Reviewerly/exHarmony huggingface dataset:
MCVDSSE: Secure Multi-client Verifiable Dynamic Symmetric Searchable Encryption

Existing multi-user dynamic symmetric searchable encryptions (DSSE) schemes unreasonably require private key sharing or require data owners to stay online. And existing verifiable DSSE is mostly based on public key primitives and confronts a large efficiency bottleneck. To address these issues, this paper proposes MCVDSSE: Secure Multi-Client Verifiable Dynamic Symmetric Searchable Encryption. In MCVDSSE, the data owner neither needs to stay online nor leak the critical private key to ensure secure data search and dynamic update. In addition, it further achieves forward and backward security, and is secure against replay attacks and collusion attacks. Finally, the security proof shows that the user has the ability to verify the integrity and timeliness.
SVFL: Secure Vertical Federated Learning on Linear Models

Federated learning (FL) is a popular technique that enables multiple parties to train a machine learning model collaboratively without disclosing the raw data to each other. A vertically partitioned federated learning configuration is applicable in a variety of real-world scenarios. In this configuration, a comprehensive feature collection is established only when all parties’ datasets are merged and only one party has access to the labels. Existing vertical federated learning strategies for linear models are not very practical, since they involve either a trusted third-party authority (TPA) or heavy communication overheads. To address this issue, this paper proposes SVFL, a secure vertical federated learning framework on linear models, which is based on the Verifiable Inner-Product Computation (VIP) protocol. SVFL enables the secure and private training of linear models, as well as the validation of a malicious server’s computation. In addition, it decreases the number of communication rounds to 3 and is resistant to collusion attacks. Experiments are done on a variety of real-world datasets from the UCI ML repository, and the results demonstrate that SVFL achieves comparable accuracy to conventional linear models.
Decompilation Based Deep Binary-Source Function Matching

Binary and source matching is vital for vulnerability detection or program comprehension. Most existing works focus on library matching (coarse-grained) by utilizing some simple features. However, they are so coarse-grained that high false positives occur since developers tend to reuse source code library partly. These shortcomings drive us to perform fine-grained matching (i.e., binary and source function matching). At the same time, due to the enormous differences between the form of binary and source functions, function matching (fine-grained) meets huge challenges. In this work, inspired by the decompilation technique and advanced neural networks, we propose tool, a Decompilation based deep Binary-Source function Matching framework. Specifically, we take the triplet features from both binary pseudo-code and source code functions as input, which are extracted from code property graph and can represent both the syntactic and semantic information. In this way, the binary and source functions are represented in the same feature space so to ease the matching model to learn function similarity. For the matching model, we adopt a self-attention based siamese network with contrastive loss. Experiments on two datasets, R0 and R3, show that our tool achieves consistent improvements than other methods, which demonstrate the effectiveness of our self-attention based matching model, and our triplets features can well capture the two kinds of code functions. Our work improves the accuracy of binary and source code matching, which in turn enables us to better address security issues such as vulnerability detection and program comprehension.
Multiprime Strategies for Serial Evaluation of eSIDH-Like Isogenies

We present new results and speedups for the large-degree isogeny computations within the extended supersingular isogeny Diffie-Hellman (eSIDH) key agreement framework. As proposed by Cervantes-Vázquez, Ochoa-Jiménez, and Rodríguez-Henríquez, eSIDH is an extension to SIDH and fourth round NIST post-quantum cryptographic standardization candidate SIKE. By utilizing multiprime large-degree isogenies, eSIDH and eSIKE are faster than the standard SIDH/SIKE and amenable to parallelization techniques that can noticeably increase their speed with multiple cores. Here, we investigate the use of multiprime isogeny strategies to speed up eSIDH and eSIKE in serial implementations. These strategies have been investigated for other isogeny schemes such as CSIDH. We apply them to the eSIDH/eSIKE scenario to speed up the multiprime strategy by about 10%. When applied to eSIDH, we achieve a 7–8% speedup for Bob’s shared key agreement operation. When applied to eSIKE, we achieve a 3–4% speedup for key decapsulation. Historically, SIDH and SIKE have been considerably slower than its competitors in the NIST PQC standardization process. These results continue to highlight the various speedups achievable with the eSIKE framework to alleviate these speed concerns. Though eSIDH and eSIKE are susceptible to the recent devastating attacks on SIKE, our analysis applies to smooth degree isogeny computations in general, and isogeny-based signature schemes which use isogenies of smooth (not necessarily powersmooth) degree.
Implementation of the Elliptic Curve Method

