-
Deep Learning-Based Cyberbullying Detection in Kurdish Language Comput. J. (IF 1.4) Pub Date : 2024-03-04 Soran Badawi
Cyberbullying is a significant concern in this digital age due to its harmful effects on individuals and society. Sadly, social media platforms have only exacerbated the problem, making it imperative to find effective ways to identify and prevent offensive content. While previous research has extensively focused on English and explored machine learning techniques to tackle this issue. To address this
-
An Improved Density Peaks Clustering Algorithm Based On Density Ratio Comput. J. (IF 1.4) Pub Date : 2024-03-04 Yujuan Zou, Zhijian Wang, Pengfei Xu, Taizhi Lv
Density peaks clustering (DPC) is a relatively new density clustering algorithm. It is based on the idea that cluster centers always have relatively high local densities and are relatively far from the points with higher densities. With the aforementioned idea, a decision graph can be drawn, and cluster centers will be chosen easily with the aid of the decision graph. However, the algorithm has its
-
Service Function Placement Optimization For Cloud Service With End-to-End Delay Constraints Comput. J. (IF 1.4) Pub Date : 2024-03-04 Guofeng Yan, Zhengwen Su, Hengliang Tan, Jiao Du
Network function virtualization (NFV) has been proposed to enable flexible management and deployment of the network service in cloud. In NFV architecture, a network service needs to invoke several service functions (SFs) in a particular order following the service chain function. The placement of SFs has significant impact on the performance of network services. However, stochastic nature of the network
-
BotGSL: Twitter Bot Detection with Graph Structure Learning Comput. J. (IF 1.4) Pub Date : 2024-03-04 Chuancheng Wei, Gang Liang, Kexiang Yan
Twitter bot detection is an important and meaningful task. Existing methods can be bypassed by the latest bots that disguise themselves as genuine users and evade detection by mimicking them. These methods also fail to leverage the clustering tendencies of users, which is the most important feature for detecting bots at the community level. Moreover, they neglect the implicit relations between users
-
Concept Drift–Based Intrusion Detection For Evolving Data Stream Classification In IDS: Approaches And Comparative Study Comput. J. (IF 1.4) Pub Date : 2024-03-04 Sugandh Seth, Kuljit Kaur Chahal, Gurvinder Singh
Static machine and deep learning algorithms are commonly used in intrusion detection systems (IDSs). However, their effectiveness is constrained by the evolving data distribution and the obsolescence of the static data sources used for model training. Consequently, static classifiers lose efficacy, necessitating expensive model retraining with time. The aim is to develop a dynamic and adaptable IDS
-
CFAuditChain: Audit BlockChain Based On Cuckoo Filter Comput. J. (IF 1.4) Pub Date : 2024-02-29 Kang Liu, Yang Lu, Shiyi Tan, Wei Liang, Huiping Sun, Zhong Chen
Log blockchain can be used to ensure the integrity of log data. However, current methods are facing the problems of throughput mismatch and rough audit granularity. Packaging multiple logs to generate integrity proofs improves throughput but reduces audit granularity, and the auditor can only locate the tampering log packet instead of specific records. This paper proposes CFAuditChain, an audit blockchain
-
CM-UTC: A Cost-sensitive Matrix based Method for Unknown Encrypted Traffic Classification Comput. J. (IF 1.4) Pub Date : 2024-02-27 Zhiyuan Gao, Jinguo Li, Liangliang Wang, Yin He, Peichun Yuan
Deep learning has been widely adopted in the field of network traffic classification due to its unique advantages in handling encrypted network traffic. However, most existing deep learning models can only classify known encrypted traffic that has been sampled and labeled. In this paper, we propose CM-UTC, a cost-sensitive matrix-based method for classifying unknown encrypted traffic. CM-UTC explores
-
A Hybrid BERT-CNN Approach for Depression Detection on Social Media Using Multimodal Data Comput. J. (IF 1.4) Pub Date : 2024-02-27 Rohit Beniwal, Pavi Saraswat
