Reviewerly/exHarmony huggingface dataset:
Weaponizing IoT Sensors: When Table Choice Poses a Security Vulnerability

The security threat posed by keyloggers on laptop and desktop computers is traditionally understood from the perspective of malware that directly reads keystrokes on the victim’s machine. While recent research on smart phone platforms has shown that motion/vibration sensors inbuilt in these phones also pose a keylogging threat, this line of attack has never been investigated in desktop and laptop settings given that no such sensors exist in these settings. In this paper, we show that the vibration dynamics of commonly used computer table materials transmit keyboard vibrations during typing with such fine granularity that keyboard typing locations (and hence keystrokes) could be learned from the vibrations. In practice such an attack would be executed by methodically rigging the underside of a computer table or keyboard itself with a series of motion sensors, and then mining the data generated by these sensors during typing. Taking the case of typical computer table materials such as glass, plastic, metal and wood, we study this line of attack and highlight scenarios where it poses a potent threat. Thanks to fast growing IoT platforms making available easy-to-use, fully featured, cheap sensors, we argue that this line of attack is accessible to even casual "computer hackers" having no knowledge of low-level hardware programming. The paper brings to light a previously unexplored privacy threat that security practitioners and end-users need to pay attention to as IoT goes mainstream.
What Makefile? Detecting Compiler Information Without Source Using The Code Property Graph

Users frequently lack access to the underlying source code and build artifacts of the programs they use. Without access, uncovering information about programs, such as compiler information or security properties, becomes a difficult task. Various methods exist for static analysis testing on source code languages, but few tools work solely with the executable machine code. This paper proposes constructing the code property graph from a program’s lifted machine code to observe structural differences between other executables. We implement our approach with the Binary Ninja Intermediate Language (BNIL) and the graph2vec neural embedding framework to create embedded representations of the graphical properties of the program. Downstream applications, such as supervised machine learning, can then analyze these representations. We demonstrate the effectiveness of our approach by training a supervised random forest classifier on the embedded graphs to determine, at the function level, which compiler, clang or gcc, created the executable the function belongs to. Our results achieved an accuracy of 100% across our testing set of 25,600 samples.
Identification of Car Make and Model Using Deep Learning and Computer Vision Techniques

Vehicle model detection is now an integral part of the traffic scene. In the realm of object detection, Deep Learning models have been widely applied. Though there are few research articles in this area, the lack of effective image recognition has been a major challenge to be addressed. Therefore, the present study has used Single Shot Detector (SSD)-YOLOV5 for car recognition and a deep residual network (ResNet) for classification purposes to combat this issue. The proposed model will take the real-time image as an input and using probability estimation, attempts to recognize the car model. We used a combination of the Stanford University car dataset with 196 classes and an Indian car dataset with 97 classes to train the model. The proposed model achieved an accuracy of 86%, which was higher compared to the VGG16 model's accuracy of 71%. The front-end web application was constructed with React JS and the model is deployed using the Flask micro-framework.
Distributed Nearest Neighbor-based outlier identification technique for WSNs

In this research paper, we provide a distributed K-mean strategy based on dynamic Neighbor co-ordination. It deals with locating problematic nodes by using intermittent faults and clustering mechanisms in the sensor network. The described model, sensor node, takes into account the average of all the cluster regions by using the median-based K-mean approach to gather the data from nearby sensors within the specified environment. Every region's cluster head gathers the data that has been captured from all nearby sensor nodes. Every specified interval of time, a fault occurs, and the system calculates the data's median and takes its average. It determines the separation between each cluster head and the sensor value based on the distance and average data. The proposed method is rigorously tested, with the cluster head presumed to be the trustworthy node that reliably supplies the right data. The correctness is determined after taking into account the data from the dispersed cluster heads. With regard to the different parameters are to use for predicting the accuracy of data, fault positive rate and fault alarm ratio over the data transmission. This proposed model is contrasted with other ones already in use. The outcomes of the statistical analysis show that the proposed methodology produces an accurate result as compared to the traditional or existing approaches.
Twitter Data-based Sarcastic Sentiment Analysis using Deep Learning Framework

