OpenRaiser/Intern-Atlas huggingface dataset:
The security of the Coordicide: the implementation and analysis of possible attack vectors
Venue: arXiv cs.CR 2022
Year: 2022

The goal of the thesis is to study and perform an analysis of the possible attack vectors on the Iota network 2.0 version of the protocol. In this work, existing attack vectors on Distributed Ledger Technologies are studied and their applicability to the Iota 2.0 protocol is discussed. A specific attack that targets the capability of honest participants to write to the ledger is presented and analyzed in a network of nodes that run a full node software version.
Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning
Venue: arXiv cs.CR 2022
Year: 2022

As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feature sets have been presented to date, often reporting impresive detection performances. However, the lack of reproducibility and the absence of a standard evaluation framework make these proposals difficult to compare. In this paper, we perform an analysis of 10 influential research works on Android malware detection using a common evaluation framework. We have identified five factors that, if not taken into account when creating datasets and designing detectors, significantly affect the trained ML models and their performances. In particular, we analyze the effect of (1) the presence of duplicated samples, (2) label (goodware/greyware/malware) attribution, (3) class imbalance, (4) the presence of apps that use evasion techniques and, (5) the evolution of apps. Based on this extensive experimentation, we conclude that the studied ML-based detectors have been evaluated optimistically, which justifies the good published results. Our findings also highlight that it is imperative to generate realistic experimental scenarios, taking into account the aforementioned factors, to foster the rise of better ML-based Android malware detection solutions.
On Secure NOMA-CDRT Systems with Physical Layer Network Coding
Venue: arXiv cs.IT 2022
Year: 2022

This paper proposes a new scheme to enhance the secrecy performance of a NOMA-based coordinated direct relay transmission system (NOMA-CDRT) with an untrusted relay. The physical-layer network coding and the non-orthogonal multiple access scheme are combined to improve the spectrum efficiency. Furthermore, inter-user interference and friendly jamming signals are utilized to suppress the eavesdropping ability of the untrusted relay without affecting the acceptance quality of legitimate users. Specifically, the far user in the first slot and the near user in the second slot act as jammers to generate jamming signals to ensure secure transmissions of the confidential signals. We investigate the secrecy performance of the proposed scheme in NOMA-CDRT systems and derive the closed-form expression for the ergodic secrecy sum rate. The asymptotic analysis at high signal-to-noise ratio is performed to obtain more insights. Finally, simulation results are presented to demonstrate the effectiveness of the proposed scheme and the correctness of the theoretical analysis.
Heterogeneous Reservoir Computing Models for Persian Speech Recognition
Venue: arXiv cs.SD 2022
Year: 2022

Over the last decade, deep-learning methods have been gradually incorporated into conventional automatic speech recognition (ASR) frameworks to create acoustic, pronunciation, and language models. Although it led to significant improvements in ASRs' recognition accuracy, due to their hard constraints related to hardware requirements (e.g., computing power and memory usage), it is unclear if such approaches are the most computationally- and energy-efficient options for embedded ASR applications. Reservoir computing (RC) models (e.g., echo state networks (ESNs) and liquid state machines (LSMs)), on the other hand, have been proven inexpensive to train, have vastly fewer parameters, and are compatible with emergent hardware technologies. However, their performance in speech processing tasks is relatively inferior to that of the deep-learning-based models. To enhance the accuracy of the RC in ASR applications, we propose heterogeneous single and multi-layer ESNs to create non-linear transformations of the inputs that capture temporal context at different scales. To test our models, we performed a speech recognition task on the Farsdat Persian dataset. Since, to the best of our knowledge, standard RC has not yet been employed to conduct any Persian ASR tasks, we also trained conventional single-layer and deep ESNs to provide baselines for comparison. Besides, we compared the RC performance with a standard long-short-term memory (LSTM) model. Heterogeneous RC models (1) show improved performance to the standard RC models; (2) perform on par in terms of recognition accuracy with the LSTM, and (3) reduce the training time considerably.
ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation
Venue: arXiv cs.CV 2022
Year: 2022

