Reviewerly/exHarmony huggingface dataset:
Detecting Cryptomining Traffic Over an Encrypted Proxy Based on K-S Test

In recent years, the good revenue generated by cryptocurrency mining has attracted a lot of people to participate in it. It has also caught the attention of hackers, and cryptojacking attacks are becoming more common. Detecting cryptomining behavior can effectively reduce the lost caused by cryptojacking attacks. Existing host-based cryptomining detection methods can protect only end devices and violate users' privacy. Besides, network-based solutions can not better handle anti-reconnaissance means of encrypted proxy. To bridge this gap, we propose a cryptomining traffic detection model based on K-S Test(CMD-KST). Our traffic analysis study confirms that the feature distributions of cryptomining traffic over an encrypted proxy are still stable and unique. CMD-KST compares the feature distributions of a network flow segment with that of cryptomining traffic over the encrypted proxy to complete the detection task. CMD-KST is easily deployable and can detect cryptomining traffic at the entrance of the managed network. Our experimental results demonstrate that CMD-KST achieves a recall of 98.84% without generating false positives and takes only 6 minutes of analyzing mining traffic to complete the detection. CMD-KST is faster than other network-based cryptomining traffic detection methods and achieves a higher precision. Furthermore, the adversarial evaluation shows that it is challenging for the attackers to counteract our detection.
Distributed Machine Learning for Predicting Customer Behavior While Preserving Privacy

Analyzing customer behavior has great value, that's why researchers until this day are trying to more understand customer behavior, because this enables them to realize what's behind purchase intention, how and what customers want to buy, and what are the factors that affect customer behavior. Big data, artificial intelligence, and machine learning are the three most critical pillars on which the concept of business analytics is based, which leads to ideal customer behavior analyzes and through which customer behavior can be predicted. The benefits of predicting customer behavior are many, such as increasing sales and profits, maintaining existing customers and attracting more. Therefore, the purpose of this study is to analyze the existing methods for predicting customer behavior in addition to identifying the types of federated learning and the suitable type for each scenario. This study provides a comprehensive description of customer behavior prediction techniques. The authors found that there are many models used to predict customer behavior and that there are four main factors affecting customer behavior. The most prominent approaches to maintain data privacy are differential privacy and homomorphic encryption. Moreover, there are three main types of federated learning which are horizontal, vertical and transfer federated learning. As a future study, one of the federated learning approaches will be used to predict customer behavior.
FedSCS: Client Selection for Federated Learning Under System Heterogeneity and Client Fairness with a Stackelberg Game Approach

Federated learning (FL) embraces the concepts of targeted data gathering and training, and it can reduce many of the systemic privacy costs and hazards associated with traditional machine learning frameworks. However, on the one hand, the wireless communication propagation circumstances and client computation capabilities are not taken into account in traditional synchronous FL algorithms, which causes the time delay in the FL process because the time of each round is determined by the client with the worst performance. On the other hand, the unfairness of client participation in FL may lead to damage to the global accuracy because of the non identically independently distributed (Non-IID) data. In this paper, we propose a Stackelberg game-based FL Client Selection framework, named FedSCS, which considers system heterogeneity, client heterogeneity, and the fairness of client participation to reduce the latency resulting from the heterogeneous communication conditions and computation capabilities among clients with Non-IID data distributions. Specifically, the Stackelberg leader-follower game is first formulated in which the server decides the price of the single quota of participating in the FL process every communication round and the clients decide whether to participate in FL. Then the equilibrium solution of the game is derived and proved. The simulation results verify the effectiveness of the proposed strategy for reducing the time latency of FL processes with heterogeneous clients, i.e., FedAvg, FedOpt, and FedNova.
$\mathcal{L}{-}$ ETC: A Lightweight Model Based on Key Bytes Selection for Encrypted Traffic Classification

To protect the confidentiality of communication data, internet users often use encryption protocols (e.g., TLS/SSL) or tools (e.g., VPN, Tor) for network access. Therefore, as a pivotal network management method, encrypted traffic classification technology is vital for guaranteeing the quality of service, the quality of experience, and network security. Researchers have already developed some end-to-end deep learning-based methods to realize encrypted traffic classification. However, given the constrained computational resources available in real-world network measurement scenarios, the existing approaches with high complexity and computation overhead are not appropriate. In this paper, we propose a lightweight model to tackle this issue. Firstly, we propose a base model based on the self-attention mechanism to obtain the key bytes in packets contributing to the classification. Secondly, we leverage these key bytes to reconstruct the input and then streamline the base model, carrying out a lightweight model, i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{L}{-}$</tex> ETC. Finally, we implement experiments on three benchmark datasets. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{L}-\mathbf{ETC}^{\prime}\mathrm{s}$</tex> macro Fl score of the three tasks exceeds 0.92 with only 0.076M (Million) parameters, and the throughput reaches 917 pps, which is also superior to state-of-the-art methods.
DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning

Federated Learning (FL) is a distributed machine learning (ML) paradigm, aiming to train a global model by exploiting the decentralized data across millions of edge devices. Compared with centralized learning, FL preserves the clients’ privacy by refraining from explicitly downloading their data. However, given the geo-distributed edge devices (e.g., mobile, car, train, or subway) with highly dynamic networks in the wild, aggregating all the model updates from those participating devices will result in inevitable long-tail delays in FL. This will significantly degrade the efficiency of the training process. To resolve the high system heterogeneity in time-sensitive FL scenarios, we propose a novel FL framework, DynamicFL, by considering the communication dynamics and data quality across massive edge devices with a specially designed client manipulation strategy. DynamicFL actively selects clients for model updating based on the network prediction from its dynamic network conditions and the quality of its training data. Additionally, our long-term greedy strategy in client selection tackles the problem of system performance degradation caused by short-term scheduling in a dynamic network. Lastly, to balance the trade-off between client performance evaluation and client manipulation granularity, we dynamically adjust the length of the observation window in the training process to optimize the long-term system efficiency. Compared with the state-of-the-art client selection scheme in FL, DynamicFL can achieve a better model accuracy while consuming only 18.9% – 84.0% of the wallclock time. Our component-wise and sensitivity studies further demonstrate the robustness of DynamicFL under various real-life scenarios.
Smart-PKI: A Blockchain-based Distributed Identity Validation Scheme for IoT Devices

Internet of Things (IoT) devices have achieved rapid development but most of them are vulnerable to spoofing attacks and spoofing-related attacks. It is crucial to verify source identity at the near-source end to defend against attacks, save network forwarding resources, and relieve the authentication pressure on the receiver end. In this paper, we propose Smart-PKI, a blockchain-based distributed identity validation scheme for IoT Devices. In the architecture of Smart-PKI, near-source forwarders can verify the authenticity of the source identity of packets and can filter spoofed packets. Besides, we apply Merkle Patricia Trie (MPT) to the Smart-PKI blockchain to enable lightweight blockchain copy storage and efficient retrieval and verification of identity information on forwarders. Meanwhile, Smart-PKI proposes an identity restoration mechanism and enables solutions for the attacks caused by public and private key compromise. Furthermore, we implement Smart-PKI on Network Simulator Version 3 (NS3) and evaluate its performance against reflection denial-of-service (DDoS) attacks. The simulation results demonstrate the effectiveness and efficiency of Smart-PKI and it outperforms existing blockchain-based PKI solutions for IoT devices in terms of network latency for verifying certificates.
Immersive Venice - A Thousand Echoes

The soundscapes of Venice are as fascinating as its architectural wonders, rich history, impressive art and vibrant contemporary life. The timeless essence of the city, carved in marble over the millennia, appears firm and fluid, echoing the ebb of the tides. Venice is a city engaged in an incessant dialogue with its past, bridging the gap between the ephemeral and the lasting. Following the technique of auralization, we embark on a journey to explore the echoes of Venice, capturing its acoustic footprints and the existing and imaginary immersive sound. We venture into the quiet nights of the city, recording impulse responses from unusual viewpoints capturing different moments in time and space. Our collection includes field recordings that include the rhythmic cadence of thousands of steps, the daily buzz of voices on the streets, and the gentle caress of water against boats, docks, and seabeds, all contributing to the unique acoustics of the city. Through our artistic practice, we challenge the notion of objectivity in scientific research. We intersect our subjective sound practices with a scientific approach using technological means that respond to a specific space definition. Living a notion of space as physical and sensory and a notion of time as layered and non-linear, we agree on all these elements to present an exploration enriched by the auditory landscape of Venice. This material that reveals the deep connection between this extraordinary city's sound and essence will nourish three compositions and an acoustic mapping. This article presents meta-perspectives that consider paradigms of objectivity/ subjectivity and the importance of recognizing multiple perspectives when working with acoustic and sonic footprints of places.
A BIC-Based Mixture Model Defense Against Data Poisoning Attacks on Classifiers

