Reviewerly/exHarmony huggingface dataset:
Timely Classification and Verification of Network Traffic Using Gaussian Mixture Models

We present a novel approach for timely classification and verification of network traffic using Gaussian Mixture Models (GMMs). We generate a separate GMM for each class of applications using component-wise expectation-maximization (CEM) to match the network traffic distribution generated by these applications. We apply our models for both traffic classification, where the goal is to identify the source application from which the traffic originates, by evaluating the maximum posterior probability, and for traffic verification, where the goal is to verify whether the application that claims to be the source of the traffic is as expected, by likelihood testing. Our models use only the first initial packets of truncated flows in order to provide more efficient and timely traffic classification and verification. This allows for triggering timely countermeasures before the end of flows. We demonstrate the effectiveness of our approach by experiments on a public dataset collected from a real network. Our traffic classification approach outperforms other state-of-the-art approaches that are based on machine learning, and achieves up to 97.7% flow classification accuracy when using only 9 first initial packets of flows. We show that 96.6% flow classification accuracy can still be obtained when training the GMMs using only 0.5% of all flows. Our traffic verification approach achieves a minimum Half Total Error Rate (HTER) of 7.65% when using only 6 first initial packets of flows.
An Improved Multi-modal Data Decision Fusion Method Based on DS Evidence Theory

The method from DS evidence theory based multi-modal information decision fusion uses the classification structure information which the correct and error classification information provided by the classifiers. These two types of information affect the fusion results of DS evidence theory. This paper proposes a new method(DShW) for correct and error classification information in the balanced classification structure information based on DS evidence theory. That is, a method based on inertia weight normalization is introduced in the confusion matrix. To adjust the specific gravity of correct and error classification in classification structure information by changing the size of the value h, so as to achieve the purpose of balancing correct and error classification information. By comparing with other classifiers, we find that the DShW method effectively improves the accuracy of decision fusion.
Succinct Non-interactive Secure Computation

We present the first maliciously secure protocol for succinct non-interactive secure two-party computation (SNISC): Each player sends just a single message whose length is (essentially) independent of the running time of the function to be computed. The protocol does not require any trusted setup, satisfies superpolynomial-time simulation-based security (SPS), and is based on (subexponential) security of the Learning With Errors (LWE) assumption. We do not rely on SNARKs or "knowledge of exponent"-type assumptions. Since the protocol is non-interactive, the relaxation to SPS security is needed, as standard polynomial-time simulation is impossible; however, a slight variant of our main protocol yields a SNISC with polynomial-time simulation in the CRS model.
An Analysis of Motive and Observable Behavioral Indicators Associated With Insider Cyber-Sabotage and Other Attacks

Malicious insider attacks affect all sectors, economies, and organizations of the world, with insider cyber-sabotage causing significant damage. Key findings from previous research on malicious insider threats underscore a need for prevention and detection programs that are broadly based. Current risk management administrative and technical mitigation techniques often include indicators of potential malicious insiders in the form of personal factors and observable behaviors. However, these need empirical validation in the context of cyber-sabotage. This article builds on previous research on insider threats and investigates the relationship between motives, behavioral indicators, and insider cyber-sabotage. We examine 74 actual cases of convicted insider threat attacks using a logistic regression model. The results confirm hypotheses that revenge (one of the motives), addiction (one of the observable behavioral indicators), and poor work performance increase the likelihood of insider sabotage. The findings provide empirical validation of cyber-sabotage specific indicators for insider threat programs. Implications and future research are discussed.
Examining Protest as An Intervention to Reduce Online Prejudice: A Case Study of Prejudice Against Immigrants

There has been a growing concern about online users using social media to incite prejudice and hatred against other individuals or groups. While there has been research in developing automated techniques to identify online prejudice acts and hate speech, how to effectively counter online prejudice remains a societal challenge. Social protests, on the other hand, have been frequently used as an intervention for countering prejudice. However, research to date has not examined the relationship between protests and online prejudice. Using large-scale panel data collected from Twitter, we examine the changes in users’ tweeting behaviors relating to prejudice against immigrants following recent protests in the U.S. on immigration related topics. This is the first empirical study examining the effect of protests on reducing online prejudice. Our results show that there were both negative and positive changes in the measured prejudice after a protest, suggesting protest might have a mixed effect on reducing prejudice. We further identify users who are likely to change (or resist change) after a protest. This work contributes to the understanding of online prejudice and its intervention effect. The findings of this research have implications for designing targeted intervention.
Should I Stay or Should I Go

