technol Grp CH-8803 Ruschlikon Switzerland

It is imperative for public cloud providers to guarantee performance targets for tenants' virtual machines (VMs) while respecting strict business confidentiality, e.g., having no information on applications nor their performance. A large body of related work addresses the challenges of detecting performance interferences by leveraging client's quality of service (QoS) metrics, e.g., latency, and additional profiling servers. In this paper, we take the perspective of the cloud provider and propose a general black-box approach that detects different resource contentions by throttling neighboring VMs. Specifically, we design a three-phase detection algorithm that includes: (i) an alarm phase to identify statistical outliers using control charts;(ii) a passive clustering phase to match the current sample to historical behaviors;and (iii) an active throttling phase to discern contentions from application phase changes via throttling. The algorithm is specifically designed for scenarios where multiple co-located VMs request detection analysis simultaneously. We implement and evaluate the proposed three-phase algorithm on four latency sensitive applications, i.e., Wikimedia and three benchmarks from Cloudsuite. Our extensive experimental results show that we can reach an average detection accuracy above 90 percent while limiting the performance degradation experienced by offender workloads to short learning phases.

关键词： Measurement Monitoring Quality of service Cloud computing Servers Detectors detection algorithms Contention detection cloud QoS

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Automatic Concept Extraction Based on Semantic Graphs From Big Data in Smart City

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2020年第1期7卷 225-233页

作者： Qiu, Jing Chai, Yuhan Tian, Zhihong Du, Xiaojiang Guizani, Mohsen Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Peng Cheng Lab Cyberspace Secur Res Ctr Shenzhen 518000 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Qatar Univ Coll Engn Doha Qatar

With the rapid development of smart cities, various types of sensors can rapidly collect a large amount of data, and it becomes increasingly important to discover effective knowledge and process information from massive amounts of data. Currently, in the field of knowledge engineering, knowledge graphs, especially domain knowledge graphs, play important roles and become the infrastructure of Internet knowledge-driven intelligent applications. Domain concept extraction is critical to the construction of domain knowledge graphs. Although there have been some works that have extracted concepts, semantic information has not been fully used. However, the excellent concept extraction results can be obtained by making full use of semantic information. In this article, a novel concept extraction method, Semantic Graph-Based Concept Extraction (SGCCE), is proposed. First, the similarities between terms are calculated using the word co-occurrence, the LDA topic model and Word2Vec. Then, a semantic graph of terms is constructed based on the similarities between the terms. Finally, according to the semantic graph of the terms, community detection algorithms are used to divide the terms into different communities where each community acts as a concept. In the experiments, we compare the concept extraction results that are obtained by different community detection algorithms to analyze the different semantic graphs. The experimental results show the effectiveness of our proposed method. This method can effectively use semantic information, and the results of the concept extraction are better from domain big data in smart cities.

关键词： Semantics Data mining Smart cities Big Data Clustering algorithms Frequency-domain analysis detection algorithms Knowledge discovery text analysis text mining

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Evaluating Overfit and Underfit in Models of Network Community Structure

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第9期32卷 1722-1735页

作者： Ghasemian, Amir Hosseinmardi, Homa Clauset, Aaron Univ Colorado Dept Comp Sci Boulder CO 80309 USA Univ Southern Calif Inst Informat Sci Marina Del Rey CA 90292 USA Univ Colorado Boulder CO 80309 USA Univ Colorado Biofrontiers Inst Boulder CO 80305 USA Santa Fe Inst 1399 Hyde Pk Rd Santa Fe NM 87501 USA

A common graph mining task is community detection, which seeks an unsupervised decomposition of a network into groups based on statistical regularities in network connectivity. Although many such algorithms exist, community detection's No Free Lunch theorem implies that no algorithm can be optimal across all inputs. However, little is known in practice about how different algorithms over or underfit to real networks, or how to reliably assess such behavior across algorithms. Here, we present a broad investigation of over and underfitting across 16 state-of-the-art community detection algorithms applied to a novel benchmark corpus of 572 structurally diverse real-world networks. We find that (i) algorithms vary widely in the number and composition of communities they find, given the same input;(ii) algorithms can be clustered into distinct high-level groups based on similarities of their outputs on real-world networks;(iii) algorithmic differences induce wide variation in accuracy on link-based learning tasks;and, (iv) no algorithm is always the best at such tasks across all inputs. Finally, we quantify each algorithm's overall tendency to over or underfit to network data using a theoretically principled diagnostic, and discuss the implications for future advances in community detection.