Nowadays, due to rapid growth in web technologies and internet usage, there will be lots of data generated and available in the webpages. People are using social media websites to connect with each other and to exchange their knowledge, opinions and thoughts through Facebook, Instagram, Twitter and Google this are common platform to all. In the current generation, an individual opinion is expressed on social media. These views represent a variety of sentiments, which range from extreme positive to extreme negative. Most of the organizations leverage those views in an attempt to market their products better. In this paper, we proposed the approach to identify the sarcasm in the tweets. The approach preprocessed the tweets. Deep Learning frame-work is proposed to identify the sarcasm in the tweets. RNN and LSTM approaches are used in which depth wise separable convolutions are used to build lightweight deep neural networks for sarcasm detection. Glove model is used for vector representations for words. The approach used simple sentence patterns to determine sarcasm which is very challenging as the sentences are short and simple. The proposed model has obtained 92% accuracy.
Sentiment Analysis of Twitter Dataset using Ensemble Classification Approach

On social media, words and phrases express people's opinions about companies, services, governments, and events. The objective of sentiment analysis in the discipline of natural language processing is to extract positive or negative polarities from social media text. The exponential growth of demands for businesses and governments motivates academics to complete sentiment analysis study. For sentiment analysis, we have proposed various machine learning algorithms like Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) and ensemble learning like Random Forest (RF), Gradient Boosting (GB), Voting Classifier (VC), XGBoost Classifier (XGBC), ADABoost Classifier (ADABC), Bagging Classifier (BC). Then the accuracy of each algorithm is compared and the result was quite good with GB. The accuracy of 94.99% is obtained with gradient descent.
Enhancement of Resistivity and Computation Speed through Non-Linear Feedback using Cipher Feedback Mode for AES

Cryptography is an area that offers secrecy for the transmission of confidential information's over unreliable networks. To achieve this secrecy, various algorithms or ciphers have been proposed in this area. One among these ciphers is Advanced Encryption Standard (AES). This work proposes new solutions on AES to enhance its immunity against attacks and speed of computation. The enhancement of efficiency is implemented through two non-linear feedback shift operations using Cipher Feedback Mode (CFB). An extra value-based rotation is added in every round to increase the immunity against attacks. To increase the computation speed, one round of AES is eliminated in processing every block of data. The proposed solution on AES is verified to determine the enhancement in immunity against attacks and gained speedup in execution.
Understanding Model Extraction Games

The privacy of machine learning models has become a significant concern in many emerging Machine-Learning-as- a-Service applications, where prediction services based on well- trained models are offered to users via the pay-per-query scheme. However, the lack of a defense mechanism can impose a high risk on the privacy of the server’s model since an adversary could efficiently steal the model by querying only a few ‘good’ data points. The game between a server’s defense and an adversary’s attack inevitably leads to an arms race dilemma, as commonly seen in Adversarial Machine Learning. To study the fundamental tradeoffs between model utility from a benign user’s view and privacy from an adversary’s view, we develop new metrics to quantify such tradeoffs, analyze their theoretical properties, and develop an optimization problem to understand the optimal adversarial attack and defense strategies. The developed concepts and theory match the empirical findings on the ‘equilibrium’ between privacy and utility. In terms of optimization, the key ingredient that enables our results is a unified representation of the attack-defense problem as a min-max bi-level problem. The developed results are demonstrated by examples and empirical experiments.
Enabling Location Based Services with Privacy and Integrity Protection in Untrusted Environments through Blockchain and Secure Computation

Privacy and integrity preservation of user data is a major challenge in the context of location based services, as the assumption of trusted relationship between the user and the service provider might be too strong. The question is: how to securely collect, store and process position, navigation and timing (PNT) information and/or georeferenced data, assuming that the service provider cannot be trusted? In this work, we propose an architecture that enables LBS with privacy and integrity in untrusted environments, leveraging blockchain and secure computation. We provide mechanisms for sharing and processing PNT information and/or georeferenced data, with a detailed description of the employed cryptographic schemes and algorithms. Furthermore, we provide a validation of the proposed architecture by means of an emulation-based testbed.
Person Authentication using Visual Representations of Keyboard Typing Dynamics

In this paper, we focus on the problem of user's authentication through typing dynamics patterns. We specifically focus on small-sized problems, where it is difficult to fully train corresponding machine (deep) learning algorithms from scratch. Instead, we propose a different approach based on the visualization of the typing patterns and subsequent usage of pre-trained feature extractors from the computer vision domain. We evaluated the approach on a publicly-available dataset and results indicate that this is a viable solution capable to improve over several baselines. Moreover, the proposed visual representation of the data contributes to the explainability of AI.
Developing Self-evolving Deepfake Detectors Against AI Attacks

