Reviewerly/exHarmony huggingface dataset:
Stealthy DGoS Attack: DeGrading of Service Under the Watch of Network Tomography

Network tomography is a powerful tool to monitor the internal state of a closed network that cannot be measured directly, with broad applications in the Internet, overlay networks, and all-optical networks. However, existing network tomography solutions all assume that the measurements are trust-worthy, leaving open how effective they are in an adversarial environment with possibly manipulated measurements. To understand the fundamental limit of network tomography in such a setting, we formulate and analyze a novel type of attack that aims at maximally degrading the performance of targeted paths without being localized by network tomography. By analyzing properties of the optimal attack strategy, we formulate novel combinatorial optimizations to design the optimal attack strategy, which are then linked to well-known NP-hard problems and approximation algorithms. As a byproduct, our algorithms also identify approximations of the most vulnerable set of links that once manipulated, can inflict the maximum performance degradation. Our evaluations on real topologies demonstrate the large potential damage of such attacks, signaling the need of new defenses.
A Semantic Model-Based Security Engineering Framework for Cyber-Physical Systems

The coupling of safety-relevant embedded- and cyber-space components to build Cyber-Physical Systems (CPS) extends the functionality and quality in many business domains, while also creating new ones. Prime examples like Internet of Things and Industry 4.0 enable new technologies and extend the service capabilities of physical entities by building a universe of connected devices. In addition to higher complexity, the coupling of these heterogeneous systems results in many new challenges, which should be addressed by engineers and administrators. Here, security represents a major challenge, which may be well addressed in cyber-space engineering, but less in embedded system or CPS design. Although model-based engineering provides significant benefits for system architects, like reducing complexity and automated analysis, as well as being considered as standard methodology in embedded systems design, the aspect of security may not have had a major role in traditional engineering concepts. Especially the characteristics of CPS, as well as the coupling of safety-relevant (physical) components with high-scalable entities of the cyber-space domain have an enormous impact on the overall level of security, based on the introduced side effects and uncertainties. Therefore, we aim to define a model-based security-engineering framework, which is tailored to the needs of CPS engineers. Hereby, we focus on the actual modeling process, the evaluation of security, as well as quantitatively expressing security of a deployed CPS. Overall and in contrast to other approaches, we shift the engineering concepts on a semantic level, which allows to address the proposed challenges in CPS in the most efficient way.
Grid Structure Attention for Natural Language Interface to Bash Commands

Natural language interfaces are user interfaces that allow a human to interact with the computer by using natural languages. Mapping natural languages to bash commands is a novel interface problem and has been attracted great curiosity in recent years. Due to application domains of bash commands including the file system administration, the networking management, the file processing, etc, we need novel mechanisms of parameterization and normalization on this rich domain. In this paper, we propose a new mechanism, called GSAM (Grid Structure Attention Mechanism). This mechanism is used in the recurrent neural networks (RNNs) base in the sequence-to-sequence model. Our mechanism first uses a non-linear function to parameterize natural language sentences, and then use another model to capture the adjacency information of the natural language sentences. To show the robustness of GSAM, we reduce the training dataset of utility bash commands and GSAM still gets better performance in Template Matching scores and BLEU-scores.
PPCards: Toward Enhancing Electronic Prototyping with Editions of a Card-based Platform

Prototyping electronic circuits is often facilitated by web-based tutorials and breadboards. Several virtual and hybrid platforms do exist, each carrying their own limitations. Some of these platforms fall prey to split-attention effects, wherein users are required to split their attention to integrate multiple sources of spatially separated information. This hinders the learning and prototyping processes. Other platforms provide a single source of information, but lack tangible interaction with electronic components or suffer from the absence of active feedback which can also hinder these processes. There is hence a need for prototyping platforms that mitigate split attention effects, while continuing to provide other desirable aspects such as tangible interaction. To address this, we present three editions of PPCards, a card-based platform for prototyping electronic circuits, towards overcoming limitations of existing paradigms. Through a comparative study, it was determined that the first edition of PPCards outdid the conventional breadboard web-based tutorial paradigm in aspects of split attention, usability, and user experience. The second and third editions build upon successful characteristics of the first, additionally provisioning support for multimedia content and real-time feedback during the prototyping process. Based on quantitative data and qualitative feedback, we go on to discuss design considerations for future tangible card-based tools.
On the transferability of adversarial examples between convex and 01 loss models

