Reviewerly/exHarmony huggingface dataset:
Communication-Efficient ∆-Stepping for Distributed Computing Systems

This paper considers the single source shortest path (SSSP) problem, which is the key for many applications such as navigation, mapping, routing, and social networking. Existing SSSP algorithms are designed mostly for shared-memory systems. Nevertheless, with the prevalence of diverse smart devices like drones, there is a growing interest in deploying SSSP algorithms over distributed computing systems so that they can run efficiently onboard of smart devices via Mobile Ad Hoc Computing or at the network edges via Mobile Edge Computing. In this paper, we introduce a communication-efficient ∆-stepping algorithm for distributed computing systems. The proposed algorithm is featured by 1) a message coordination architecture for reducing message exchanges between workers, 2) a pruning technique for reducing redundant computations, and 3) an aggregation technique for further reducing message exchanges when communication delay is significant. Theoretical analyses and experimental studies on real-world graph datasets demonstrate the promising performance of proposed algorithm.
Fully Homomorphic Encryption: Precision Loss in Wireless Mobile Communication

Fully Homomorphic Encryption (FHE) is a cryp-tographic technique that enables secure computation over en-crypted data. It has been considered as a promising solution to provide secure and privacy-preserving Fifth Generation (5G) wireless network traffic prediction. However, one of the main challenges of using FHE is the precision loss occurring during the homomorphic computations which can have an impact on network planning and optimization, Quality of Service (QoS) management, and security monitoring. Therefore, this paper discusses the effect of precision loss in 5G wireless network traffic prediction. The result of the underlying study provides experimental upper and lower bounds of the precision loss as well as the selection of an appropriate precision parameter to balance the trade-off between performance and computational cost. All practical FHE schemes are based on a mathematical problem that appears to be resistant to quantum computers meaning that the work in this paper will be valid for future wireless generations even in the quantum era.
Structural learning of mixed noisy-OR Bayesian networks

In this paper we discuss learning Bayesian networks whose conditional probability tables are either Noisy-OR models or general conditional probability tables. We refer to these models as Mixed Noisy-OR Bayesian Networks. To learn their structure, we modify the Bayesian Information Criterion used for standard Bayesian networks to reflect the number of parameters of a Noisy-OR model. We prove that the log-likelihood function of a Noisy-OR model has a unique maximum and adapt the EM-learning method for the leaky Noisy-OR model. We propose a structure learning algorithm that learns optimal Mixed Noisy-OR Bayesian Networks. We evaluate the proposed approach on synthetic data, where it performs substantially better than standard Bayesian networks. We perform experiments with Bipartite Noisy-OR Bayesian networks of different complexity to find out when the results of Mixed Noisy-OR Bayesian Networks are significantly better than the results of standard Bayesian networks and when they perform similarly. We also study how different penalties based on the number of model parameters affect the quality of the results. Finally, we apply the suggested approach to a problem from the domain of linguistics. Specifically, we use Mixed Noisy-OR Bayesian Networks to model the spread of loanwords in the South-East Asian Archipelago. We perform numerical experiments in which we compare the prediction ability of standard Bayesian networks with Mixed Noisy-OR Bayesian networks and test different pruning methods to reduce the number of parent sets considered.
Practical Trustworthiness Model for DNN in Dedicated 6G Application

Artificial intelligence (AI) is considered an efficient response to several challenges facing 6G technology. However, AI still suffers from a huge trust issue due to its ambiguous way of making predictions. Therefore, there is a need for a method to evaluate the AI’s trustworthiness in practice for future 6G applications. This paper presents a practical model to analyze the trustworthiness of AI in a dedicated 6G application. In particular, we present two customized deep neural networks (DNNs) to solve the automatic modulation recognition (AMR) problem in Terahertz communications-based 6G technology. Then, a specific trustworthiness model and its attributes, namely data robustness, parameter sensitivity, and security covering adversarial examples, are introduced. The evaluation results indicate that the proposed trustworthiness attributes are crucial to evaluate the trustworthiness of DNN for this 6G application.
5G Mobile Network Signal Strength Interpolation Using Machine Learning GAN Algorithm

This article presents a new interpolation algorithm for received signal strength in mobile networks. The algorithm aims to fill in the missing values in a network environment by creating a full signal strength map. It is based on the deep learning Generative Adversary Network (GAN), which has previously been used successfully only for fixing pixels in image repairs. The algorithm was trained and tested using publicly available data on outdoor mobile network signal strength under 6GHz, and the results were evaluated using two performance metrics: accuracy of measurement and size of predicted values. The results show that the accuracy of the algorithm depends heavily on the characteristics of the input data. When the interpolating points are evenly distributed, the accuracy is high and the number of predicted values is satisfactory. Our algorithm reduces the number of manual measurements required to obtain a complete signal strength map, simplifying network planning and quality assurance processes.
Learning to Detect Wireless Spectrum Occupancy Using Clustering Approaches