As deep-learning based image and video manipulation technology advances, the future of truth and information looks bleak. In particular, Deepfakes, wherein a person’s face can be transferred onto the face of someone else, pose a serious threat for potential spread of convincing misinformation that is drastic and ubiquitous enough to have catastrophic real-world consequences. To prevent this, an effective detection tool for manipulated media is needed. However, the detector cannot just be good, it has to evolve with the technology to keep pace with or even outpace the enemy. At the same time, it must defend against different attack types to which deep learning systems are vulnerable. To that end, in this paper, we review various methods of both attack and defense on AI systems, as well as modes of evolution for such a system. Then, we put forward a potential system that combines the latest technologies in multiple areas as well as several novel ideas to create a detection algorithm that is robust against many attacks and can learn over time with unprecedented effectiveness and efficiency.
Important Factors to Improve Student Performance in the Universities of UAE: Examining the Mediating Effect of using M-Learning

This study aims to examine different factors that can improve student performance. Therefore, this study examined the impact of lecturers' influence, hedonic motivation, quality of service, and intention to use M -learning on student performance. This study also examined the mediating role of using m-learning. The proposed research model is examined using Structural Equation Modeling (SEM) through Smart PLS as a tool. The quantitative data were collected from students at the universities of the UAE. The usable questionnaires received back from the respondents were 399. Snowball sampling was used for data collection in the present study. The results of the study support the claim that lecturers' influence, hedonic motivation, quality of service and intention to use M -learning directly affect student performance. The mediation effect of M -learning was accepted in the present study. The findings are beneficial for policymakers and academicians for future studies and strategic development.
Using Artificial Intelligence to Resolve Disputes through Online Arbitration

This article discusses using Artificial Intelligence in the context of law, specifically online arbitration. The article starts with some theoretical basis discussing the concept of AI, its development, and its advantages and disadvantages. The practical part of this article focuses on the main concerns when using AI in online arbitration. The first concern is using AI to assist with the arbitrator's selection. The second concern is using AI to issue an arbitral award. The article concludes that AI can positively impact online arbitration while taking into consideration that human intervention is necessary in some cases.
Privacy Challenges and Solutions for Image Data Sharing

Sharing image data benefits a wide range of applications, including social media, medical imaging, and intelligent systems. Image data often contain sensitive information, the sharing of which may inflict individual privacy concerns. Traditional image privacy techniques, such as pixelization and blurring, do not provide effective protection. In this paper, we discuss privacy challenges and solutions for image data sharing. Specifically, we review existing solutions based on cryptography and federated learning, and discuss recent results on differential privacy in image domain. While differential privacy provides provable guarantees, we identify specific privacy challenges for image data and point out several considerations for future research.
Anomalous/Relevant Event Detection (A/RED): Active Machine Learning for Finding Rare Events

In many industrial applications, data comes in the form of an unlabeled stream, likely containing classes that a user has not seen before. In these cases, a user generally cares about four things: classification, class discovery, notification of events in certain classes, and the amount of data they need to label. In this work we present Anomalous/ Relevant Event Detection (A/RED), an active learning system that operates upon imbalanced data streams to find new classes and classify incoming events. A/RED is unique in that it takes into account user preference for the relevance of classes. An A/RED query involves asking for a label and a binary relevance label. A relevant class is queried more often, and as a result, the classifier performs better for these instances.
Implicit User Network Analysis of Communication Platform Open Data for Channel Recommendation

Recommender systems play a pivotal role in various human-centered online systems by filtering out relevant information from large databases. However, most recommender systems consume explicit private user information such as exchanged messages and information between users and items such as likes and shares without exploring other latent factors. Past events have shown that this can have decremental consequences on users' privacy. One type of application where alternative solutions have not yet been investigated are messaging platforms in larger corporate environments. These applications would benefit from recommender systems that consume only anonymized implicit data to enable employees to discover new communities and people. As a first step in developing such a recommender system, this paper describes the construction and analysis of implicit social network data from the messaging platform Mattermost at CERN and the extraction of measures for indicating similarity between users and channels. Additionally, it describes the use of these measures to evaluate multiple existing collaborative filter-based recommender systems, where their performances are compared and evaluated against simple measures. The evaluation results indicate that combining clustering approaches and custom features extracted through our data analysis outperforms standard collaborative filtering techniques. These results will be used in the future to create a new custom recommender system for messaging at CERN that only uses anonymized and implicit data.
Classifying Arabian Gulf Tweets to Detect People's Trends: A case study