关键词： Task analysis Clustering algorithms Probabilistic logic Bayes methods detection algorithms Prediction algorithms Partitioning algorithms Community detection model selection overfitting underfitting link prediction link description

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Codeword Position Index Modulation Design for Sparse Code Multiple Access System

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第11期69卷 13273-13288页

作者： Lai, Ke Lei, Jing Wen, Lei Chen, Gaojie Natl Univ Def Technol Coll Elect Sci & Engn Dept Commun Engn Changsha 410000 Hunan Peoples R China Univ Leicester Dept Engn Leicester LE1 7RH Leics England

Codeword position index based sparse code multiple access (CPI-SCMA) is a novel variant of SCMA that employs the idea of index modulation (IM), which has been proposed recently as an effective alternative to the conventional SCMA system. In contrast to the conventional SCMA that directly maps the information bits of each user to a predefined M-point codebook, the data in CPI-SCMA is not only conveyed by the M-point codebook as in SCMA, but also by the indexes of the codeword positions. In this paper, we first propose a simplified detector termed as message passing algorithm aided index reliability detection (MPA-IRD) that is more suitable for practical implementation. Moreover, we investigate the spectral efficiency (SE) in CPI-SCMA, and two important features are identified to facilitate the design of the system. A tight approximation of the average block error probability (ABLEP) under the maximum likelihood detector (MLD) is also derived from studying the error rate performance of CPI-SCMA. Finally, to further improve the SE of CPI-SCMA, an enhanced version of CPI-SCMA termed as hybrid CPI-SCMA (HCPI-SCMA) is proposed. It is shown via both simulation, and analytical results that the proposed scheme not only achieves better error rate performance in the high signal-to-noise ratio (SNR) region but also improves the SE, and the level of robustness under channel estimation error compares to SCMA.

关键词： Indexes Modulation Error analysis OFDM NOMA detection algorithms Receivers Codeword position index modulation scma spectral efficiency

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Detecting Data Flow Errors Across Processes in Business Process Collaboration

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IEEE ACCESS 2020年 8卷 170862-170871页

作者： Xiong, Tianhong Pan, Maolin Yu, Yang Lou, Dingjun Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China

In business process collaboration (BPC), especially when it comes to message communication and data exchange, there are complex data dependencies among sender process, receiver process and messages. However, each participant of the overall BPC develops its part independently as a service, including its own communication part and data flow. As a result, data flow errors across processes may occur easily. In this article, we propose a method based on BPMN to detect these errors caused by data dependency violations. Our method is inspired by the study of detecting data flow errors within a single process and focuses on a subset of the elements of the BPC model, without having to consider the complete set. In particular, we define a set of data flow error patterns by analyzing and formalizing data dependencies in order to clearly clarify and identify errors. Then we give the corresponding automatic detection algorithm. Finally, through two evaluations, we demonstrate the effectiveness of our proposal.

关键词： Collaboration Business Correlation Receivers Data models Standards detection algorithms Business process management business process collaboration data flow error across processes data dependency

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Compositional Probabilistic Analysis of Temporal Properties Over Stochastic Detectors

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2020年第11期39卷 3288-3299页

作者： Ruchkin, Ivan Sokolsky, Oleg Weimer, James Hedaoo, Tushar Lee, Insup Univ Penn Dept Comp & Informat Sci 200 S 33Rd St Philadelphia PA 19104 USA

Runtime monitoring is a vital part of safety-critical systems. However, early stage assurance of monitoring quality is currently limited: it relies either on complex models that might be inaccurate in unknown ways or on data that would only be available once the system has been built. To address this issue, we propose a compositional framework for modeling and analysis of noisy monitoring systems. Our novel 3-value detector model uses probability spaces to represent atomic (noncomposite) detectors, and it composes them into a temporal logic-based monitor. The error rates of these monitors are estimated by our analysis engine, which combines symbolic probability algebra, independence inference, and estimation from labeled detection data. Our evaluation on an autonomous underwater vehicle found that our framework produces accurate estimates of error rates while using only detector traces, without any monitor traces. Furthermore, when data are scarce, our approach shows higher accuracy than noncompositional data-driven estimates from monitor traces. Thus, this article enables accurate evaluation of logical monitors in early design stages before deploying them.