Data augmentation (DA) is a widely used technique for enhancing the training of deep neural networks. Recent DA techniques which achieve state-of-the-art performance always meet the need for diversity in augmented training samples. However, an augmentation strategy that has a high diversity usually introduces out-of-distribution (OOD) augmented samples and these samples consequently impair the performance. To alleviate this issue, we propose ReSmooth, a framework that firstly detects OOD samples in augmented samples and then leverages them. To be specific, we first use a Gaussian mixture model to fit the loss distribution of both the original and augmented samples and accordingly split these samples into in-distribution (ID) samples and OOD samples. Then we start a new training where ID and OOD samples are incorporated with different smooth labels. By treating ID samples and OOD samples unequally, we can make better use of the diverse augmented data. Further, we incorporate our ReSmooth framework with negative data augmentation strategies. By properly handling their intentionally created OOD samples, the classification performance of negative data augmentations is largely ameliorated. Experiments on several classification benchmarks show that ReSmooth can be easily extended to existing augmentation strategies (such as RandAugment, rotate, and jigsaw) and improve on them. Our code is available at https://github.com/Chenyang4/ReSmooth.
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization
Venue: arXiv cs.LG 2022
Year: 2022

We tackle real-world problems with complex structures beyond the pixel-based game or simulator. We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph that defines a set of subtasks and their dependencies that are unknown to the agent. Different from the previous meta-rl methods trying to directly infer the unstructured task embedding, our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks, and use it as a prior to improve the task inference in testing. Our experiment results on 2D grid-world and complex web navigation domains show that the proposed method can learn and leverage the common underlying structure of the tasks for faster adaptation to the unseen tasks than various existing algorithms such as meta reinforcement learning, hierarchical reinforcement learning, and other heuristic agents.
Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages
Venue: arXiv cs.CL 2022
Year: 2022

Scaling multilingual representation learning beyond the hundred most frequent languages is challenging, in particular to cover the long tail of low-resource languages. A promising approach has been to train one-for-all multilingual models capable of cross-lingual transfer, but these models often suffer from insufficient capacity and interference between unrelated languages. Instead, we move away from this approach and focus on training multiple language (family) specific representations, but most prominently enable all languages to still be encoded in the same representational space. To achieve this, we focus on teacher-student training, allowing all encoders to be mutually compatible for bitext mining, and enabling fast learning of new languages. We introduce a new teacher-student training scheme which combines supervised and self-supervised training, allowing encoders to take advantage of monolingual training data, which is valuable in the low-resource setting. Our approach significantly outperforms the original LASER encoder. We study very low-resource languages and handle 50 African languages, many of which are not covered by any other model. For these languages, we train sentence encoders, mine bitexts, and validate the bitexts by training NMT systems.
Understanding Natural Language in Context
Venue: arXiv cs.CL 2022
Year: 2022

Recent years have seen an increasing number of applications that have a natural language interface, either in the form of chatbots or via personal assistants such as Alexa (Amazon), Google Assistant, Siri (Apple), and Cortana (Microsoft). To use these applications, a basic dialog between the robot and the human is required. While this kind of dialog exists today mainly within "static" robots that do not make any movement in the household space, the challenge of reasoning about the information conveyed by the environment increases significantly when dealing with robots that can move and manipulate objects in our home environment. In this paper, we focus on cognitive robots, which have some knowledge-based models of the world and operate by reasoning and planning with this model. Thus, when the robot and the human communicate, there is already some formalism they can use - the robot's knowledge representation formalism. Our goal in this research is to translate natural language utterances into this robot's formalism, allowing much more complicated household tasks to be completed. We do so by combining off-the-shelf SOTA language models, planning tools, and the robot's knowledge-base for better communication. In addition, we analyze different directive types and illustrate the contribution of the world's context to the translation process.
Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models
Venue: arXiv cs.CL 2022
Year: 2022

Model compression by way of parameter pruning, quantization, or distillation has recently gained popularity as an approach for reducing the computational requirements of modern deep neural network models for NLP. Inspired by prior works suggesting a connection between simpler, more generalizable models and those that lie within wider loss basins, we hypothesize that optimizing for flat minima should lead to simpler parameterizations and thus more compressible models. We propose to combine sharpness-aware minimization (SAM) with various task-specific model compression methods, including iterative magnitude pruning (IMP), structured pruning with a distillation objective, and post-training dynamic quantization. Empirically, we show that optimizing for flatter minima consistently leads to greater compressibility of parameters compared to vanilla Adam when fine-tuning BERT models, with little to no loss in accuracy on the GLUE text classification and SQuAD question answering benchmarks. Moreover, SAM finds superior winning tickets during IMP that 1) are amenable to vanilla Adam optimization, and 2) transfer more effectively across tasks.
jTrans: Jump-Aware Transformer for Binary Code Similarity
Venue: arXiv cs.CR 2022
Year: 2022