Due to the absence of early facilities, a large population is dealing with stress, anxiety, and depression issues, which may have disastrous consequences, including suicide. Past studies revealed a direct relationship between the high engagement with social media and the increasing depression rate. This research initially creates a dataset with text, emoticons and image data, and then preprocessing
-
Borehole Depth Recognition Based on Improved YOLOX Detection Comput. J. (IF 1.4) Pub Date : 2024-02-12 Dawei Ren, Lingwei Meng, Rui Wang
This study proposes a method for recognizing the drill depth in low-light underground environments, with the aim of addressing the issues of low efficiency and susceptibility to manual changes in the current methods. The method is based on an improved You Only Look Once X model. Initially, image data undergo enhancement and annotation. Secondly, it incorporates an attention mechanism to improve the
-
An Intelligent Security System Using Enhanced Anomaly-Based Detection Scheme Comput. J. (IF 1.4) Pub Date : 2024-02-12 Faten Louati, Farah Barika Ktata, Ikram Amous
Ensuring the security of computer networks is of utmost importance, and intrusion detection plays a vital role in safeguarding these systems. Traditional intrusion detection systems (IDSs) often suffer from drawbacks like reliance on outdated rules and centralized architectures, limiting their performance in the face of evolving threats and large-scale data networks. To address these challenges, we
-
Discrete-Time Quantum Walks Community Detection in Multi-Domain Networks Comput. J. (IF 1.4) Pub Date : 2024-02-09 Xiaoyang Liu, Nan Ding, Yudie Wu, Giacomo Fiumara, Pasquale De Meo
The problem of detecting communities in real-world networks has been extensively studied in the past, but most of the existing approaches work on single-domain networks, i.e. they consider only one type of relationship between nodes. Single-domain networks may contain noisy edges and they may lack some important information. Thus, some authors have proposed to consider the multiple relationships that
-
Visual Intrusion Detection Based On CBAM-Capsule Networks Comput. J. (IF 1.4) Pub Date : 2024-02-09 Zhongjun Yang, Qing Huang, Qi Wang, Xuejun Zong, Ran Ao
Intrusion detection has become a research focus in internet information security, with deep learning algorithms playing a crucial role in its development. Typically, intrusion detection data are transformed into a two-dimensional matrix by segmenting, stacking and padding them with zeros for input into deep learning models. However, this method consumes computational resources and fails to consider
-
SEDD: Robust Blind Image Watermarking With Single Encoder And Dual Decoders Comput. J. (IF 1.4) Pub Date : 2024-02-09 Yuyuan Xiang, Hongxia Wang, Ling Yang, Mingze He, Fei Zhang
Blind image watermarking is regarded as a vital technology to provide copyright of digital images. Due to the rapid growth of deep neural networks, deep learning-based watermarking methods have been widely studied. However, most existing methods which adopt simple embedding and extraction structures cannot fully utilize the image features. In this paper, we propose a novel Single-Encoder-Dual-Decoder
-
Link Residual Closeness of Graphs with Fixed Parameters Comput. J. (IF 1.4) Pub Date : 2024-02-07 Leyou Xu, Chengli Li, Bo Zhou
Link residual closeness is a newly proposed measure for network vulnerability. In this model, vertices are perfectly reliable and the links fail independently of each other. It measures the vulnerability even when the removal of links does not disconnect the graph. In this paper, we characterize those graphs that maximize the link residual closeness over the connected graphs with fixed order and one
-
LiteMixer: Cauliflower Disease Diagnosis based on a Novel Lightweight Neural Network Comput. J. (IF 1.4) Pub Date : 2024-02-03 Yi Zhong, Zihan Teng, Mengjun Tong
Cauliflower, a globally cultivated and nutritionally rich crop, confronts significant challenges in quality and yield due to the rising prevalence of diseases. Traditional manual detection methods, suitable for empiricists or plant pathologists, prove inefficient. Furthermore, existing automated disease identification methods in cauliflower often neglect crucial computational performance metrics within
-
Joint Alignment Networks For Few-Shot Website Fingerprinting Attack Comput. J. (IF 1.4) Pub Date : 2024-02-03 Qiang Zhou, Liangmin Wang, Huijuan Zhu, Tong Lu, Heping Song