To improve the user engagement, especially under moderate to high traffic demand, it is important to understand the impact of the network and application QoS on user experience. This article comparatively evaluates the impact of impairments, with emphasis on rebufferings, startup delay, and bitrate changes, and their intensity and temporal dynamics, on user engagement in the context of video streaming. The analysis employed two large YouTube datasets. To characterize the user engagement and the impact of impairments, several new metrics were defined. We assessed whether or not there is a statistically significant relationship between different types of impairments and user engagement metrics, taking into account not only the characteristics of the impairments but also the covariates of the session (e.g., video duration, mean data rate). After observing the relationships across the entire dataset, we tested whether these relationships also persist under specific conditions with respect to the covariates. The introduction of several new metrics and of various covariates in the analysis are two innovative aspects of this work. We found that the presence of negative bitrate changes (BR-) is a stronger predictor of abandonment than rebufferrings (RB). Positive bitrate changes (BR+) in low resolution sessions are not well received. High rebufferring ratio has a prominent impact on the video watching percentage. These results can be used to guide the video streaming adaptation as well as suggest which parameters should be varied in controlled field studies.
Attention-Based Graph Evolution

Based on the recent success of deep generative models on continuous data, various new methods are being developed to generate discrete data such as graphs. However, these approaches focus on unconditioned generation, which limits their control over the generating procedure to produce graphs in context, thus limiting the applicability to real-world settings. To address this gap, we introduce an attention-based graph evolution model (AGE). AGE is a conditional graph generator based on the neural attention mechanism that can not only model graph evolution in both space and time, but can also model the transformation between graphs from one state to another. We evaluate AGE on multiple conditional graph-generation tasks, and our results show that it can generate realistic graphs conditioned on source graphs, outperforming existing methods in terms of quality and generality.
Tailoring and evaluating the Wikipedia for in-domain comparable corpora extraction

Abstract We propose a language-independent graph-based method to build à-la-carte article collections on user-defined domains from the Wikipedia. The core model is based on the exploration of the encyclopedia’s category graph and can produce both mono- and multilingual comparable collections. We run thorough experiments to assess the quality of the obtained corpora in 10 languages and 743 domains. According to an extensive manual evaluation, our graph model reaches an average precision of $$84\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>84</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on in-domain articles, outperforming an alternative model based on information retrieval techniques. As manual evaluations are costly, we introduce the concept of domainness and design several automatic metrics to account for the quality of the collections. Our best metric for domainness shows a strong correlation with human judgments, representing a reasonable automatic alternative to assess the quality of domain-specific corpora. We release the toolkit with the implementation of the extraction methods, the evaluation measures and several utilities.
A Two-stage Conversational Query Rewriting Model with Multi-task Learning

Conversational context understanding aims to recognize the real intention of user from the conversation history, which is critical for building the dialogue system. However, the multi-turn conversation understanding in open domain is still quite challenging, which requires the system extracting the important information and resolving the dependencies in contexts among a variety of open topics. In this paper, we propose the conversational query rewriting model, reformulating the multi-turn conversational queries into a single turn query, which conveys the true intention of users concisely and alleviates the difficulty of the multi-turn dialogue modeling. In the model, we formulate the query rewriting as a sequence generation problem and introduce word category information via the auxiliary word category label predicting task. To train our model, we construct a new Chinese query rewriting dataset and conduct experiments on it. The experimental results show that our model outperforms compared models, and prove the effectiveness of the word category information in improving the rewriting performance.
Replay attack detection using excitation source and system features