Data Poisoning (DP) is an effective attack which degrades a trained classifier’s accuracy through covert injection of attack samples into the training set. We propose an unsupervised Bayesian Information Criterion (BIC)-based mixture model defense against DP attacks that: 1) addresses the most challenging embedded DP scenario wherein, if DP is present, the poisoned samples are an a priori unknown subset of the training set, and with no clean validation set available; 2) applies a mixture model to both well-fit potentially multi-modal class distributions and capture poisoned samples within a small subset of the mixture components; 3) jointly identifies poisoned components and samples by minimizing the BIC cost defined over the whole training set. Our experimental results demonstrate the effectiveness of our defense under strong DP attacks, as well as its superiority over other works.
“Kong” Element: A Brand-New Mathematical Definition of Communication

For the first time, this paper proposes a new mathematical definition of communication: “Kong” element, and defines its operation and properties. It also proposes the XOR operation theorem, and proved the theorem. Based on the XOR operation of “Kong” elements, the existence theorem and absorption rate theorem of “Kong” elements in encrypted communication are proposed for the first time, and one typical case is proved in detail. Besides, this paper brought up the idea of “Kong” element encryption which is transmitted by defined operation, and enumerated the encrypted communication case of “Kong” element transmitting information. This example not only diffuses and confuses information, but also breaks the strong correlation of traditional ciphertext by adding random redundant bits to information. This communication mode based on “Kong” elements is expected to realize secure communication with a new idea.
PLPR: Towards Efficient and Privacy-Preserving LBSNs-Based POI Recommendation in Cloud

With the popularity of location-based social networks (LBSNs), locations and social relationships have been considered to be important factors in point-of-interest (POI) recommendation services. The boom of cloud computing has driven data owners to outsource the LBSN data and the POI recommendation services to the cloud with powerful computing and storage capabilities. However, as the data usually contains sensitive information, it should be encrypted before being out-sourced, and consequently, the POI recommendation has to be processed over encrypted data. Although several privacy-preserving LBSNs-based POI recommendation schemes have been proposed, they are either inapplicable to the outsourcing scenario or have issues with the recommendation accuracy. Aiming at addressing these issues, in this paper, we propose an efficient and privacy-preserving LBSNs-based POI recommendation scheme (PLPR). Specifically, we first index the users' social relationships with Bloom filters and then organize the user resident location and social relationship dataset into a Vantage Point (VP) tree. Then, we design an efficient LSBNs-based POI recommendation algorithm based on the VP tree. After that, we design a privacy-preserving range determination protocol (PRD) and a privacy-preserving neighbor determination protocol (PND) to respectively protect the privacy of locations and social relationships in the designed algorithm and propose our PLPR scheme. Security analysis shows that our scheme is privacy-preserving, and performance evaluation demonstrates that our scheme is also efficient.
Self-Adaptive Healthcare Platforms Development: A Dynamic Product Line Approach Driven by Ontologies

Software reuse has been a fundamental and recurring problem in software engineering since its inception. Its main objective is to reduce software development and maintenance costs by facilitating the reuse of existing software components. Currently, the approach of software product lines and software architecture enables the simultaneous achievement of these objectives. The objectives include the development of a Healthcare dynamic product line and the identification of reusable components. This dynamic product line will later allow the generation of various applications based on user requirements. Additionally, we aim to construct a domain ontology that represents the feature model of e-health, determining the common functionalities and variabilities of software in this domain. The architecture is modeled following component-based principles to enhance reuse. In summary, our research project aims to address the challenges of software reuse by developing an e-health product line, constructing a domain ontology, and adopting a component oriented architecture.
On The Failure of Invariant Risk Minimization and an Effective fix via Classification Error Control

Invariant Risk Minimization is a well-known Domain Generalization framework that has received much attention over the past few years. Invariant Risk Minimization is capable of learning domain-invariant features from multiple domains by finding representation features such that the optimal classifier on top of these features matches all training domains. In this paper, we show that even though the Invariant Risk Minimization algorithm is based on a compelling idea, it is easily vulnerable in a simple toy example where multiple domain-invariant features exist and each possesses a corresponding classifier that is optimal for all domains. Based on this observation, we propose an effective modification of the traditional Invariant Risk Minimization algorithm named Error-Control Invariant Risk Minimization, which allows learning different domain-invariant features via controlling the training classification error, leading to a new algorithm that works well on both our toy synthetic dataset and the real-world datasets.
Quantum Approximate Optimization Algorithm for Knapsack Resource Allocation Problems in Communication Systems