Binary code similarity detection (BCSD) has important applications in various fields such as vulnerability detection, software component analysis, and reverse engineering. Recent studies have shown that deep neural networks (DNNs) can comprehend instructions or control-flow graphs (CFG) of binary code and support BCSD. In this study, we propose a novel Transformer-based approach, namely jTrans, to learn representations of binary code. It is the first solution that embeds control flow information of binary code into Transformer-based language models, by using a novel jump-aware representation of the analyzed binaries and a newly-designed pre-training task. Additionally, we release to the community a newly-created large dataset of binaries, BinaryCorp, which is the most diverse to date. Evaluation results show that jTrans outperforms state-of-the-art (SOTA) approaches on this more challenging dataset by 30.5% (i.e., from 32.0% to 62.5%). In a real-world task of known vulnerability searching, jTrans achieves a recall that is 2X higher than existing SOTA baselines.
Secure and Efficient Tunneling of MACsec for Modern Industrial Use Cases
Venue: arXiv cs.CR 2022
Year: 2022

Trends like Industry 4.0 will pose new challenges for future industrial networks. Greater interconnectedness, higher data volumes as well as new requirements for speeds as well as security will make new approaches necessary. Performanceoptimized networking techniques will be demanded to implement new use cases, like network separation and isolation, in a secure fashion. A new and highly efficient protocol, that will be vital for that purpose, is MACsec. It is a Layer 2 encryption protocol that was previously extended specifically for industrial environments. Yet, it lacks the ability to bridge local networks. Therefore, in this work, we propose a secure and efficient Layer 3 tunneling scheme for MACsec. We design and implement two approaches, that are equally secure and considerably outperform comparable state-of-the-art techniques.
A Case for Practical Configuration Management Using Hardware-based Security Tokens
Venue: arXiv cs.CR 2022
Year: 2022

Future industrial networks will consist of a complex mixture of new and legacy components, while new use cases and applications envisioned by Industry 4.0 will demand increased flexibility and dynamics from these networks. Industrial security gateways will become an important building block to tackle new security requirements demanded by these changes. Their introduction will further increase the already high complexity of these networks, demanding more efforts in properly and securely configuring them. Yet, past research showed, that most operators of industrial networks are already today unable to configure industrial networks in a secure fashion. Therefore, we propose a scheme that allows factory operators to configure security gateways in an easy and practical way that is also understandable for staff not trained in the security domain. We employ hardware security tokens that allow to reduce every day configuration to one physical interaction. Our results show the practical feasibility of our proposed scheme and that it does not reduce the security level of industrial security gateways in any way.
SAT Preprocessors and Symmetry
Venue: arXiv cs.LO 2022
Year: 2022

Exploitation of symmetries is an indispensable approach to solve certain classes of difficult SAT instances. Numerous techniques for the use of symmetry in SAT have evolved over the past few decades. But no matter how symmetries are used precisely, they have to be detected first. We investigate how to detect more symmetry, faster. The initial idea is to reap the benefits of SAT preprocessing for symmetry detection. As it turns out, applying an off-the-shelf preprocessor before handling symmetry runs into problems: the preprocessor can haphazardly remove symmetry from formulas, severely impeding symmetry exploitation. Our main contribution is a theoretical framework that captures the relationship of SAT preprocessing techniques and symmetry. Based on this, we create a symmetry-aware preprocessor that can be applied safely before handling symmetry. We then demonstrate that applying the preprocessor does not only substantially decrease symmetry detection and breaking times, but also uncovers hidden symmetry not detectable in the original instances. Overall, we depart the conventional view of treating symmetry detection as a black-box, presenting a new application-specific approach to symmetry detection in SAT.
Real-time Visualization of Stream-based Monitoring Data
Venue: arXiv cs.FL 2022
Year: 2022

Stream-based runtime monitors are used in safety-critical applications such as Unmanned Aerial Systems (UAS) to compute comprehensive statistics and logical assessments of system health that provide the human operator with critical information in hand-over situations. In such applications, a visual display of the monitoring data can be much more helpful than the textual alerts provided by a more traditional user interface. This visualization requires extensive real-time data processing, which includes the synchronization of data from different streams, filtering and aggregation, and priorization and management of user attention. We present a visualization approach for the \rtlola monitoring framework. Our approach is based on the principle that the necessary data processing is the responsibility of the monitor itself, rather than the responsibility of some external visualization tool. We show how the various aspects of the data transformation can be described as RTLola stream equations and linked to the visualization component through a bidirectional synchronous interface. In our experience, this approach leads to highly informative visualizations as well as to understandable and easily maintainable monitoring code.
Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images
Venue: arXiv cs.CV 2022
Year: 2022