Website fingerprinting (WF) attacks based on deep neural networks pose a significant threat to the privacy of anonymous network users. However, training a deep WF model requires many labeled traces, which can be labor-intensive and time-consuming, and models trained on the originally collected traces cannot be directly used for the classification of newly collected traces due to the concept drift caused
-
Partitioned 2D Set-Pruning Segment Trees with Compressed Buckets for Multi-Dimensional Packet Classification Comput. J. (IF 1.4) Pub Date : 2024-02-03 Yeim-Kuan Chang, Hsin-Mao Chen
Multi-dimensional packet classification is one of the most important functions to support various services in next generation routers. Both the memory-efficient data structure to support larger rule tables and the hardware architecture to achieve a higher throughput are desired. In this paper, we propose a parallel and pipelined architecture called Set-Pruning Segment Trees with Buckets (SPSTwB) for
-
CNN-LSTM Base Station Traffic Prediction Based On Dual Attention Mechanism and Timing Application Comput. J. (IF 1.4) Pub Date : 2024-02-01 Hairong Jia, Suying Wang, Zelong Ren
Energy consumption in 5G base stations remains consistently high, even during periods of low traffic loads, thereby resulting in unnecessary inefficiencies. To address this problem, this paper presents a novel approach by proposing a convolutional neural network (CNN)-long short-term memory (LSTM) traffic prediction model with a dual attention mechanism, coupled with the particle swarm optimization
-
Multimodal Sentiment Analysis Based on Composite Hierarchical Fusion Comput. J. (IF 1.4) Pub Date : 2024-02-01 Yu Lei, Keshuai Qu, Yifan Zhao, Qing Han, Xuguang Wang
In the field of multimodal sentiment analysis, it is an important research task to fully extract modal features and perform efficient fusion. In response to the problems of insufficient semantic information and poor cross-modal fusion effect of traditional sentiment classification models, this paper proposes a composite hierarchical feature fusion method combined with prior knowledge. Firstly, the
-
Enhancing Aspect Category Detection Through Hybridised Contextualised Neural Language Models: A Case Study In Multi-Label Text Classification Comput. J. (IF 1.4) Pub Date : 2024-01-29 Kursat Mustafa Karaoglan, Oguz Findik
Recently, the field of Natural Language Processing (NLP) has made significant progress with the evolution of Contextualised Neural Language Models (CNLMs) and the emergence of large LMs. Traditional and static language models exhibit limitations in tasks demanding contextual comprehension due to their reliance on fixed representations. CNLMs such as BERT and Semantic Folding aim to produce feature-rich
-
A General Blockchain-Based Automatic Audit Scheme For Proofs Of Retrievability Comput. J. (IF 1.4) Pub Date : 2024-01-29 Xiuyuan Chen, Chao Lin, Wei Wu, Debiao He
Cloud storage has been widely used in remote data management, although correct storage of the outsourced file is still challenging in practice. Proofs of Retrievability (PoRs), a storage-oriented cryptographic tool, support integrity checking and efficient retrieval of the file. However, due to the lack of a fully credible oversight mechanism or a serious dependence on a trusted third party, most PoRs
-
Hybrid ITÖ Algorithm for Maximum Scatter Colored Traveling Salesman Problem Comput. J. (IF 1.4) Pub Date : 2024-01-15 Xueshi Dong, Qing Lin, Wei Wang
This research presents a new problem maximum scatter colored traveling salesman problem (MSCTSP), the objective of MSCTSP is to find Hamiltonian cycles with the minimal edge as max as possible, it is used to simulate the real-world applications of network and transport. Since MSCTSP has been proved to be a NP-hard problem, population-based algorithms can be used for solving it. However, the performances
-
Ensemble of Deep Features for Breast Cancer Histopathological Image Classification Comput. J. (IF 1.4) Pub Date : 2024-01-15 Jaffar Atwan, Nedaa Almansour, Mohammad Hashem Ryalat, Shahnorbanun Sahran, Hamza Aldabbas, Dheeb Albashish
Analysis of histopathological images (HIs) is crucial for detecting breast cancer (BR). However, because they vary, it is still very difficult to extract well-designed elements. Deep learning (DL) is a recent development that is used to extract high-level features. However, DL techniques continue to confront several difficult problems, such as the need for sufficient training data for DL models, which