The 01 loss gives different and more accurate boundaries than convex loss models in the presence of outliers. Could the difference of boundaries translate to adversarial examples that are non-transferable between 01 loss and convex models? We explore this empirically in this paper by studying transferability of adversarial examples between linear 01 loss and convex (hinge) loss models, and between dual layer neural networks with sign activation and 01 loss vs sigmoid activation and logistic loss. We first show that white box adversarial examples do not transfer effectively between convex and 01 loss and between 01 loss models compared to between convex models. As a result of this non-transferability we see that convex substitute model black box attacks are less effective on 01 loss than convex models. Interestingly we also see that 01 loss substitute model attacks are ineffective on both convex and 01 loss models mostly likely due to the non-uniqueness of 01 loss models. We show intuitively by example how the presence of outliers can cause different decision boundaries between 01 and convex loss models which in turn produces adversaries that are non-transferable. Indeed we see on MNIST that adversaries transfer between 01 loss and convex models more easily than on CIFAR10 and ImageNet which are likely to contain outliers. We show intuitively by example how the non-continuity of 01 loss makes adversaries non-transferable in a dual layer neural network. We discretize CIFAR10 features to be more like MNIST and find that it does not improve transferability, thus suggesting that different boundaries due to outliers are more likely the cause of non-transferability. As a result of this non-transferability we show that our dual layer sign activation network with 01 loss can attain robustness on par with simple convolutional networks.
Partial-SMT: Core-Scheduling Protection Against SMT Contention-Based Attacks

Numerous recent works in side-channel attacks have experimentally shown that Simultaneous Multi-Threading (SMT) inherently has a broader attack surface as it exposes more microarchitecture components per-core than cross-core. Existing mechanisms that protect against these attacks either incur high execution costs or are ineffective against certain attack variants. In this paper, we propose Partial-SMT, a system based on core-scheduling that protects security-critical programs from all contention-based attacks due to SMT. Partial-SMT allocates some complete physical cores for the exclusive use of the individual applications and provides a user-level threading library linked into each application to control the placement of their threads on dedicated cores, thereby preventing the attacker from accessing shared CPU resources simultaneously on the victim's core. The key insight is that by limiting ourselves to SMT contention-based side channels, we can translate the protection into an allocation policy that allocates or frees computing resources with a granularity of one physical core. Security-critical applications can be implemented on-demand and coexist with existing applications. We demonstrate that Partial-SMT effectively defeats typical SMT contention-based attacks. We modify AES and SPEC 2006 to use Partial-SMT, and they all incur the slight negligible performance overhead.
Towards a new Deep Learning Based Approach for the Password Prediction

Although tools for tracking and monitoring illegal networks have been developed for centuries, current methods available at the moment still need continues improvement. This is due to the fact that tracking and monitoring illegal networks in the cyberspace has become increasingly challenging for law enforcement agencies due to sophisticated encryption algorithms and strong passwords. Password predicting approaches will allow investigators to crack passwords used by criminals to protect their data or their communications. Hence, it will help judges to prosecute authors of crimes since they will have at their disposal all evidences needed which are, until now, hidden thanks to strong passwords. In this paper, we introduce a work-in-progress password guessing approach, called PassGuess to guess a missing character in a given password. PassGuess is powered by deep learning and it already predicts passwords with 80/% accuracy on the train set and 73\% accuracy on the test set for a random missing character in any position for a given password. This model is the very first preliminary work of the PassGuess intended for the purpose of the feasibility study and the authors of this paper are confident that the performance of PassGuess can be improved significantly in the future.
Quantitative Shape Measurement of an Inflatable Rubber Dam Using an Array of Inertial Measurement Units

Shape measurement plays an important role in the condition monitoring and operation control of inflatable rubber dams. This article presents a method to measure the cross-sectional shape of a rubber dam using an array of inertial measurement units (IMUs) placed on the circumference of the dam. Accelerometer and gyroscope measurements are combined using an adaptive complementary filter to determine the tangent angles of the dam circumference. The adaptive complementary filter adjusts the weights of the accelerometer and gyroscope measurements dynamically in order to reduce the uncertainty in orientation estimation due to external acceleration under dynamic conditions. A natural cubic spline that interpolates the measured tangent angles at discrete locations is used to represent the tangent angles along the dam circumference as a continuous function of the arc length. Finally, the cross-sectional shape is reconstructed by integrating the continuous tangent angle function along the circumference of the dam. Experimental assessment of the measurement system was performed on a purpose-built test rig using a digital camera as a reference measuring device. Results under a typical static condition show that the measured and reference shapes agree well with each other, with a similarity index being 3.74%, a mismatch distance of the last IMU node being 12.3 mm, and a relative error of height measurement being -2.44%. Under dynamic conditions, the measurement results deteriorate due to external acceleration, but considerable improvement is achieved in comparison with an accelerometer-only approach. In addition, the elimination of faulty nodes from shape reconstruction has negligible influence on the results, suggesting that the measurement system enjoys a high degree of fault tolerance.
A Regularized Tensor Network for Cyclone Wind Speed Estimation