Driven by various academic, standardization and regulatory initiatives, recent research on spectrum resource utilisation has focused also on technology and transmission classification using various deep learning (DL) architectures. However, especially in unlicensed bands it is often hard to obtain labelled data of sufficient quality for training DL for all transmissions that may occur. Therefore in this paper we investigate clustering techniques that require no labelled data or prior knowledge on continuous spectrum sensing sweeps over a 200 kHz wide band in the unlicensed European 868 MHz frequency band. Using several clusterability tests we show that the sweeps can be clustered, however the number of clusters is not clear. By analyzing the sweeps with three state-of-the-art techniques, K-Means, Agglomerative Hierarchical Clustering, and Hierarchical Density-Based Spatial Clustering (HDBSCAN) 9-10 clusters are discovered. The quantitative evaluation shows that HDBSCAN outperforms the other two and the qualitative analysis shows that HDBSCAN seems to be able to better discriminate between the same technology transmitting at/from different power levels/distance, however it has a relatively poorer class balance compared to K-Means and tends to group more transmissions than needed in the majority no transmission cluster.
Semantically Optimized End-to-End Learning for Positional Telemetry in Vehicular Scenarios

End-to-end learning for wireless communications has recently attracted much interest in the community, owing to the emergence of deep learning-based architectures for the physical layer. Neural network-based autoencoders have been proposed as potential replacements of traditional model-based transmitter and receiver structures. Such a replacement primarily provides an unprecedented level of flexibility, allowing to tune such emerging physical layer network stacks in many different directions. The semantic relevance of the transmitted messages is one of those directions. In this paper, we leverage a specific semantic relationship between the occurrence of a message (the source), and the channel statistics. Such a scenario could be illustrated for instance, in vehicular communications where the distance is to be conveyed between a leader and a follower. We study two autoencoder approaches where these special circumstances are exploited. We then evaluate our autoencoders, showing through the simulations that the semantic optimization can achieve significant improvements in the BLERs (up till 93.6%) and RMSEs (up till 87.3%) for vehicular communications leading to considerably reduced risks and needs for message retransmissions.
MLOps meets Edge Computing: an Edge Platform with Embedded Intelligence towards 6G Systems

The evolution towards more human-centered 6G networks requires the extension of network functionalities with advanced, pervasive automation features. In this direction, cloud-native, softwarized network functions and integration of extreme/far edge devices shall be supported by even more distributed and decomposable systems, such as Edge Cloud environments, while building on AI/ML data-driven mechanisms to improve their performance and resilience for the stringent requirements of next-generation applications. In this work, we propose an intelligence-native Edge Management Platform coupled with MLOps functionalities-the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pi$</tex> -Edge Platform-which encompasses automation features for cloud-native lifecycle management of Edge Services. Our introduced architecture incorporates MLOps services and processes, operating as integrated micro-services with the rest of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pi$</tex> -Edge architectural components, ensuring the reliable operation and QoS of Edge network and application services. We experimentally validate our approach with a prototypical implementation of key <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pi$</tex> -Edge features, including the incorporation of state-of-the-art ML models for performance prediction and anomaly detection, on a multi-media streaming use case based on scenarios from real production environment.
DDoS attack detection using unsupervised federated learning for 5G networks and beyond

The rapid expansion of 5G networks, coupled with the emergence of 6G technology, has highlighted the critical need for robust security measures to protect communication infrastructures. A primary security concern in 5G core networks is Distributed Denial of Service (DDoS) attacks, which target the GTP protocol. Conventional methods for detecting these attacks exhibit weaknesses and may struggle to effectively identify novel and undiscovered attacks. In this paper, we proposed a federated learning-based approach to detect DDoS attacks on the GTP protocol within a 5G core network. The suggested model leverages the collective intelligence of multiple devices to efficiently and privately identify DDoS attacks. Additionally, we have developed a 5G testbed architecture that simulates a sophisticated public network, making it ideal for evaluating AI-based security applications and testing the implementation and deployment of the proposed model. The results of our experiments demonstrate that the proposed unsupervised federated learning model effectively detects DDoS attacks on the 5G network while preserving the privacy of individual network data. This underscores the potential of federated learning in enhancing the security of 5G networks and beyond.
RF Fingerprint-based Identity Verification in the Presence of an SEI Mimicking Adversary