-
From Stars to Diamonds: Counting and Listing Almost Complete Subgraphs in Large Networks Comput. J. (IF 1.4) Pub Date : 2023-12-26 Irene Finocchi, Renan Leon Garcia, Blerina Sinaimeri
Listing dense subgraphs is a fundamental task with a variety of network analytics applications. A lot of research has been done focusing on $k$-cliques, i.e. complete subgraphs on $k$ nodes. However, requiring complete connectivity between the nodes of a subgraph may be too restrictive in many real applications. Hence, in this paper, we consider a natural relaxation of cliques, called $k$-diamonds
-
WiPoTS: An Application Layer Protocol With Network Protocol Stack Enhancements For Wireless Power Transfer Networks Comput. J. (IF 1.4) Pub Date : 2023-12-23 Muhammad Omer Farooq
Nowadays, a far-field wireless power transfer (WPT) system aims to deliver wireless power over a distance of a few meters. Communication among devices for the purpose of WPT in the far-field WPT system is unique as its purpose is to establish, maintain and monitor a WPT session among devices in the system. For proper functionality of a WPT system, a number of communication-, control- and management-related
-
Laws of Timed State Machines Comput. J. (IF 1.4) Pub Date : 2023-12-23 Ana Cavalcanti, Madiel Conserva Filho, Pedro Ribeiro, Augusto Sampaio
State machines are widely used in industry and academia to capture behavioural models of control. They are included in popular notations, such as UML and its variants, and used (sometimes informally) to describe computational artefacts. In this paper, we present laws for state machines that we prove sound with respect to a process algebraic semantics for refinement, and complete, in that they are sufficient
-
Online Optimization Method of Learning Process for Meta-Learning Comput. J. (IF 1.4) Pub Date : 2023-12-02 Zhixiong Xu, Wei Zhang, Ailin Li, Feifei Zhao, Yuanyuan Jing, Zheng Wan, Lei Cao, Xiliang Chen
Meta-learning is a pivotal and potentially influential machine learning approach to solve challenging problems in reinforcement learning. However, the costly hyper-parameter tuning for training stability of meta-learning is a known shortcoming and currently a hotspot of research. This paper addresses this shortcoming by introducing an online and easily trainable hyper-parameter optimization approach
-
Enhancing Auditory Brainstem Response Classification Based On Vision Transformer Comput. J. (IF 1.4) Pub Date : 2023-11-07 Hunar Abubakir Ahmed, Jafar Majidpour, Mohammed Hussein Ahmed, Samer Kais Jameel, Amir Majidpour
A method for testing the health of ear’s peripheral auditory nerve and its connection to the brainstem is called an auditory brainstem response (ABR). Manual quantification of ABR tests by an audiologist is not only costly but also time-consuming and susceptible to errors. Recently in machine learning have prompted a resurgence of research into ABR classification. This study presents an automated ABR
-
Eager Term Rewriting For The Fracterm Calculus Of Common Meadows Comput. J. (IF 1.4) Pub Date : 2023-11-07 Jan A Bergstra, John V Tucker
Eager equality is a novel semantics for equality in the presence of partial operations. We consider term rewriting for eager equality for arithmetic in which division is a partial operator. We use common meadows which are essentially fields that contain an absorptive element $\bot $. The idea is that term rewriting is supposed to be semantics preserving for non-$\bot $ terms only. We show soundness
-
An Intrusion Detection Method Based on Attention Mechanism to Improve CNN-BiLSTM Model Comput. J. (IF 1.4) Pub Date : 2023-11-07 Dingyu Shou, Chao Li, Zhen Wang, Song Cheng, Xiaobo Hu, Kai Zhang, Mi Wen, Yong Wang
Security of computer information can be improved with the use of a network intrusion detection system. Since the network environment is becoming more complex, more and more new methods of attacking the network have emerged, making the original intrusion detection methods ineffective. Increased network activity also causes intrusion detection systems to identify errors more frequently. We suggest a
-