Maximum wind speed (MWS) is an important characteristic of tropical cyclone (TC). Estimation of MWS with remote sensing images of TCs via machine learning is a relatively new and challenging task. Here we propose a novel and effective method, Regularized Tensor Network (RTN), to estimate MWS using multispectral images (MSIs). RTN is a transductive regression model, built on a deep Tensor Network (TN) combined with two regularizations: manifold learning and categorization error. Experimental results showed that RTN outperformed several classic regression methods as well as advanced models based on deep learning.
A New Feature to Secure Web Applications

Web application security is one of essential components of any web-based systems. As becoming popular of the Internet makes many web sites be attacked by many kinds of attacks. So, we have to propose a secure framework for web applications. For this reason, we have to propose a web application framework which not only analyzes the source code implemented by web developers, also it can detects the vulnerabilities in the source code dynamically. Although a lot of research works have already proposed detection methods of vulnerabilities in web application attacks, but those are not fully detected because their methods do not use the information of the web applications. Therefore, we propose a new method which analyzes the source code of a web application, and then modifies it if needed, in addition, our method has a detection method of an application's vulnerabilities that are difficult to detect by previous methods. According to our implementation and experiments, it is possible to detect actual attacks, which have been considered difficult to detect, against authentication leaks and SQL injection attacks using dynamic queries.
Formal modelling and verification of a multi-agent negotiation approach for airline operations control

Abstract This paper proposes and evaluates a new airline disruption management strategy using multi-agent system modelling, simulation, and verification. This new strategy is based on a multi-agent negotiation protocol and is compared with three airline strategies based on established industry practices. The application concerns Airline Operations Control whose core functionality is disruption management. To evaluate the new strategy, a rule-based multi-agent system model of the AOC and crew processes has been developed. This model is used to assess the effects of multi-agent negotiation on airline performance in the context of a challenging disruption scenario. For the specific scenario considered, the multi-agent negotiation strategy outperforms the established strategies when the agents involved in the negotiation are experts. Another important contribution is that the paper presents a logic-based ontology used for formal modelling and analysis of AOC workflows.
I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering

In this work we propose SRE, the first internal evaluation measure for arbitrary oriented subspace clustering results. For this purpose we present a new perspective on the subspace clustering task: the goal we formalize is to compute a clustering which represents the original dataset by minimizing the reconstruction loss from the obtained subspaces, while at the same time minimizing the dimensionality as well as the number of clusters. A fundamental feature of our approach is that it is model-agnostic, i.e., it is independent of the characteristics of any specific subspace clustering method. It is scale invariant and mathematically founded. The experiments show that the SRE scoring better assesses the quality of an arbitrarily oriented sub-space clustering compared to commonly used external evaluation measures.
Optical Chaos KEY generator for Cryptosystems

Abstract Chaotic structures are ideally appropriate for the construction of cryptographic schemes, because random numbers are an inseparable part of every encryption scheme as a safety mechanism. The optical chaotic circuit is an ultra-fast physical Random number generator, which is illustrated using the CW laser that generates optical signal at 193.1 THz and M-Z modulator that works on an interferometric principle. The circuit generates truly high random bit sequences at different higher bit rates of 320Gb/s and 640 Gb/s. These generated random bits have passed the NIST statistical tests and Diehard battery tests, indicating their randomness for different practical applications. Further, the random number generated by optical chaos circuit is used to secure the image data using RC4 ciphering method.
FPGA Oriented Compression of DNN Using Layer-Targeted Weights and Activations Quantization

