Reviewerly/exHarmony huggingface dataset:
Improving Sinhala Hate Speech Detection Using Deep Learning

Automatic Hate Speech Detection is a fine-grained sentiment analysis task that has been the focus of many researchers around the world. This has been a difficult task due to challenges such as the usage of native languages and distinct vocabularies, as well as the distortion of words. However, based on the findings of previous studies on Sinhala hate speech identification, this has proven to be more difficult for low-resource languages like Sinhala. The effectiveness of pretrained embedding for Sinhala hate speech detection has not been investigated. We investigated several embeddings as well as frequency-based features, including bag of words, n-grams, and TF-IDF to address this shortcoming. We present results from several machine learning experiments, including deep learning experiments and transfer learning experiments on state-of-the-art cross-lingual transformers. With an f1-score of 0.764 and a recall value of 0.788 in our study, the XLMR model outperformed other baseline algorithms and deep learning models.
Employing Super Resolution to Improve Low-Quality Deepfake Detection

The rapid progress in deepfake content generation has now come to a point where it raises significant concerns about the implications for society. Therefore, a new challenge of detecting deepfakes arises to protect individuals from potential misuse. Even though introduced detection algorithms perform well on high-quality deepfakes, detecting low-quality deepfakes has been challenging. As a remedy, researchers try to feed more training data to increase detection ability. HoWever, providing more data and processing them is not always feasible in a practical scenario. Thus, for the first time in this domain, we propose to employ super-resolution (SR) as a preprocessing step instead of feeding more data to improve low-quality deepfake detection. Extensive experiments were conducted on the FaceForensics++ deepfake dataset. Initially, three baseline models, Meso-4, MesoInception-4, and XceptionNet, were trained and tested on the dataset without any preprocessing mechanism. XceptionNet outperformed with 90.54% accuracy revealing deeper networks detect low-quality depfakes adequately. Then those baseline models were trained with SR preprocessing. To do that, we employed two SR networks, called VDSR and RSRGAN. RESRGAN+XceptionNet outperformed the previous baseline models by obtaining 96.05% accuracy, showing SR preprocessing usefulness in low-quality deepfake detection. Further experiments utilizing performance metrics, statistical tests, and visualization of activation maps showed that SR preprocessing is promising when applied to deepfake detection networks and detection algorithms experience a significant performance.
Business Model for Shared Energy Generation with Peer-To-Peer Trading via Blockchain

Blockchain technology brings numerous varieties of applications, and within them, the electric energy area stands out. To apply this technology in the sector, it is necessary to understand its characteristics and the needs of users. This paper presents the main characteristics of blockchain technology, a survey carried out with users of shared generation, and, using the data collected together with a market research, a business model was developed with short, medium and long expectations.
Generating Deep Learning Model-Specific Explanations at the End User’s Side

End users who cannot afford to collect and label big data to train accurate deep learning (DL) models resort to Machine Learning as a Service (MLaaS) providers, who provide paid access to accurate DL models. However, the lack of transparency in how the providers’ models make predictions causes a problem of trust. A way to increase trust (and also to align with ethical regulations) is for predictions to be accompanied by explanations locally and independently generated by the end users (rather than by explanations offered by the model providers). Explanation methods using internal components of DL models (a.k.a. model-specific explanations) are more accurate and effective than those relying solely on the inputs and outputs (a.k.a. model-agnostic explanations). However, end users lack white-box access to the internal components of the providers’ models. To tackle this issue, we propose a novel approach allowing an end user to locally generate model-specific explanations for a DL classification model accessed via a provider’s API. First, we approximate the provider’s model with a local surrogate model. We then use the surrogate model’s components to locally generate model-specific explanations that approximate the explanations obtainable with white-box access to the provider’s DL model. Specifically, we leverage the surrogate model’s gradients to generate adversarial examples that counterfactually explain why an input example is classified into a specific class. Our approach only requires the end user to have unlabeled data of size [Formula: see text] of the provider’s training data and with a similar distribution; given the small size and unlabeled nature of these data, they can be assumed to be already available to the end user or even to be supplied by the provider to build trust in his model. We demonstrate the accuracy and effectiveness of our approach through extensive experiments on two ML tasks: image classification and tabular data classification. The locally generated explanations are consistent with those obtainable with white-box access to the provider’s model, thus giving end users an independent and reliable way to determine if the provider’s model is trustworthy.
Deep Learning-based Interference Detection and Classification for LPI/LPD Radar Systems