We present a novel technique to automatically colorize grayscale images that combine the U-Net model and Fusion Layer features. This approach allows the model to learn the colorization of images from pre-trained U-Net. Moreover, the Fusion layer is applied to merge local information results dependent on small image patches with global priors of an entire image on each class, forming visually more compelling colorization results. Finally, we validate our approach with a user study evaluation and compare it against state-of-the-art, resulting in improvements.
Robust Reinforcement Learning on Graphs for Logistics optimization
Venue: arXiv cs.LG 2022
Year: 2022

Logistics optimization nowadays is becoming one of the hottest areas in the AI community. In the past year, significant advancements in the domain were achieved by representing the problem in a form of graph. Another promising area of research was to apply reinforcement learning algorithms to the above task. In our work, we made advantage of using both approaches and apply reinforcement learning on a graph. To do that, we have analyzed the most recent results in both fields and selected SOTA algorithms both from graph neural networks and reinforcement learning. Then, we combined selected models on the problem of AMOD systems optimization for the transportation network of New York city. Our team compared three algorithms - GAT, Pro-CNN and PTDNet - to bring to the fore the important nodes on a graph representation. Finally, we achieved SOTA results on AMOD systems optimization problem employing PTDNet with GNN and training them in reinforcement fashion. Keywords: Graph Neural Network (GNN), Logistics optimization, Reinforcement Learning
Cryptocurrency Giveaway Scam with YouTube Live Stream
Venue: arXiv cs.CR 2022
Year: 2022

This paper investigates the cryptocurrency giveaway scam with the YouTube live stream carried out on 5/15/2022 and 5/16/2022. In this scam scheme, the scammer plays a recorded video of a famous person in a YouTube live stream annotated with a cryptocurrency giveaway announcement. In the annotated announcement, the victims are directed to the scammer's webpage. The scammer's webpage is designed intelligently to deceive victims such that they believe the legitimacy of the giveaway. The scammer claims that whatever donation the victim sends to a cryptocurrency wallet address, the giveaway scheme will double the donated amount and immediately send it back to the victim. By analyzing the scammers' wallet addresses, it can be seen that scammers could steal a significant amount of money in a short time. After analyzing the attackers' techniques, tactics, and procedures, this paper discusses the countermeasures that can be applied to mitigate such a fraudulent activity in the future.
Information Set Decoding for Lee-Metric Codes using Restricted Balls
Venue: arXiv cs.IT 2022
Year: 2022

The Lee metric syndrome decoding problem is an NP-hard problem and several generic decoders have been proposed. The observation that such decoders come with a larger cost than their Hamming metric counterparts make the Lee metric a promising alternative for classical code-based cryptography. Unlike in the Hamming metric, an error vector that is chosen uniform at random of a given Lee weight is expected to have only few entries with large Lee weight. Using this expected distribution of entries, we are able to drastically decrease the cost of generic decoders in the Lee metric, by reducing the original problem to a smaller instance, whose solution lives in restricted balls.
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series
Venue: arXiv cs.LG 2022
Year: 2022

Deep learning has become a one-size-fits-all solution for technical and business domains thanks to its flexibility and adaptability. It is implemented using opaque models, which unfortunately undermines the outcome trustworthiness. In order to have a better understanding of the behavior of a system, particularly one driven by time series, a look inside a deep learning model so-called posthoc eXplainable Artificial Intelligence (XAI) approaches, is important. There are two major types of XAI for time series data, namely model-agnostic and model-specific. Model-specific approach is considered in this work. While other approaches employ either Class Activation Mapping (CAM) or Attention Mechanism, we merge the two strategies into a single system, simply called the Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series (TSEM). TSEM combines the capabilities of RNN and CNN models in such a way that RNN hidden units are employed as attention weights for the CNN feature maps temporal axis. The result shows that TSEM outperforms XCM. It is similar to STAM in terms of accuracy, while also satisfying a number of interpretability criteria, including causality, fidelity, and spatiotemporality.
People counting system for retail analytics using edge AI
Venue: arXiv cs.LG 2022
Year: 2022

Developments in IoT applications are playing an important role in our day-to-day life, starting from business predictions to self driving cars. One of the area, most influenced by the field of AI and IoT is retail analytics. In Retail Analytics, Conversion Rates - a metric which is most often used by retail stores to measure how many people have visited the store and how many purchases has happened. This retail conversion rate assess the marketing operations, increasing stock, store outlet and running promotions ..etc. Our project intends to build a cost-effective people counting system with AI at Edge, where it calculates Conversion rates using total number of people counted by the system and number of transactions for the day, which helps in providing analytical insights for retail store optimization with a very minimum hardware requirements.