The Elliptic Curve Method (ECM) is a versatile algorithm for determining prime factors of medium-sized composite integers. It is commonly used in the sieving phase of the Number Field Sieve to accelerate factorization. In this paper, we propose new approaches to improve the efficiency of ECM. Specifically, we utilize a combination of twisted Hessian curves and Montgomery curves, along with a new combination of prime factors, to achieve improved performance. We present the scalar multiplication costs, which constitute the most computationally demanding aspect of ECM. Experimental results demonstrate that our approach achieves comparable efficiency to state-of-the-art ECM implementations.
Redactable Blockchain in the Permissioned Setting

As a momentous attribute of blockchains, the immutability ensures the integrity and credibility of historical data, but it is inevitably abused to spread illegal content and does not meet certain requirements of relevant privacy protection laws and regulations such as the General Data Protection Regulation (GDPR). In this paper, we focus on the redactable blockchain, which can break the immutability in a safe and controllable way without affecting the normal operation of the blockchain. We propose a redactable blockchain based on the aggregation signature in the permissioned setting. This scheme supports democratic instant modification and accountability, in which every user has the right to propose editing requests, and the credible balloting committee is responsible for reviewing and voting on the redaction. In addition, in order to achieve accountability, we introduce the concept of a witness chain to ensure that every revision can be traced.
Research on Encrypted Malicious 5G Access Network Traffic Identification Based on Deep Learning

The 5G small stations are widely deployed to improve the capacity of the 5G communication system. However, increasing malicious attacks are hidden in the traffic of small stations. Therefore, it is of great significance to identify the encrypted malicious traffic in the 5G access network. However, the traffic transferred through the backhaul of 5G small base station is usually encrypted. To this end, a deep learning-based method to identify the signaling hijacking traffic on the access network is proposed. Firstly, a 5G signaling hijacking system is developed to address the vulnerabilities of small stations and generate practical malicious traffic. To identify encrypted malicious traffic from 5G backhaul links, a 1D-CNN recognition model based on data packets is constructed. Finally, the 1D-CNN model is tested and validated multiple dimensions. The extensive experiment results reveal that the proposed method can achieve a recognition accuracy of over 99.95%.
A Robust Reversible Data Hiding Algorithm Based on Polar Harmonic Fourier Moments

The Robust Reversible Data Hiding (RRDH) algorithm can recover both the secret data and the cover image entirely from an intact stego image, and can still restore the secret message comprehensively even if the stego image undergoes various attacks. While many current RRDH schemes possess a strong capability for resisting attacks, they often fail to withstand geometric deformation attacks such as rotation and scaling. In this paper, a new RRDH algorithm based on Polar Harmonic Fourier Moments (PHFMs) is presented to enhance its resistance to geometric transformation attacks. Firstly, leveraging the rotation invariance of PHFMs, a quantitation index modulation algorithm is designed to embed secret data into the coefficients of PHFMs. This approach achieves a high degree of anti-geometric transformation capability for the stego image while minimizing image distortion. Additionally, the differences between the original and restored image are employed as compensation information and embedded into the restored image. Moreover, a two-dimensional reversible data hiding scheme is adopted to embed the compensation information in order to minimize image distortion. The combination of PHFMs transformation and two-dimensional reversible data hiding enables the proposed RRDH algorithm to achieve high visual quality and strong resistance capability against geometric transformation attacks. Extensive experimental results demonstrate that the proposed RRDH algorithm outperforms other state-of-the-art techniques.
DomainIsolation: Lightweight Intra-enclave Isolation for Confidential Virtual Machines

In recent years, there has been a rise in the use of confidential computing as a new computing paradigm that enables privacy-preserving computation on sensitive and regulated data. This approach relies heavily on hardware-based Trusted Execution Environments (TEE), which establish isolated regions for data processing within a protected CPU region. Currently, a variety of TEEs (such as p-enclave in HyperEnclave and AMD SEV) support privilege separation and running a fully-fledged operating system within the confidential Virtual Machines (VMs). However, running a fully-fledged operating system inevitably increases the trusted computing base (TCB), making it challenging to conduct security verification. To address the problem, this paper studies the cases when complex OS services (such as device drivers and networking etc.) are removed from the confidential VM, and presents DomainIsolation, a page table based lightweight and efficient isolation scheme. We show that DomainIsolation enhances both the security and performance of enclave applications through several case studies, including confinement for untrusted libraries, fine-grained data protection, and fast communication. We have integrated DomainIsolation with the Occlum library OS, Enarx and ported several real-world applications. The evaluations on common benchmarks and applications (such as NBench, Lighttpd, Redis and OpenSSL) show that DomainIsolation only introduces a low overhead (<2% in most cases).
Keeping Your Enemies Closer: Shedding Light on the Attacker’s Optimal Strategy