In this paper, we propose an interference detection and classification (IDC) algorithm for low probability of intercept/detection (LPI/LPD) radar communications. Specifically, considering LPI/LPD radar systems with frequency-modulated continuous-wave (FMCW) waveforms, an IDC algorithm based on anomaly detection (AD) and multi-class signal classification is proposed in this paper. First, a mathematical model of the received signal is constructed, when the radar signal reflected by a target interferes a Gaussian jammer and illegitimate in-band FMCW waveform. Next, the received signal is represented in time and frequency domains using time-frequency distribution (TFD). Then, the IDC is trained using the TFD and In-phase and Quadrature (I/Q) representations of the received signal as features. Using the proposed IDC algorithm, we can classify four distinct interference signal types with an accuracy greater than 97 %. In terms of true positive ratio (TPR) and false positive ratio (FPR), simulation results demonstrate that the proposed algorithm outperforms other existing algorithms.
Classification of Radio Signals Using Truncated Gaussian Discriminant Analysis of Convolutional Neural Network-Derived Features

To improve the utility and scalability of distributed radio frequency (RF) sensor and communication networks, reduce the need for convolutional neural network (CNN) retraining, and efficiently share learned information about signals, we examined a supervised bootstrapping approach for RF modulation classification. We show that CNN-bootstrapped features of new and existing modulation classes can be considered as mixtures of truncated Gaussian distributions, allowing for maximum-likelihood-based classification of new classes without retraining the network. In this work, the authors observed classification performance using maximum likelihood estimation of CNN-bootstrapped features to be comparable to that of a CNN trained on all classes, even for those classes on which the bootstrapping CNN was not trained. This performance was achieved while reducing the number of parameters needed for new class definition from over 8 million to only 200.
Information-Based Mobile Sensor Network Localization and Tracking of an Uncooperative Target

This paper focuses on the localization and tracking of a radio frequency (RF) emitter with unknown waveform (uncooperative target) using an information-based centralized network of mobile, collaborative sensors (agents). Each agent utilizes the measured waveform parameters such as time difference of arrival (TDOA), frequency difference of arrival (FDOA), or differential received signal strength (DRSS). The proposed algorithm leverages Bayesian estimation to handle non-Gaussian measurements and it employs information-theoretic motion planning to move sensors to areas of high information-gain to minimize measurement uncertainty. Mutual information is computed for pairs of sensor through a four-permutation with replacement process. The generated mutual information surfaces are combined and then used to plan the motion of each sensor in the network. Simulation results involving TDOA measurements are presented to illustrate the effectiveness of the algorithm, where it is shown that three agents are able to accurately track a target that moves at the same speed as the agents.
Resource-Efficient and Power-Efficient FPGA Frequency Channelizer Using Novel Systolic Array Architectures

DoD communications systems often employ frequency hopping for resiliency against adversary interference. A key component of a frequency-hopping receiver design is the front-end digital channelizer, which can take significant resources on modern field-programmable gate arrays (FPGAs). In this paper, we present a highly resource-efficient and power-efficient FPGA-based frequency channelizer designed using a systolic architecture that replicates arrays of small leaf cells and requires only nearest-neighbor communications. These leaf cells are small, optimized, and can be replicated easily across the entire design. Additionally, initial analysis indicates that up to several times higher power efficiency with several times lower resource unitization may be achievable compared to the conventional design method using a systolic array design approach while also requiring significantly less design effort.
Data-Centric Approaches to Radio Frequency Machine Learning

The successes of machine learning (ML), and in particular deep learning, in other fields has inspired similar research within the radio frequency (RF) domain. Initial research in RF ML has been largely applied to the application of modulation recognition, with the past several years seeing it expand into other applications as well. The field has slowly evolved from the direct application of models developed in other fields, e.g., convolutional neural networks (CNN), to ones that are better suited for RF signals, e.g., dilated causal convolutions (DCCs). At the same time, the broader ML community has realized the importance data has on deep learning performance and a growing datacentric ML movement has emerged. In this paper, we return to the problem of modulation recognition and provide insights into how a data-centric approach can be coupled with a DCC model. In particular, we look at cases with limited amounts of training data and investigate means to achieve levels of performance typical reserved for larger training datasets. This is done by developing specific SNR models, data augmentation, performing multi-burst processing, and upsampling expected undersampled parts of an unbalanced training dataset. Overall, we present ways to intelligently use sparse available data to achieve the same performance as larger datasets, helping to mitigate a challenge in RF ML where gathering and curating large representative datasets is not always feasible.
Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning

This paper presents a game-theoretic framework to study the interactions of attack and defense for deep learning-based NextG signal classification. NextG systems such as the one envisioned for a massive number of IoT devices can employ deep neural networks (DNNs) for various tasks such as user equipment identification, physical layer authentication, and detection of incumbent users (such as in the Citizens Broadband Radio Service (CBRS) band). By training another DNN as the surrogate model, an adversary can launch an inference (exploratory) attack to learn the behavior of the victim model, predict successful operation modes (e.g., channel access), and jam them. A defense mechanism can increase the adversary's uncertainty by introducing controlled errors in the victim model's decisions (i.e., poisoning the adversary's training data). This defense is effective against an attack but reduces the performance when there is no attack. The interactions between the defender and the adversary are formulated as a non-cooperative game, where the defender selects the probability of defending or the defense level itself (i.e., the ratio of falsified decisions) and the adversary selects the probability of attacking. The defender's objective is to maximize its reward (e.g., throughput or transmission success ratio), whereas the adversary's objective is to minimize this reward and its attack cost. The Nash equilibrium strategies are determined as operation modes such that no player can unilaterally improve its utility given the other's strategy is fixed. A fictitious play is formulated for each player to play the game repeatedly in response to the empirical frequency of the opponent's actions. The performance in Nash equilibrium is compared to the fixed attack and defense cases, and the resilience of NextG signal classification against attacks is quantified.
Insights on Using Deep Learning to Spoof Inertial Measurement Units for Stealthy Attacks on UAVs

Unmanned Aerial Vehicles (UAVs) find increasing use in mission critical tasks both in civilian and military operations. Most UAVs rely on Inertial Measurement Units (IMUs) to calculate vehicle attitude and track vehicle position. Therefore, an incorrect IMU reading can cause a vehicle to destabilize, and possibly even crash. In this paper, we describe how a strategic adversary might be able to introduce spurious IMU values that can deviate a vehicle from its mission-specified path while at the same time evade customary anomaly detection mechanisms, thereby effectively perpetuating a “stealthy attack” on the system. We explore the feasibility of a Deep Neural Network (DNN) that uses a vehicle's state information to calculate the applicable IMU values to perpetrate such an attack. The eventual goal is to cause a vehicle to perturb enough from its mission parameters to compromise mission reliability, while, from the operator's perspective, the vehicle still appears to be operating normally.
GAN-Based Detection of Adversarial EM Signal Waveforms

Detection of unauthorised or malicious electromagnetic (EM) transmissions in the wireless spectrum is highly important in both military and commercial systems. In military wireless networks, and particularly in congested EM environments, the detection of unknown radar or communication waveforms can lead to timely identification of potentially adversarial transmissions or intruders in the area. On the other hand, in cognitive radio networks the identification of unauthorised communication waveforms can prevent and mitigate security threats, such as Primary User Emulation (PUE) attacks. However, data of such waveforms are usually of insignificant size to be effectively modelled or even there are no prior data available since they appear for the first time, which makes their timely detection particularly difficult. In this paper, we present a Generative Adversarial Network (GAN) based system which trains on available (presumably friendly) EM signals to detect any previously unseen types of EM waveforms, which can be potentially characterised as unauthorised or malicious. The proposed system is successfully trained and tested on a synthetic dataset comprising different pulsed radar and communication modulated signals impaired with Rician multipath fading, AWGN and random clock offset, resulting in center frequency offset and sampling time drift, and it was shown to successfully detect any previously unseen types of EM waveforms even in low SNR.
MalGrid: Visualization of Binary Features in Large Malware Corpora

The number of malware is constantly on the rise. Though most new malware are modifications of existing ones, their sheer number is quite overwhelming. In this paper, we present a novel system to visualize and map millions of malware to points in a 2-dimensional (2D) spatial grid. This enables visualizing relationships within large malware datasets that can be used to develop triage solutions to screen different malware rapidly and provide situational awareness. Our approach links two visualizations within an interactive display. Our first view is a spatial point-based visualization of similarity among the samples based on a reduced dimensional projection of binary feature representations of malware. Our second spatial grid-based view provides a better insight into similarities and differences between selected malware samples in terms of the binary-based visual representations they share. We also provide a case study where the effect of packing on the malware data is correlated with the complexity of the packing algorithm.
Securing Software Updates under Receiver Radio Frequency Geolocation Risk