关键词： Cyber-physical systems (CPSs) detection algorithms formal languages parameter estimation probabilistic logic

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Detecting Noisy ECG QRS Complexes Using WaveletCNN Autoencoder and ConvLSTM

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IEEE ACCESS 2020年 8卷 143802-143817页

作者： Yuen, Brosnan Dong, Xiaodai Lu, Tao Univ Victoria Dept Elect & Comp Engn Victoria BC V8P 5C2 Canada

In this paper, we propose a novel machine learning pipeline to detect QRS complexes in very noisy wearable electrocardiogram (ECG) devices. The machine learning pipeline consists of a Butterworth filter, two wavelet convolutional neural networks (WaveletCNNs) autoencoders, an optional QRS complex inverter, a Monte Carlo k-nearest neighbours (k-NN), and a convolutional long short-term memory (ConvLSTM). WaveletCNN autoencoders filter out electrode contact noise, instrumentation noise, and motion artifact noise by using the advantages of wavelet filters and convolutional neural networks. The QRS complex inverter flips inverted QRS complexes. Monte Carlo k-NN performs automatic gain control on the ECG signals in order to normalize it. The ConvLSTM executes the final QRS complex detection by using the power of a convolutional neural network and a long short-term memory. The MIT-BIH, the European ST-T, and the Long Term ST database Noise Stress Test databases provide the training and testing ECG recordings. The proposed machine learning pipeline performs 3 standard deviations better than the state of the art QRS complex detection algorithms in terms of F-1 score for very noisy environments.

关键词： Electrocardiography detection algorithms Noise measurement Machine learning Pipelines Testing Databases Artificial neural networks electrocardiogram (ECG) QRS complex feedforward neural networks multi-layer neural network convolutional neural networks recurrent neural networks

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Speaker Verification by Partial AUC Optimization With Mahalanobis Distance Metric Learning

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 1533-1548页

作者： Bai, Zhongxin Zhang, Xiao-Lei Chen, Jingdong Northwestern Polytech Univ Ctr Intelligent Acoust & Immers Commun CIAIC Xian 710072 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Northwestern Polytech Univ CIAIC Xian 710072 Peoples R China

Receiver operating characteristic (ROC) and detection error tradeoff (DET) curves are two widely used evaluation metrics for speaker verification. They are equivalent since the latter can be obtained by transforming the former's true positive y-axis to false negative y-axis and then re-scaling both axes by a probit operator. Real-world speaker verification systems, however, usually work on part of the ROC curve instead of the entire ROC curve given an application. Therefore, we propose in this article to use the area under part of the ROC curve (pAUC) as a more efficient evaluation metric for speaker verification. A Mahalanobis distance metric learning based back-end is applied to optimize pAUC, where the Mahalanobis distance metric learning guarantees that the optimization objective of the back-end is a convex one so that the global optimum solution is achievable. To improve the performance of the state-of-the-art speaker verification systems by the proposed back-end, we further propose two feature preprocessing techniques based on length-normalization and probabilistic linear discriminant analysis respectively. We evaluate the proposed systems on the major languages of NIST SRE16 and the core tasks of SITW. Experimental results show that the proposed back-end outperforms the state-of-the-art speaker verification back-ends in terms of seven evaluation metrics.

关键词： Measurement Feature extraction Acoustics detection algorithms Training Speech processing Optimization Metric learning pAUC speaker verification squared Mahalanobis distance

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Relative Afferent Pupillary Defect Screening Through Transfer Learning

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第3期24卷 788-795页

作者： Temel, Dogancan Mathew, Melvin J. AlRegib, Ghassan Khalifa, Yousuf M. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Emory Univ Sch Med Atlanta GA 30322 USA Grady Mem Hosp Atlanta GA 30303 USA

Abnormalities in pupillary light reflex can indicate optic nerve disorders that may lead to permanent visual loss if not diagnosed in an early stage. In this study, we focus on relative afferent pupillary defect (RAPD), which is based on the difference between the reactions of the eyes when they are exposed to light stimuli. Incumbent RAPD assessment methods are based on subjective practices that can lead to unreliable measurements. To eliminate subjectivity and obtain reliable measurements, we introduced an automated framework to detect RAPD. For validation, we conducted a clinical study with lab-on-a-headset, which can perform automated light reflex test. In addition to benchmarking handcrafted algorithms, we proposed a transfer learning-based approach that transformed a deep learning-based generic object recognition algorithm into a pupil detector. Based on the conducted experiments, proposed algorithm RAPDNet can achieve a sensitivity and a specificity of 90.6% over 64 test cases in a balanced set, which corresponds to an AUC of 0.929 in ROC analysis. According to our benchmark with three handcrafted algorithms and nine performance metrics, RAPDNet outperforms all other algorithms in every performance category.

关键词： Benchmark testing Visualization detection algorithms Informatics Target tracking Indexes Metadata Pupillary light reflex video dataset relative afferent pupillary defect (RAPD) pupil detection deep learning transfer learning teleophthalmology portable eye exam artificial intelligence-based eye examination

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