Realistically simulating a human attacker can effectively help the defender identify security weaknesses in the network. One important factor that affects the attacker’s strategy is the various human characteristics. In this paper, we develop an attack engine, dubbed Attacker-Patience-Experience-Curiosity or APEC for short, to model the attacker’s strategy under uncertainty. The proposed model is based on the Partially Observable Markov Decision Process (POMDP) model, taking three familiar characteristics of the attacker into consideration, including: (i) patience towards the target network; (ii) experience with attack tools; (iii) curiosity to develop new attack tools. These characteristics are modeled into the state space, action space, transition function, and reward function in the POMDP model. We further propose the betrayal principle, sunk cost, and “silence speaks volumes” to demonstrate how the attacker’s characteristics affect its strategy, and why the attacker’s strategy is changed at some specific points. We evaluate the effectiveness of the proposed model over two realistic network scenarios and draw several useful insights.
A Graphical Password Scheme Based on Rounded Image Selection

Graphical password is considered as an alternative to traditional textual password, but it also faces many threats such as shoulder-surfing attack. To design and build a more secure and robust graphical password system with the resistance to multiple attacks modalities, especially brute force attack, guessing attack and shoulder-surfing attack, it is important to avoid the credentials being captured in just one step, e.g., by adding several rounds of input. For example, with respect to shoulder-surfing attack resistance, the input design ought to incorporate a certain degree of fault tolerance, with the specific value determined based on the acceptable tolerance range. By integrating this fault tolerance characteristic, the system can effectively withstand shoulder-surfing attacks while preserving the integrity of the authentication procedure. In this work, we learn from the current literature and design a graphical password scheme based on rounded image selection (e.g., three rounds). We provide a detailed scheme design and perform a performance analysis via a user study. Our results indicate that our proposed scheme is viable and gets credit from the participants.
2D Crime Scene Investigation Simulator for the Criminology Schools of Negros Occidental

Criminal investigation is a fundamental subject of Criminal Justice education, where students develop analytical and critical thinking skills by performing crime scene investigations. However, the demand for enhanced training is also rising due to the rising population of criminology colleges. This training is vital as this is where the student’s skills and knowledge are applied and improved. This study aimed to develop a reliable, easy-to-use 2D crime scene investigation simulator to address this issue. The study utilized descriptive analytical tools for monitoring and assessing students’ performance and progress in crime scene investigation. The study also used the Agile method to ensure the development team can complete the project on time. After utilizing the Crime Scene Simulator, the Clustering Algorithm was applied to analyze the student’s performance. Only the crime scene investigation process was included in the study, excluding other methods, such as forensics, identifying crimes, or arresting perpetrators. The findings of the system testing show that the system is reliable and easy to use for tracking the progress and performance of the students. The College of Criminal Justice education of STI West Negros University was considered the pilot criminology school of this study. The project was developed during the School Year 2022-2023.
Predicting and Exploring Abandonment Signals in a Banking Task-Oriented Chatbot Service

In this study, we developed predictive models to address the problem of chatbot abandonment, a problem that can result in losing business opportunities. Specifically, we target on a conversation log dataset of a banking chatbot involving 1,373 users and hand-crafted features. By leveraging a pre-trained BERT model on the textural features and the hand-crafted features, the model achieved an F1-score of 0.89 in predicting discontinued conversation and 0.80 in predicting abandonment. Our findings indicate that textual features help capture more abandonment, while hand-crafted features improve detection precision. Our analysis with SHAP and LIME revealed that user typing, the chatbot expressing inability of recognizing intent, and the chatbot asking what users want to do during an ongoing conversation are top signals of user abandoning the chatbot. These findings suggest that chatbot designers should consider providing pre-set options or constraints for user inputs and presenting possible intents to the user and avoid expressing inability, incompetence, or ignoring the users’ current attempt.
Exploring the Role of Online Courses in COVID-19 Crisis Management in the Supply Chain Sector—Forecasting Using Fuzzy Cognitive Map (FCM) Models

Globalization has gotten increasingly intense in recent years, necessitating accurate forecasting. Traditional supply chains have evolved into transnational networks that grow with time, becoming more vulnerable. These dangers have the potential to disrupt the flow of goods or several planned actions. For this reason, increased resilience against various types of risks that threaten the viability of an organization is of major importance. One of the ways to determine the magnitude of the risk an organization runs is to measure how popular it is with the buying public. Although risk is impossible to eliminate, effective forecasting and supply chain risk management can help businesses identify, assess, and reduce it. As a result, good supply chain risk management, including forecasting, is critical for every company. To measure the popularity of an organization, there are some discrete values (bounce rate, global ranking, organic traffic, non-branded traffic, branded traffic), known as KPIs. Below are some hypotheses that affect these values and a model for the way in which these values interact with each other. As a result of the research, it is clear how important it is for an organization to increase its popularity, to increase promotion in the shareholder community, and to be in a position to be able to predict its future requirements.
A Novel RUL Prognosis Model Based on Counterpropagating Learning Approach