In recent years, complex Deep neural networks (DNNs) have achieved state-of-the-art results in a wide range of real-world tasks. However, the gains in their performance lead to an increase in computations and frequent access to external memory slow down the FPGA inference speed. To address this problem, our research proposes an algorithm-hardware co-design, i.e. a layer-targeted quantization combined with an FPGA streaming architecture to reduce the computations, memory bandwidth and circuit size, specifically the bit numbers, Block Random Access Memories (BRAMs) size and the number of Digital Signal Processing (DSP) blocks while keeping a comparable accuracy drop compared to recent state-of-the-art. Our layer-targeted quantization method balances the bitwidth and model's accuracy drop based on the distribution of weights and activations. Particularly, by repeating simulations, the quantization range and step size are determined to minimize the bitwidth while the accuracy decreases slightly. Moreover, for the large-scale network, the activation function CReLU is used complying with quantization to further improve the accuracy drop. While training the quantized network, a backward method using Straight Through Estimator is proposed to help the quantized model to be converged and retain the accuracy. Our method's efficiency is demonstrated by two experimental results on small-scale network VGG16 with CIFAR-10 and large-scale one VGG16-SSD with VOC07+12 benchmark. The first result shows the model size reduction of nearly 32× for weights and more than 5× for activations with a small loss of accuracy 1.26%. While the second one expresses a comparable accuracy drop of 1.8% with most of state-of-the-art results. For the implementation of CNNs on FPGA, a pipelined streaming accelerator for VGG16-SSD is presented. To minimize the number of on-chip buffer of the large-scale network, together with the software quantization, a data-path optimization technique named Input Feature Map Fully Reuse is applied. Preliminary calculations show that the required BRAMs size is 2974 blocks and the DSP blocks are 552 which is significantly smaller than those of recent published works.
Feasibility analysis of autonomous hybrid solar-wind system for household consumption: a case study

Abstract The constant increase of electricity prices from the power grid, rising fossil fuel prices and environmental motives make people look for opportunities to supply their homes with renewable energy independently from the grid. The easy technical feasibility of the projects and the climatic features of Bulgaria determine the solar and wind energy as the most suitable for the households. The variability of solar and wind energy predetermines their shared use in hybrid systems. Feasibility analysis of autonomous hybrid solar-wind system for household consumption was made in the article. The calculations were made for a residential building located in the village of Okop, Yambol District, Bulgaria. It has been verified if the levelized cost of energy (LCOE) of the domestic solar-wind hybrid system is in parity with the final cost of the grid electricity. Different variants of system configurations and different ways of financing were considered. The results show that for the considered place, the prices of electricity produced by autonomous hybrid systems are higher than the prices of electricity for household customers connected to the power grid. The use of standalone hybrid systems is only justified when there is no power supply from the grid. Lower average prices than those of the power grid are obtained only when building a photovoltaic plant connected to the grid.
Category-oriented Sentiment Polarity Dictionary for Rating Prediction of Japanese Hotels

Hotel booking sites provide us hotel evaluations, each of which includes a textual review and numeric ratings for multiple categories such as Service, Location, Rooms, etc., submitted by a hotel guest. However, some evaluations have no numeric rating. Also, textual reviews and numeric ratings of some evaluations are inconsistent. For example, a good textual review is submitted with low ratings. Such evaluations may confuse site users. To resolve such problems, we propose a high accuracy method to predict a numeric rating for each category from a textual review. Our new idea is to use Category-oriented Sentiment Polarity Dictionaries (CSPD), each of which is automatically compiled for each category using a Rakuten Travel review database in advance. The CSPD gives the sentiment polarity value (i.e., the positivity/negativity value) for each sentiment word such as "bad", "light", and "delicious" for each category. In our experiments, CSPD showed higher precision in rating prediction than the existing dictionaries when we use only sentiment polarity values for rating prediction. We also combine the vectorization method of words and the CSPD to obtain an expected rating value from a textual review. Our experimental results show that our combined method attains higher accuracy than the previously published sentence vectorization method.
Face Morphing Attack Detection in the Presence of Post-processed Image Sources Using Neighborhood Component Analysis and Decision Tree Classifier

Recently, Face Morphing Attack Detection (MAD) has gained a great deal of attention as criminals have started to use freely and easily available digital manipulation techniques to combine two or more subject facial images to create a new facial image that can be viewed as an accurate image of any of the individual images that constitute it. Some of these morphing tools create morphed images of high quality which pose a serious threat to existing Face Recognition Systems (FRS). In the literatures, it has been identified that FRS is vulnerable to multiform morphing attacks. Based on this vulnerability, several types of research on the detection of this morph attack was conducted using several techniques. Despite the remarkable levels of MAD reported in various literature, so far no suitable solution has been found to handle post-processed images such as images modified after morphing with sharpening operation that can dramatically reduce visible artifacts of morphed photos. In this work, an approach is proposed for MAD before image post-processing and after image post-processing built on a combination of Local Binary Pattern (LBP) for extraction of feature, Neighborhood Component Analysis (NCA) for selection of features and classification using K-Nearest Neighbor (KNN), Decision Tree Classifier (DTC) and Naïve Bayes (NB) classifier. The outcome gotten by training the different classifiers with feature vectors selected using the NCA algorithm improved the classification accuracy from 90% to 94%, consequently improving the general performance of the MAD.