Leveraging Meta-Learning To Improve Unsupervised Domain Adaptation Comput. J. (IF 1.4) Pub Date : 2023-11-07 Amirfarhad Farhadi, Arash Sharifi
Unsupervised Domain Adaptation (UDA) techniques in real-world scenarios often encounter limitations due to their reliance on reducing distribution dissimilarity between source and target domains, assuming it leads to effective adaptation. However, they overlook the intricate factors causing domain shifts, including data distribution variations, domain-specific features and nonlinear relationships,
-
Underwater Wireless Sensor Network-Based Delaunay Triangulation (UWSN-DT) Algorithm for Sonar Map Fusion Comput. J. (IF 1.4) Pub Date : 2023-10-13 Xin Yuan, Ning Li, Xiaobo Gong, Changli Yu, Xiaoteng Zhou, José-Fernán Martínez Ortega
Robust and fast image recognition and matching is an important task in the underwater domain. The primary focus of this work is on extracting subsea features with sonar sensor for further Autonomous Underwater Vehicle navigation, such as the robotic localization and landmark mapping applications. With the assistance of high-resolution underwater features in the Side Scan Sonar (SSS) images, an efficient
-
The Orbits of Folded Crossed Cubes Comput. J. (IF 1.4) Pub Date : 2023-10-13 Jia-Jie Liu
Two vertices $u$ and $v$ in a graph $G=(V,E)$ are in the same orbit if there exists an automorphism $\phi $ of $G$ such that $\phi (u)=v$. The orbit number of a graph $G$, denoted by $Orb(G)$, is the smallest number of orbits, which form a partition of $V(G)$, in $G$. All vertex-transitive graphs $G$ are with $Orb(G)=1$. Since the $n$-dimensional hypercube, denoted by $Q_{n}$, is vertex-transitive
-
Similarity Regression Of Functions In Different Compiled Forms With Neural Attentions On Dual Control-Flow Graphs Comput. J. (IF 1.4) Pub Date : 2023-10-13 Yun Zhang, Yuling Liu, Ge Cheng, Jie Wang
Detecting if two functions in different compiled forms are similar has a wide range of applications in software security. We present a method that leverages both semantic and structural features of functions, learned by a neural-net model on the underlying control-flow graphs (CFGs). In particular, we devise a neural function-similarity regressor (NFSR) with attentions on dual CFGs. We train and evaluate
-
An Intelligent Air Monitoring System For Pollution Prediction: A Predictive Healthcare Perspective Comput. J. (IF 1.4) Pub Date : 2023-10-10 Veerawali Behal, Ramandeep Singh
The extensive potential of Internet of Things (IoT) technology has enabled the widespread real-time perception and analysis of health conditions. Furthermore, the integration of IoT in the healthcare industry has resulted in the development of intelligent applications, including smartphone-based healthcare, wellness-aware recommendations and smart medical systems. Building upon these technological
-
Identifying and Ranking Influential Spreaders in Complex Networks by Localized Decreasing Gravity Model Comput. J. (IF 1.4) Pub Date : 2023-10-07 Nan Xiang, Xiao Tang, Huiling Liu, Xiaoxia Ma
Identifying crucial nodes in complex networks is paid more attention in recent years. Some classical methods, such as degree centrality, betweenness centrality and closeness centrality, have their advantages and disadvantages. Recently, the gravity model is applied to describe the relationship of nodes in a complex network. However, the interaction force in gravity model follows the square law of distance
-
Sparse Backdoor Attack Against Neural Networks Comput. J. (IF 1.4) Pub Date : 2023-10-07 Nan Zhong, Zhenxing Qian, Xinpeng Zhang
Recent studies show that neural networks are vulnerable to backdoor attacks, in which compromised networks behave normally for clean inputs but make mistakes when a pre-defined trigger appears. Although prior studies have designed various invisible triggers to avoid causing visual anomalies, they cannot evade some trigger detectors. In this paper, we consider the stealthiness of backdoor attacks from
-
Coalitional Double Auction For Ridesharing With Desired Benefit And QoE Constraints Comput. J. (IF 1.4) Pub Date : 2023-09-23 Jiale Huang, Jigang Wu, Long Chen, Yalan Wu, Yidong Li