In the new, ever-changing cyber domain, it is crucial that the military establishes a method of delivering large data payloads to remote locations that minimizes radio frequency (RF) signature for receivers, thereby reducing the associated geolocation potential. This paper introduces three cryptographic protocols for different components of a delivery architecture for a large data payload from a trusted, back-end source to receivers: a low-response protocol for initial transmission and confirmation and two possible inter-unit distribution protocols with differing optimizations based on connectivity scenarios. All three protocols expressly aim to minimize the radio frequency (RF) footprint created on the receiver end. We provide security models and analyze the security protocols, and furthermore provide a worst-case example bound on RF footprint created at the receivers for each protocol, with variable inputs for data transmission size. These protocols introduce a means for accounting for both security (authentication) and safety (minimized RF footprint) in the delivery of critical data payloads to remote receivers.
I Know Your Triggers: Defending Against Textual Backdoor Attacks with Benign Backdoor Augmentation

A backdoor attack seeks to introduce a backdoor into a machine learning model during training. A backdoored model performs normally on regular inputs but produces a target output chosen by the attacker when the input contains a specific trigger. Backdoor defenses in computer vision are well-studied. Previous approaches for addressing backdoor attacks include 1) cryptographically hashing the original, pristine training and validation datasets to provide evidence of tampering and 2) using machine learning algorithms to detect potentially modified examples. In contrast, textual backdoor defenses are understudied. While textual backdoor attacks have started evading defenses through invisible triggers, textual backdoor defenses have lagged. In this work, we propose Benign Backdoor Augmentation (BBA) to fill the gap between vision and textual backdoor defenses. We discover that existing invisible textual backdoor attacks rely on a small set of publicly documented textual patterns. This unique limitation enables training models with increased robustness to backdoor attacks by augmenting the training and validation datasets with backdoor samples and their true labels. In this way, the model can learn to discard the adversarial connection between the trigger and the target label. Extensive experiments show that the defense can effectively mitigate and identify invisible textual backdoor attacks where existing defenses fall short.
Positive and Unlabeled Learning for Mobile Application Traffic Classification

Encrypted messaging applications are a type of social media that provide privacy for the contents of messages, but are susceptible to side-channel attacks that take advantage of information leaked through network traces. Mobile network traffic classification using machine learning (ML) is an effective technique to take advantage of this leaked information to reveal the presence of a specific application on a network but requires fully-labeled datasets that are difficult to obtain in the real-world. We show the ability of positive and unlabeled (PU) learning to increase situational awareness of a given network by revealing the presence of mobile applications (e.g., social media applications used for encrypted messaging, such as Signal), using a large unlabeled dataset coupled with a small amount of labeled data for a given application. We attain an average f1-score of 0.8708 when classifying an encrypted messaging application, Signal, and an average f1-score of 0.6497 across every application in our dataset. Our results demonstrate that a dataset of network traffic containing a single target mobile application of interest can be used to reveal the presence of that application in a larger, unlabeled dataset. In other words, this technique makes it possible to expand the situational awareness of which encrypted social messaging applications are in use on defense networks with only a small amount of labeled data.
Statistical Analysis of the MK-3 Customizable Authenticated Encryption

To provide security autonomy capability, such that different users can have independent variants of the encryption algorithm, MK-3 is designed to be customizable. Two levels of customization are supported, Factory Customization and Field Customization. Customization is done by modifying functions and function parameters in the algorithm to yield differing cipher functions while preserving the algorithm's security. The main goal of this work is to present the results from the statistical analysis of the customizable MK-3 encryption scheme, focusing on field customized mixers. We recall the main components of the MK-3 algorithm and overview a subset of available factory and field customizations for MK-3. We test the main instances of the field customized versions and give a general argument for their desired statistical properties expected from an encryption scheme.
A Novel Resilient HF Data Communications Architecture

This paper introduces a novel resilient High Frequency (HF) data communications architecture, optimized for operation in highly cluttered RF environments. The proposed architecture is based on networking a set of geographically dispersed HF transceivers in order to derive a net system gain through diversity reception and transmission. Uniquely, the transceivers in this network make use of STANAG 4538 packet data waveforms. The paper describes the architecture, advantages and benefits, and presents the results from measurement campaigns with the Royal Thai Navy, where the proposed approach is compared to conventional point-to-point STANAG 5066 with MIL-STD-188-110C/D streaming data approaches. Results are given which show that the proposed architecture outperforms conventional implementations, by a considerable margin, in highly cluttered RF environments.