Quantum technologies have recently scaled up from laboratories into commercial applications thanks to the rapid technical developments and the growing investments in quantum computing. These developments open up the way for the emergence of the so-called noisy intermediate-scale quantum (NISQ) devices, where the quantum approximation optimization algorithms (QAOAs) represent a class of algorithms tailored for the NISQera computing for provisioning tangible quantum advantages. Meanwhile, wireless communications networks have become more complex over time and the pressure to conquer communications complexity is intense for both researchers and system designers. Specifically, a major optimization problem in this context is the resource allocation in modern communications where typically appears as an intricate 0/1 knapsack (0/1-KP) problem and finding its optimal solution using classical computers is prohibitively difficult. Thus, a parallel QAOA framework for optimizing the 0/1- KP problems is proposed in this paper. The proposal has the space complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O} (n)$</tex> and pseudopolynomial time complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O} (nW)$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W$</tex> is the knapsack's total capacity and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> is the total number of items. However, the proposed QAOA solution is highly parallel and can be implemented on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> NISQ devices of n-qubits each to obtain <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(n W / M)$</tex> time complexity and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O} (nM)$</tex> space complexity. Numerical experiments show high approximation ratios even for shallow depth QAOA instances.
RoLL: Real-Time and Accurate Route Leak Location with AS Triplet Features

BGP is the only inter-domain routing protocol that plays an important role on the Internet. However, BGP suffers from route leak, which can cause serious security threats. To mitigate the effects of route leak, accurate and timely route leak location is of great importance. Prior studies leverage AS business relationships to locate route leak in real time. However, they fail to achieve high location accuracy. Recent studies apply machine learning to accurately detect route leak from statistical features of massive BGP messages. Nevertheless, they have high detection latency and cannot further locate route leak. In this paper, we propose a real-time and accurate route leak location system named RoLL. It leverages distinctive AS triplet features to accurately locate AS triplets with route leak from each BGP update message in real time. Our experimental results on real-world BGP route leak data demonstrate that RoLL can achieve 91% location accuracy with less than 10 ms location latency.
Space-Centric Adaptive Video Streaming with Quality of Experience Optimization in Low Earth Orbit Satellite Networks

This study aims to enable space-centric adaptive video streaming through low earth orbit (LEO) satellites. Nowadays, satellite TV is sent in a broadcasting manner via geostationary orbit (GSO) satellites. However, the long distance between GSO and the Earth results in long latency, making GSO satellites less likely to provide livecast streaming services. To solve this problem, LEO satellites, which are much closer to the Earth, can relay traffic from video servers to remote users in high capacity and low latency. However, the orbiting characteristic of LEO satellites introduces new challenges such as frequent handover and fluctuating capacity that do not occur in GSO satellites. In this paper, we address the video streaming problem in LEO satellite networks, concerning adaptive bitrate, relay satellite selection and super-resolution. A novel solution called SkyTube, which is a Policy Proximal Optimization (PPO) based Deep Reinforcement Learning (DRL) method, is proposed to maximize the Quality of Experience (QoE) of the streaming user. The goal for our design is to formulate video streaming in LEO satellite networks into a resource allocation problem, thus the solution is not restricted to any special type of LEO satellite constellations. Simulation results show that SkyTube outperforms other baselines in terms of the cumulative QoE per orbit period. We also show the great performance and high convergence speed of SkyTube in all cases.
Fast and Parallel Modular Multiplication without Borrow for ECC on ARM-NEON

Fast reduction is a powerful modular reduction method for many ECC curves using NIST-style prime modulus. Based on fast reduction, existing vectorized modular multiplication schemes (such as MR and SMCOS) were constructed in succession. However, these schemes are only applicable to those NIST-style primes with just subtraction items. The vector design on fast reduction for ones with addition items (including certain well-known curves, NIST P-256 and SM2, etc.) rarely holds in practice because of the possible borrow situation. In this work, we fill this gap and propose a pipelined and vectorized fast reduction that, with the aid of pre-processing and post-processing, eliminates all potential borrow during the reduction. Based on the proposed reduction, we successfully construct a vectorized modular multiplication, namely Borrowless, which works with all NIST-style primes, including the ones with addition items. Using NIST P-256, SM2, and NIST P-224 curves as case studies, on the 32-bit ARM NEON platform, we demonstrate the effectiveness of Borrowless and its significant performance advantage over existing vector designs (e.g., CICOS and MR) and widely-used algorithm libraries (e.g., OpenSSL and GMP).