In this work, different excitation source features have been explored for automatic speaker verification (ASV) antispoofing. For this purpose, three linear prediction (LP) residual-based implicit source features—residual mel-frequency cepstral coefficient, LP residual Hilbert envelope mel-frequency cepstral coefficient (LPRHEMFCC), and residual phase cepstral coefficient (RPCC)—are proposed for the replay detection task. In addition, two explicit excitation source features, pitch contour and glottal flow derivative cepstral coefficient (GFDCC), are also proposed. Experimental analysis has been performed using GMM-UBM ASV experiments (on the IITG-MV replay database) and spoof detection experiments (on the ASVspoof 2017 database). For ASV experiments on the IITG-MV database, the performance is measured in terms of the difference (RFAR-ZFAR) and tandem-detection cost function (t-DCF) evaluation metrics. The RFAR and ZFAR stand for the false acceptance rate under replay attacks and zero effort impostor attacks, respectively. The best-reported t-DCF performances of 0.094 (males), 0.291 (females), and 0.175 (whole-set) are achieved for (LPRHEMFCC + RPCC) + MFCC combination. The equal error rate (EER) is used as an evaluation metric for spoof detection experiments on the ASVspoof 2017 database. The best-reported EERs of 8.86% and 8.30% were achieved for (LPRHEMFCC + RPCC) + CQCC and GFDCC + CQCC combinations. The experimental results show that source features complement the vocal-tract information-based spoof detection systems. This represents the significance of joint use of source and system features for the development of generalized countermeasures to replay attacks.
PrivDRM

Digital Right Management (DRM) is a technology developed to prevent illegal reproduction and distribution of digital contents. It protects the rights of content owners by allowing only authorised consumers to legitimately access associated digital content. DRM systems typically use a consumer's identity for authentication. In addition, some DRM systems collect consumer's preferences to obtain a content license. Thus, the behaviour of DRM systems disadvantages the digital content consumers (i.e. neglecting consumers' privacy) focusing more on securing the digital content (i.e. biased towards content owners). This paper proposes the Privacy-Preserving Digital Rights Management System (PrivDRM) that allows a consumer to acquire digital content with its license without disclosing complete personal information and without using any third parties. To evaluate the performance of the proposed solution, a prototype of the PrivDRM system has been developed and investigated. The security analysis (attacks and threats) are analysed and showed that PrivDRM supports countermeasures for well-known attacks and achieving the privacy requirements. In addition, a comparison with some well-known proposals shows that PrivDRM outperforms those proposals in terms of processing overhead.
Aspect category detection using statistical and semantic association

Abstract Aspect category detection (ACD) is an important subtask of aspect‐based sentiment analysis (ABSA). It is a challenging problem due to subjectivity involved in categorization, as well as the existence of overlapping classes. Among various approaches that have been applied to ACD include rule‐based approaches along with other machine learning approaches, and most of them are statistical in nature. In this article, we have used an association rule‐based approach. To deal with the statistical limitation of association rules, we proposed a hybridized rule‐based approach that combines association rules with the semantic association. For semantic associations, we have used the notion of word‐embeddings. Experiments were performed on SemEval dataset, a standard benchmark dataset for aspect categorization in the restaurant domain. We observed that semantic associations can complement statistical association and improve the accuracy of classification. The proposed method performs better than several state‐of‐the‐art methods.
A DCNN-Based Fast NIR Face Recognition System Robust to Reflected Light From Eyeglasses

Due to an increasing need for face recognition under poor lighting conditions, near infrared (NIR) face recognition based on deep convolutional neural networks (DCNN) has become an active area of research. However, in NIR face images of eyeglasses wearers, reflected light is generated around the eyes due to active NIR light sources, and it is one of the main contributors to performance degradation in NIR face recognition. In addition, there have to date been no attempts to lighten DCNN models for NIR face recognition. To solve these problems, we propose a DCNN-based fast NIR face recognition system which is robust to reflected light. This work has two main contributions: 1) We generated synthetic face images of individuals with and without eyeglasses using our proposed CycleGAN-based Glasses2Non-glasses (G2NG) data augmentation. We then constructed an augmented training database by adding the synthetic images, and the database helps to make the NIR face recognition system robust against reflected light. 2) A lightweight NIR FaceNet (LiNFNet) architecture was developed to reduce the computational complexity of the proposed system by adapting the depthwise separable convolutions and linear bottlenecks to VGGNet 16. The proposed architecture reduces the computation required, while improving the performance of NIR face recognition. Through the experiments reported in this paper, we verified that the proposed G2NG data augmentation improved the face recognition validation rate by 99.09% for NIR face images which have the reflected light from eyeglasses. Also, LiNFNet reduces the number of multiplication operations by 4.4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> compared with VGGNet 16.
A Security Risk Model for Online Banking System