The aviation industry is one of the fastest-growing sectors and is crucial for both passenger transport and logistics. However, the high costs associated with maintenance, refurbishment, and overhaul (MRO) constitute one of the biggest challenges facing this industry. Motivated by the significant role that remaining useful life (RUL) prognostics can play in optimising MRO operations and saving lives, this paper proposes a novel data-driven RUL prognosis model based on counter propagation network principles. The proposed model introduces the recursive growing hierarchical self-organisation map (ReGHSOM) as a variant of SOM that can cluster multivariate time series with high correlations and hierarchical dependencies typically found in RUL datasets. Moreover, ReGHSOM is designed to allow this clustering to evolve dynamically at runtime without imposing constraints or prior assumptions on the hypothesis spaces of the architectures. The output of ReGHSOM is fed into the supervised learning layers of Grossberg to make the RUL prediction. The performance of the proposed model is comprehensively evaluated by measuring its learnability, evolution, and comparison with related work using standard statistical metrics. The results of this evaluation show that the model can achieve an average mean square error of 5.24 and an average score of 293 for the C-MPASS dataset, which are better results than most of the comparable works.
HYBRID OPTIMIZATION ENABLED SEGMENTATION AND DEEP LEARNING FOR BREAST CANCER DETECTION AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES

Breast cancer detection is a highly fatal disease and is normally detected considering histopathological images (HPIs). However, the complexity of the HPIs makes it challenging to detect breast cancer accurately. Further, manual detection is highly time-consuming and subjective and depends on the experience of the medical professionals. To overcome these issues, an effective deep learning (DL) method for detecting breast cancer from HPIs is proposed. Here, the proposed approach is realized using various processes, such as pre-processing, blood cell segmentation, feature extraction, and classification. Segmentation is accomplished using the SegAN, and classification is performed using the deep convolutional neural network (DCNN). Both networks are trained using the proposed invasive water Ebola optimization (IWEO) algorithm. The efficiency of breast cancer detection is improved by using various features, such as shape features, histogram of gradients (HOG) and local gradient patterns (LGP). Further, the IWEO-DCNN is inspected for its dominance by considering measures, such as accuracy, test negative rate (TNR) and test positive rate (TPR), and the experimental results show that the IWEO-DCNN attained a maximal accuracy of 0.963, TNR of 0.963 and TPR of 0.950.
Optimization of a recurrent neural network using automata with a variable structure

The subject of this study is to identify a set of common structural properties inherent in recurrent neural networks and stochastic automata, the feature of which is purposeful behavior in dynamic environments. At the same time, the necessary commonality of properties is revealed both in the process of their functioning and in the process of their training (tuning). The author considers in detail such topics as: formalization of purposeful behavior, consideration of the design of automata, as well as a comparative analysis of the considered designs of automata. From the revealed commonality of functioning and the established one-to-one correspondence of neurons of a fully connected recurrent neural network and states of a probabilistic automaton with a variable structure, it follows that the structure of a tuned stochastic automaton can be considered as a reference for a set of connections of a recurrent neural network. This leads, even at the setup stage, to the removal of redundant states (neurons) and connections between them, based on the parameters of the corresponding automaton. The methodology of the conducted research is the construction of a one-to-one correspondence between the neurons of a fully connected recurrent neural network and the internal states of an automaton with a variable structure and the probabilities of transitions between them that are relevant after the tuning process. With a one-to-one correspondence, the probabilities of transitions of the automaton correspond to the weights of connections between neurons of the optimal configuration. The main conclusions of the study: 1.Comparing the structures of recurrent neural networks and automata with a variable structure allows one to take advantage of an automaton with a variable structure to solve the problem of appropriate behavior in dynamic environments and build a recurrent neural network based on it; 2.The correspondence of the internal structure of a recurrent neural network and an automaton with a variable structure allows already at the training stage to release the trained recurrent neural network from redundant neurons and redundant connections in its structure; 3.Due to the fact that an automaton with a variable structure approaches the optimal automaton with linear tactics for these conditions with nonlinear values of the learning rate, this allows a logical analysis of the structure of the final recurrent neural network.