Specific Emitter Identification (SEI) is advantageous for its ability to passively identify emitters by exploiting distinct, unique, and organic features unintentionally imparted upon every signal during formation and transmission. These features are attributed to the slight variations and imperfections that exist in the Radio Frequency (RF) front end, thus SEI is being proposed as a physical layer security technique. The majority of SEI work assumes the targeted emitter is a passive source with immutable and difficult-to-mimic signal features. However, Software-Defined Radio (SDR) proliferation and Deep Learning (DL) advancements require a reassessment of these assumptions, because DL can learn SEI features directly from an emitter’s signals and SDR enables signal manipulation. This paper investigates a strong adversary that uses SDR and DL to mimic an authorized emitter’s signal features to circumvent SEI-based identity verification. The investigation considers three SEI mimicry approaches, two different SDR platforms, the presence or lack of signal energy as well as a "decoy" emitter. The results show that "off-the-shelf" DL achieves effective SEI mimicry. Additionally, SDR constraints impact SEI mimicry effectiveness and suggest an adversary’s minimum requirements. Future SEI research must consider adversaries capable of mimicking another emitter’s SEI features or manipulating their own.
Turbo AI, Part V: Verifying AI-Enhanced Channel Estimation for RAN from System Level

Turbo-AI is an iterative Machine-Learning (ML) based channel estimator as an additional option to balance the performance-complexity trade-off and realize challenging scenarios towards future 6th Generation (6G) wireless communications in a complementary manner. In this paper, we move the focus from algorithmic aspects and pure level link investigations, addressed in previous Turbo-AI paper series, to system level exploitation and hardware-related implementation. We integrate Turbo-AI to a 5G compliant system level simulator, through which the performance gap between 5G legacy channel estimator and ML-based channel estimator can be described and quantized to the distributions of channel estimation Mean Squared Error (MSE). Finally, Turbo-AI, representing an important component of future AI/ML-enhanced Physical Layer (PHY), is realized in a hardware platform implementing the New Radio (NR) compliant gNB-functionality, which satisfies the L1 processing latency of the whole Physical Uplink Shared Channel (PUSCH) chain. The performance advantage of Turbo-AI over the 5G legacy channel estimator is not only reflected in the improved coded Block Error Rate (BLER) curves, but also exhibited by the real-time Radio Frequency (RF) experimentation towards an early adoption of AI/ML-enhanced RAN, seamless model management and performance-latency trade-off, depending on computational resources and system constraints.
Post-Quantum QUIC Protocol in Cloud Networking

Post-quantum ciphers (PQC) secure the digital networking of the current computers against a quantum-computing-equipped adversary. The National Institute of Standards and Technology (NIST) selected post-quantum digital signature algorithms for standardization. To prepare for the transition to PQC, we study the feasibility of integrating the NIST-standardized PQC digital signatures into the existing networking protocols, which include TCP/TLS and the more advanced QUIC. We study the behavior and performances of implementing PQC signatures for HTTP networking built on TCP/TLS and QUIC. Our experiment-based studies use remote cloud servers across the globe to simulate and measure the real-world networking behaviors. Focusing on the post-quantum lattice-based ciphers of Dilithium and Falcon, our results show that QUIC generally outperforms TCP/TLS (by 52% with RSA, 2.5% or greater with Dilithium algorithms, and 32.8 % or greater with Falcon algorithms). Based on the QUIC performances and the protocol handshake duration overhead between the client and the cloud server, we recommend Falcon for the QUIC-based networking applications for quicker handshake and less variance if the devices can afford the hardware for floating-point-based operations.
Comparing 5G Network Latency Utilizing Native Security Algorithms

While Fifth Generation (5G) wireless technology promises higher data throughput rates and reduced latency in comparison to previous generations of cellular communications, the addition of encryption between communicating nodes presents potential overhead costs to the system in terms of increased processing time, thereby leading to an increase in network latency. Given that modern Internet of Things (IoT) and Industry 4.0 5G use cases both require exceptional network speed coupled with security, the choice of security algorithms is a critical element. This paper offers a live 5G testbed setup and methodology to analyze measured round trip times between a user device and a 5G network-in-a-box using the four native 128-bit ciphering algorithms as specified by current Third Generation Partnership Project (3GPP) telecommunications standards. We perform these tests using the Amarisoft AMARI Callbox Mini, a 3GPP-compliant 5G network. Our initial findings show there is no statistically significant relationship between enabled ciphering algorithms and latency given one user device and the 5G network core within the Amarisoft 5G ecosystem, though our setup and methodology allow for expanding this research with multiple devices and longer experiment durations to understand how to best balance network latency and security requirements in a complex wireless network.
Distributed Cryptography for Lightweight Encryption in Decentralized CP-ABE