Recently, media and business companies are utilizing social media to reach a large set of users to maximize the amount of gained profit. Actually, these companies are looking for the best ways to satisfy their user's requirements. It is very difficult to understand these requirements because of the large set of users on social media like Twitter. For this reason, the goal of our research project is to build a classifier that can detect Arabian trends among Gulf area Twitter users. The new built classifier can assist these companies to deliver the convenient products and media contents like photos and videos according to users' trends. By using our own designed Java-based tool, we have collected a significant dataset of tweets. Also, two experiments of tweet classification have been implemented to compare the effects of balanced and imbalanced training data and to measure the effect of data size on the accuracy of classifiers. In both experiments, Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Naïve Bayes algorithms are used as classifiers. The first experiment uses small, imbalanced data sets and four classes of data, which are Sport, Politics, Islam and Culture. The Light and Root Stemmers were used with each classifier. The best outcome achieved in our research project by utilizing a Naïve Bayes algorithm with the Light Stemmer technique. It achieved an accuracy reaching 76.27%. In the second experiment, we used a balanced large data set with the same classifiers. In addition, we have added one more class to the new data set which is Economics. The experimental results showed that the best accuracy (81.17%) is obtained by using SVM with the Light Stemmer method. The Light Stemmer achieved the best outcomes for all classifiers since almost all of the tweets were written in dialects.
Money Talks: Detection of Disposable Phishing Websites by Analyzing Its Building Costs

Websites unfortunately play a powerful role in delivering malicious content to users during cyberattacks. In particular, the threat of phishing websites that tricks users by abusing their corporate and brand names is increasing. Building a website requires infrastructure costs (e.g., domain name fees) and operational costs (e.g., managing server settings). Additionally, many companies spend considerable resources managing their own IT assets and security countermeasures. Even when phishing websites are taken down, attackers persist by scrapping and rebuilding them, as doing so is inexpensive. Notably, there are significant differences in website building costs between companies and attackers. In this study, we propose a method of analyzing the costs incurred in a process of building websites from domain name registration to website deployment to detect phishing websites. We evaluate our method using data from 1,082 large corporate websites and 1,163 phishing websites. As a result, our method achieves a detection performance of 95% precision and 96% recall. In addition, we show that our method still achieves a 95% recall for 866 phishing websites even after six months and the indicator of website building costs is robust to concept drift. We further discuss the applicability of the cost indicator.
ZAC: Efficient Zero-Knowledge Dynamic Universal Accumulator and Application to Zero-Knowledge Elementary Database

Zero-knowledge universal accumulator generates the succinct commitment to a set and produces the short (non) membership proof (universal) without leaking information about the set (zero-knowledge). In order to further support a generic set and zero-knowledge, existing techniques generally combine the zero-knowledge universal accumulator with other protocols, such as digital signatures and hashes to primes, which incur high overhead and may not be suitable for real-world use. It is desirable to commit a set of membership concealing the information with the optimal complexity. We devise ZAC, a new zero-knowledge Dynamic Universal Accumulator by taking the existing cryptographic primitives into account to produce a new efficient accumulator. Our underlying building blocks are Bloom Filter and vector commitment scheme in [19], utilizing the binary expression and aggregation to achieve efficiency, generic set support, zero-knowledge and universal properties. As a result, our scheme is improved in terms of proof size and proof time, also comparable to the RSA-based set accumulator in [8] in the verifying complexity. With 128 bit security, our proof size is 48 bytes while theirs is 1310 bytes and the running time of elliptic curve-based methods is faster than RSA-based counterpart. ZAC is proved to be complete, ϵ-sound and zero-knowledge. Extensively, based on ZAC as building block, we construct a new Zero-Knowledge Elementary Database (ZKEDB), which consumes 5 times less storage space, $\mathcal{O}\left( {\log N} \right)$ less bandwidth, and $\mathcal{O}\left( {\log N} \right)$ more efficient in proving and verification than the state-of-art work in [13] (where N is the domain space size). ZKEDB is proved to be complete, ϵ-sound and zero-knowledge. ZKEDB supports a new type of select top ℓ query, and can be extended to non-elementary databases.
SCGAN: Generative Adversarial Networks of Skip Connection for Face Image Inpainting

Deep learning has been widely applied for jobs involving face inpainting, however, there are usually some problems, such as incoherent inpainting edges, lack of diversity of generated images and other problems. In order to get more feature information and improve the inpainting effect, we therefore propose a Generative Adversarial Network of Skip Connection (SCGAN), which connects the encoder layers and the decoder layers by skip connection in the generator. The coherence and consistency of the image inpainting edges are improved, and the finer features of the image inpainting are refined, simultaneously using the discriminator's local and global double discriminators model. We also employ WGAN-GP loss to enhance model stability during training, prevent model collapse, and increase the variety of inpainting face images. Finally, experiments on the CelebA dataset and the LFW dataset are performed, and the model's performance is assessed using the PSNR and SSIM indices. Our model's face image inpainting is more realistic and coherent than that of other models, and the model training is more reliable.