Ridesharing is an effective approach to alleviate traffic congestion. In most existing works, drivers and passengers are assigned prices without considering the constraints of desired benefits. This paper investigates ridesharing by formulating a matching and pricing problem to maximize the total payoff of drivers, with the constraints of desired benefit and quality of experience. An efficient algorithm
-
Enumeration Of Subtrees Of Two Families Of Self-Similar Networks Based On Novel Two-Forest Dual Transformations Comput. J. (IF 1.4) Pub Date : 2023-09-23 Daoqiang Sun, Hongbo Liu, Yu Yang, Long Li, Heng Zhang, Asfand Fahad
As a structural topological index, the number of subtrees has great significance for the analysis and design of hybrid locally reliable networks. In this paper, with generating function and introducing a novel two-forest dual transformation technique, we solve the subtree enumerating problems of two representatives of the self-similar networks, such as the hierarchical lattice and $(u,v)$-flower networks
-
A Chinese Grammatical Error Correction Model Based On Grammatical Generalization And Parameter Sharing Comput. J. (IF 1.4) Pub Date : 2023-09-05 Nankai Lin, Xiaotian Lin, Yingwen Fu, Shengyi Jiang, Lianxi Wang
Chinese grammatical error correction (CGEC) is a significant challenge in Chinese natural language processing. Deep-learning-based models tend to have tens of millions or even hundreds of millions of parameters since they model the target task as a sequence-to-sequence problem. This may require a vast quantity of annotated corpora for training and parameter tuning. However, there are currently few
-
A Hybrid Solution For The Cold Start Problem In Recommendation Comput. J. (IF 1.4) Pub Date : 2023-08-29 Syed Irteza Hussain Jafri, Rozaida Ghazali, Irfan Javid, Yana Mazwin Mohmad Hassim, Mubashir Hayat Khan
Recommender systems are becoming more and more significant in today’s digital world and in the modern economy. They make a substantial contribution to company operations by offering tailored advice and decreasing overwhelm. Collaborative filtering, being popular in the domain of recommendation, is used to offer recommendations to attract the target audience based on the feedback of people with comparable
-
Improving Science That Uses Code Comput. J. (IF 1.4) Pub Date : 2023-08-25 Harold Thimbleby
As code is now an inextricable part of science it should be supported by competent Software Engineering, analogously to statistical claims being properly supported by competent statistics. If and when code avoids adequate scrutiny, science becomes unreliable and unverifiable because results — text, data, graphs, images, etc — depend on untrustworthy code. Currently, scientists rarely assure the quality
-
NDN-RBE: An Accountable Privacy Aware Access Control Framework For NDN Comput. J. (IF 1.4) Pub Date : 2023-08-16 Nazatul Haque Sultan, Vijay Varadharajan, Saurab Dulal, Seyit Camtepe, Surya Nepal
Named Data Networking (NDN) is an emerging network architecture. An important characteristic of NDN is its in-network cache, which enables Data packets to be available from multiple locations on the Internet. Hence the enforcement of access control mechanisms becomes even more critical in the NDN. This paper proposes a novel access control scheme referred to as Role-Based Encryption for NDN (NDN-RBE)
-
Truncated Differential Attacks On Symmetric Primitives With Linear Key Schedule: WARP And Orthros Comput. J. (IF 1.4) Pub Date : 2023-08-08 Shiqi Hou, Baofeng Wu, Shichang Wang, Hao Guo, Dongdai Lin
In truncated differential cryptanalysis of symmetric primitives, a generalized framework is to search a distinguisher concerning part of output differences, like truncated differential distribution (TDD) on certain bits (e.g. a nibble) first, and then append several rounds before and after it to recover the secret key. The logarithmic likelihood ratio statistic with respect to the TDD is usually used
-
FedEVCP: Federated Learning-Based Anomalies Detection for Electric Vehicle Charging Pile Comput. J. (IF 1.4) Pub Date : 2023-08-07 Zhaoliang Lin, Jinguo Li
Vehicle-to-Grid (V2G) is a technology that enables electric vehicles to use smart charging methods to harness low-cost and renewable energy when it is available, and obtain income by feeding energy back into the grid. With the rise of V2G technology, the use of electric vehicles has begun to increase dramatically, which relies on the reliable Electric Vehicle Charging Pile (EVCP). However, most EVCPs