Decentralized Attribute-based Encryption (DABE) is an extension of public key cryptography that allows the ciphertext to be decrypted by any node that has a predefined set of attributes. DABE can be used to control access to Internet of Things (IoT) devices and data based on attributes such as the type of device, the location, and the role of the user. Due to its heavy computation requirements, the DABE either can not be implemented on the weak devices or will be implemented with significant delay.In this paper, we study two distributed solutions of lightweight encryption in DABE using secret sharing and outsourcing. The analysis of our results showed that both approaches provided a lightweight encryption property of DABE. The secret sharing outperformed the outsourcing in small number of attributes, and both provided close efficient results in case of high number of attributes.
Emoji shellcoding in RISC-V

Shellcodes are short, executable code fragments that are utilized in various attack scenarios where code execution is possible. When they are injected through the program's inputs, they may require to be validated by filters, the most common of which is a restriction on the allowed character set. This paper explains how to design RISC-V shellcodes capable of running arbitrary code whose UTF-8 representation uses only Unicode emojis. Our approach to this problem is inspired by code-reuse attacks and involves the use of small, reusable code snippets called gadgets. By chaining these gadgets together, we are able to build a shellcode that can bypass the constraints imposed by filters, making it more versatile and effective in a wider range of attack scenarios.
Low-Complexity Neural Networks for Denoising Imperfect CSI in Physical Layer Security

Channel adaptation physical layer security (PLS) schemes are degraded when the channel state information (CSI) is imperfect. Imperfect CSI is due to factors such as noisy feedback, outdated CSI, etc. In this paper, we propose a low-complexity noisy CSI denoising scheme based on the autoencoder architecture of deep neural networks referred to as DenoiseSec-Net. To further reduce complexity, we then propose a hybrid version (HybDenoiseSecNet) that combines a legacy denoising scheme and a shallow neural network to achieve a similar performance as DenoiseSecNet. Simulation results, in terms of bit error rate (BER), secrecy capacity, and normalized mean squared error (NMSE), show the performance improvement of our proposed scheme compared to conventional denoising schemes. Finally, we study the significant reduction in computational complexity of the proposed scheme compared to another neural network scheme.
CustomProcessingUnit: Reverse Engineering and Customization of Intel Microcode

Microcode provides an abstraction layer over the instruction set to decompose complex instructions into simpler micro-operations that can be more easily implemented in hardware. It is an essential optimization to simplify the design of x86 processors. However, introducing an additional layer of software beneath the instruction set poses security and reliability concerns. The microcode details are confidential to the manufacturers, preventing independent auditing or customization of the microcode. Moreover, microcode patches are signed and encrypted to prevent unauthorized patching and reverse engineering. However, recent research has recovered decrypted microcode and reverse-engineered read/write debug mechanisms on Intel Goldmont (Atom), making analysis and customization of microcode possible on a modern Intel microarchitecture. In this work, we present the first framework for static and dynamic analysis of Intel microcode. Building upon prior research, we reverse-engineer Goldmont microcode semantics and reconstruct the patching primitives for microcode customization. For static analysis, we implement a Ghidra processor module for decompilation and analysis of decrypted microcode. For dynamic analysis, we create a UEFI application that can trace and patch microcode to provide complete microcode control on Goldmont systems. Leveraging our framework, we reverse-engineer the confidential Intel microcode update algorithm and perform the first security analysis of its design and implementation. In three further case studies, we illustrate the potential security and performance benefits of microcode customization. We provide the first x86 Pointer Authentication Code (PAC) microcode implementation and its security evaluation, design and implement fast software breakpoints that are more than 1000x faster than standard breakpoints, and present constant-time microcode division, illustrating the potential security and performance benefits of microcode customization.
An Intelligent Tool for Classifying Issue Reports

A considerable amount of issue reports are submitted daily in large-scale software development. Manually reviewing and classifying each issue report is challenging and error-prone. Thus, to assist practitioners, in this paper, we propose and evaluate an automatic supervised machine learning-based approach that can automatically predict the newly submitted issue report type (i.e., bug, feature, question, or documentation). We applied the supervised machine learning-based approach to over 1.4 million issue reports data from real open-source projects. We performed our experiments using Stochastic Gradient Descent (SGD)-based classifier and achieved an F1 micro average score of 0.8523.
Evaluating Password Composition Policy and Password Meters of Popular Websites

Password-based authentication is one of the most commonly adopted mechanisms for online security. Choosing strong passwords is crucial for protecting ones' digital identities and assets, as weak passwords can be readily guessable, resulting in a compromise such as unauthorized access. To promote the use of strong passwords on the Web, the National Institute of Standards and Technology (NIST) provides website administrators with password composition policy (PCP) guidelines. We manually inspect popular websites to check if their password policies conform to NIST's PCP guidelines by generating passwords that meet each criterion and testing the 100 popular websites. Our findings reveal that a considerable number of web sites (on average, 53.5 %) do not comply with the guidelines, which could result in password breaches.