We are living in an era, where the security of our data has become a huge issue. The services of cyber are the most entertaining and time-saving things in one's life. Though, people save their data on the cloud, which has been managed by the cyber. Cyber-security plays an important role in this matter. This is the open challenge for the security because many intruders are there who can attack the data and hack the details of the user by the server. If we will see around, then we will find many cases which include cyber-crime. This has become a very genuine concern to secure our datasets which are available on the cloud. Our research includes the security of the datasets which will include intruder detection that can be occurring anywhere on the earth. This has become very important to protect the data from intruders, for this intruder detection should be the most important key to get identified. If we don't know who is the intruder, then how we will get to know who is stealing the data which has been secured by using much biometric security, fingerprints, passwords, OTPs etc. intruder detection has become very important, especially on mobile objects - airplanes, ships, etc. As we know the problem then only we are able to find the solution. To prevent this, we are using the methods from machine learning, biometric recognition, data learning or hybrid methods. These are going to be the handle of the system that can help to secure the data from intruders by using best optimization techniques to get precise data. We proposed a model for the banking system, whereby using biometric impressions and digital signatures to make every transaction possible by bank's customer. This proposes the security for the Smart Online Banking System (SOBS) by using the biometric prints, it can become more secure and reduces a lot of threats can be made by an intruder.
A Post-Mortem Forensic Approach for the Kik Messenger on Android

Mobile devices have become an essential part of our life. These devices are a platform for social media and chat apps that are often designed to assist us retain our connection with old and new online friends. However, regardless of their valuable social influence, these apps are a medium in which criminals and perpetrators can exploit to attack innocent people. Nevertheless, these apps can provide a wealth of digital artifacts for law enforcement agencies. Often, well-established digital forensic techniques are used to determine criminal cases in the court of law. Therefore, this paper presents a post-mortem memory forensics and digital investigations for the popular instant messaging application named Kik Messenger. Kik is designed to assist users to connect quickly and easily with the world around them. Statistics such as the number of installations and active users underlines its importance. Moreover, the Android operating system and its internal memory, including RAM and NAND flash memory are examined. This paper presents our investigation methodology that employs memory dumps for both NAND flash memory as well as the heap memory. Our results of forensic analysis show promising existences for the sent and received messages and contacts in both types of memory dumps; these incidences are valuable to the digital investigator. Various scenarios are studied, experimentation results show that sent and received messages and contact artifacts can be found in both memories even after the chat session is deleted, messages are individually deleted, contacts are deleted, app is closed, and even after the phone is locked.
Top-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1657" altimg="si115.svg"><mml:mi>k</mml:mi></mml:math> closed co-occurrence patterns mining with differential privacy over multiple streams

The frequent pattern mining over data streams is a very important problem for many applications. However, many researches investigate a single stream in which every transaction is independent and it is not considered that some transactions are generated by the same individual. Some real-world applications involve multiple streams that continuously generate objects, and interesting observations are the objects appearing in many streams, such as emerging topic discovery, e-commerce, web usage pattern mining and location-based services. In this paper, we analyze the privacy problems in mining top-k closed co-occurrence patterns over multiple streams caused by single release of a window and continuous releases in successive windows. To prevent privacy leakage, we propose a differentially private top-k closed co-occurrence patterns mining algorithm across multiple streams with exponential mechanism and Laplace mechanism. The algorithm consists of dissimilarity calculation phase and differentially private mining phase, where differentially private mining phase includes adjusting CP-Graph with splitting transaction, perturbing CP-Graph to obtain the top-k closed co-occurrence patterns candidate set and adding noise to the supports of patterns. Finally, we prove our algorithm satisfies differential privacy and experiment results show the utility and efficiency of our algorithm.
Provably secure cross-domain authentication key agreement protocol based on heterogeneous signcryption scheme

The heterogeneous signcryption mechanism can not only solve the communication problems between users under different cryptosystems, but also ensure the confidentiality and unforgeability of messages. In this paper, the security analysis of an Authenticated Key Agreement Protocol for Cross-Domain Based on heterogeneous signcryption scheme is firstly carried out. It is pointed out that the authentication phase 1 in this scheme does not satisfy the unforgeability of signcryption ciphertext. In order to enhance security, this paper proposes a new two-way heterogeneous signcryption cross-domain authentication scheme. It is proved that the scheme satisfies the unforgeability under the adaptive selection message attack under the random oracle model. Finally, the efficiency analysis shows that the improved scheme has higher computational efficiency.