-
A Large-Scale Mobile Traffic Dataset For Mobile Application Identification Comput. J. (IF 1.4) Pub Date : 2023-08-04 Shuang Zhao, Shuhui Chen, Fei Wang, Ziling Wei, Jincheng Zhong, Jianbing Liang
With Internet access shifting from desktop-driven to mobile-driven, application-level mobile traffic identification has become a research hotspot. Although considerable progress has been made in this research field, two obstacles are hindering its further development. Firstly, there is a lack of sharable labeled mobile traffic datasets. Although it is easy to capture mobile traffic, labeling traffic
-
CDNM: Clustering-Based Data Normalization Method For Automated Vulnerability Detection Comput. J. (IF 1.4) Pub Date : 2023-08-03 Tongshuai Wu, Liwei Chen, Gewangzi Du, Chenguang Zhu, Ningning Cui, Gang Shi
The key to deep learning vulnerability detection framework is pre-processing source code and learning vulnerability features. Traditional source code representation techniques take a complete normalization to user-defined symbols but ignore the semantic information associated with vulnerabilities. The current mainstream vulnerability feature learning model is Recurrent Neural Network (RNN), whose time-series
-
Reversible Data Hiding With Pattern Adaptive Prediction Comput. J. (IF 1.4) Pub Date : 2023-08-03 Junying Yuan, Huicheng Zheng, Jiangqun Ni
In the area of reversible data hiding (RDH), one of the most popular techniques is prediction-error expansion (PEE), which hides data in the prediction errors with well-preserved image fidelity. The key to a successful PEE-based RDH implementation usually lies in prediction algorithms with high accuracy. Existing PEE-based RDH works often employ one single prediction algorithm, which is usually globally
-
A Blockchain-Based Public Key Infrastructure For IoT-Based Healthcare Systems Comput. J. (IF 1.4) Pub Date : 2023-08-03 Amalan Joseph Antony, Kunwar Singh
Secure exchange of data among the various stake holders of healthcare systems is of prime importance. As the size of the healthcare networks grew, several variants of Public Key Infrastructures (PKIs) were proposed as a means to achieve reliable authentication, confidentiality, non-repudiation, etc. The most prevalent approach to PKI has been the use of Certificate Authorities (CAs). But, events like
-
eBiBa: A Post-Quantum Hash-Based Signature With Small Signature Size in the Continuous Communication of Large-Scale Data Comput. J. (IF 1.4) Pub Date : 2023-07-24 Lingyun Li, Xianhui Lu, Kunpeng Wang
We present eBiBa (enhanced BiBa), a hash-based signature scheme with the smallest possible signature size, while ensuring high feasibility and security in a specific application model. Our scheme is tailored to address the communication requirement of a large-scale public data stream continuously disseminated between two participants while ensuring data source and data integrity authentication. To
-
Relinearization Attack On LPN Over Large Fields Comput. J. (IF 1.4) Pub Date : 2023-07-15 Paul Lou, Amit Sahai, Varun Sivashankar
We investigate algebraic attacks on the Learning Parity with Noise ($\mathsf{LPN}$) problem over large fields in parameter settings relevant to building indistinguishability obfuscation in which the proportion of corrupted equations is inverse-polynomially sparse. Our aim was to obtain a subexponential algorithm using the Macaulay expansion and relinearization. Alas, we did not. Nevertheless, our findings
-
A Hybrid Scheme Combining Duplications and LDPC Decoding to Reduce NAND Flash Comput. J. (IF 1.4) Pub Date : 2023-07-15 Yaofang Zhang, Peixuan Li, Ping Xie
With the development of NAND flash, the storage density has become larger and larger, but the reliability has become lower and lower. To address this problem, stronger error correction codes, low-density parity-check (LDPC) codes, are widely used in flash memory. However, the direct use of LDPC codes for error correction has a great impact on the read and write performance of SSDs. Given this, this
-
Improved Related-Key Rectangle Attacks On GIFT Comput. J. (IF 1.4) Pub Date : 2023-07-15 Qingyuan Yu, Lingyue Qin, Xiaoyang Dong, Keting Jia
GIFT is a lightweight cipher proposed by Banik et al. at CHES’17, motivated by the design strategy of PRESENT. GIFT-64[2021] is a variant of GIFT proposed by Sun et al. at EUROCRYPT’22 to achieve better resistance against differential attack while maintaining a similar security level against linear attack. At EUROCRYPT’22, Dong et al. proposed a new rectangle framework considering the key guessing
-
A Break Of Barrier To Classical Differential Fault Attack On The Nonce-Based Authenticated Encryption Algorithm Comput. J. (IF 1.4) Pub Date : 2023-07-05 Shuai Liu, Jizhou Ren, Jie Guan, Bin Hu, Sudong Ma, Hao Bai
It had always been believed that there was an inherent barrier to Differential Fault Attack (DFA) on the nonce-based authenticated encryption algorithm. At CHES 2016, Saha et al. proposed an Internal Differential Fault Attack on a parallelizable counter-mode algorithm. They induce the attack to classical DFA at the expense of one more fault injection in every encryption process. In this paper, we propose
-
CSFL: Cooperative Security Aware Federated Learning Model Using The Blockchain Comput. J. (IF 1.4) Pub Date : 2023-06-29 Jiaomei Zhang, Ayong Ye, Jianwei Chen, Yuexin Zhang, Wenjie Yang
Federated learning (FL) is a focus of research in the area of privacy protection since it does not have the privacy issues that arise from data concentration. Although its emergence has attracted widespread attention from academia and industry, existing works on FL still face security challenges. FL can be considered as a cooperative-based task to achieve global model sharing. However, the model raises
-
Towards Accurate Smartphone Localization Using CSI Measurements Comput. J. (IF 1.4) Pub Date : 2023-06-29 Runze Yang, Baoqi Huang, Zhendong Xu, Bing Jia, Gang Xu
In comparison with capturing channel state information (CSI) measurements via a laptop or desktop, using a smartphone to collect CSI measurements incurs the restriction of working with a single access point and significant signal distortions, resulting in limited information for smartphone localization. Therefore, this paper intends to leverage as much available localization information as possible
-
A Concept Forensic Methodology For The Investigation Of IoT Cyberincidents Comput. J. (IF 1.4) Pub Date : 2023-06-28 Juan Manuel Castelo Gómez, Javier Carrillo-Mondéjar, José Roldán-Gómez, José Luis Martínez Martínez
The number of Internet of Things (IoT) forensic investigations has increased considerably over recent years due to the weak nature of the security measures of its devices. In order to ensure the effectiveness and completeness of their examinations, investigators rely on forensic models, frameworks and methodologies. However, given the novelty of the environment, the existing ones are not refined enough
-
Spatial-Aware Multi-Directional Autoencoder For Pre-Training Comput. J. (IF 1.4) Pub Date : 2023-06-28 Weiwei Yang, Shangsong Liang, Jian Yin
Vision Transformers for pre-trained models explore semantic context and spatial relationships for images, which heavily depend on how you select image patches. In this paper, we propose a novel Spatial-aware Multi-directional Patches Multi-cycle Autoencoder (SMPMA) for a pre-trained model that brings the following benefits: (1) Spatial-aware Multi-directional (SM) patches are created with multi-directional
-
Efficient Comparison Of Independence Structures Of Log-Linear Models Comput. J. (IF 1.4) Pub Date : 2023-06-10 Jan Strappa, Facundo Bromberg
Log-linear models are a family of probability distributions which capture relationships between variables. They have been proven useful in a wide variety of fields such as epidemiology, economics and sociology. The interest in using these models is that they are able to capture context-specific independencies, relationships that provide richer structure to the model. Many approaches exist for automatic
-
Hybrid Optimal Ensemble SVM Forest Classifier for Task Offloading in Mobile Cloud Computing Comput. J. (IF 1.4) Pub Date : 2023-06-09 Erana Veerappa Dinesh Subramaniam, Valarmathi Krishnasamy
Mobile devices (MDs) are becoming more prevalent and their battery life is optimised by offloading tasks to cloud servers. However, communication costs must be considered when offloading tasks. To make task offloading worthwhile, it is important to measure the energy consumed during communication activities. Thus, a heterogeneous framework is developed to enhance the energy efficiency of smartphones