ABSTRACT Hard disk drive (HDD) failures in large‐scale distributed storage systems can lead to severe data loss and service disruptions. While failure prediction using SMART data is a critical mitigation strategy, existing models often inadequately capture the gradual degradation of disk health, suffering from limitations in feature selection and temporal modeling. To overcome these challenges, this paper proposes GRUHP, a Gated Recurrent Unit‐based Health Prediction model integrated with an adaptive feature selection mechanism. GRUHP efficiently handles high‐dimensional Self‐Monitoring, Analysis, and Reporting Technology (SMART) data by dynamically identifying the most discriminative features, while its GRU architecture fully leverages temporal patterns for accurate health assessment. The model also incorporates dedicated modules for continuous health state evaluation and fault diagnosis. Extensive experiments on two public datasets demonstrate that GRUHP achieves an average precision of 93.4%, recall of 99.3%, and F 0.5 ‐score of 94.5%, with a false positive rate of only 0.7%. These results confirm that the proposed method, through its synergistic feature selection and temporal modeling, offers a robust and highly applicable solution for proactive failure prediction in real‐world storage environments.

ABSTRACT Serverless computing has emerged as a promising paradigm for cloud‐based stream processing applications characterized by fluctuating workloads and latency sensitivity. While existing Function‐as‐a‐Service (FaaS) implementations primarily focus on homogeneous CPU/memory resource scaling, they fail to address the challenges of heterogeneous resource management and coordinated elasticity in distributed stream processing. This study proposes HFaaS, a novel serverless framework that integrates dataflow programming with heterogeneous resource orchestration for stream processing applications. The key innovations include: (1) a dataflow‐oriented function composition model enabling dynamic scaling of individual processing stages through peer‐to‐point communication mechanisms, (2) a fine‐grained GPU resource allocation strategy achieving 15% + utilization improvement through device sharing and elastic scaling capabilities, and (3) a locality‐aware scheduling algorithm optimizing task placement based on data proximity and heterogeneous resource availability. Experimental results demonstrate that HFaaS effectively coordinates multi‐stage function scaling while maintaining sub‐second latency guarantees. The proposed resource allocation strategy improves GPU utilization by 15.2% compared to conventional static allocation approaches, with network overhead reduced by 31.6% through data‐local scheduling. This work bridges the gap between serverless architectures and modern stream processing requirements, providing a unified platform for building resource‐efficient, latency‐sensitive distributed applications in heterogeneous cloud environments.

ABSTRACT Amid the rapid expansion of digital infrastructure and the escalating sophistication of cyberattack strategies, network traffic anomaly detection has emerged as a critical cybersecurity mechanism for securing modern digital ecosystems. To overcome the shortcomings of traditional machine learning methods—specifically their limited accuracy in traffic pattern recognition—this paper proposes a novel C‐LSTM anomaly detection model enhanced by an attention mechanism. Building on advancements in deep learning architectures, the proposed model integrates CNNs and Bi‐LSTM networks to comprehensively capture spatial and temporal traffic features. The attention mechanism mitigates Bi‐LSTM's inherent vulnerability to vanishing gradients during long‐sequence data processing by adaptively reweighting feature significance, thereby optimizing detection performance. The model was rigorously validated using the NSL‐KDD and UNSW‐NB15 standard benchmark datasets and evaluated against contemporary state‐of‐the‐art detection methods. Experimental results demonstrate superior performance, with classification accuracies of 97.3% on NSL‐KDD and 95.8% on UNSW‐NB15, alongside a 12% reduction in false positives compared to baseline models. Notably, the attention mechanism achieved incremental accuracy improvements of 1.62% (NSL‐KDD) and 1.48% (UNSW‐NB15) compared to the baseline CNN‐LSTM model. These findings demonstrate the model's effectiveness in enhancing anomaly detection robustness, providing a practical framework for real‐world cybersecurity implementations.

Abstract In recent years, network coding has received extensive attention and has been applied to various computer network systems, since it has been mathematically proven to enhance the network robustness and maximize the network throughput. However, it is well-known that network coding is extremely vulnerable to pollution attacks. Certificateless network coding scheme (CLNS) is a recently proposed mechanism to defend against pollution attacks for network coding, which avoids tedious management of certificates and key-escrow attack. Until now, only a few constructions were presented, and more ones should be given so as to enrich this field. In this paper, for the first time, we study the general construction of CLNS from certificateless public auditing protocol (CL-PAP), although the two areas seem to be quite different in their nature and are studied independently. Since there are many candidates of CL-PAPs, we can naturally obtain abundant constructions of CLNSs according to our systematic way. In addition, in order to show the power of the general construction, we also present a concrete implementation given a specific CL-PAP. The performance analysis and experimental results show that the implemented CLNS is competitive in the existing network coding schemes.

Recently, Rabaninejadet al.(2019) proposed an excellent auditing protocol for shared data (CoRPA, for short) [IEEE Trans. Ser. Comp., DOI 10.1109/TSC.2019.2919627], which has many better properties, like the identity-privacy, collusion resistant, efficient user revocation and supporting dynamic update etc. In addition, they also presented the detailed security analysis for CoRPA and described the reduction from the soundness of CoRPA to discrete logarithm assumption. However, in this article, we analyze their original security reduction (to discrete logarithm) and find out that it is incorrect and misleading. That is, the soundness of CoRPA cannot be obtained from the discrete logarithm assumption. Now, we give a new proof for their CoRPA based on the square-CDH assumption, which is also used by them to prove the security of homomorphic proxy re-signature scheme. We also hope the new security proof will provide theoretical guarantee when using CoRPA in practical scenes.

With the advent of the era of big data, the recommendation system has become an effective solution to the problem of information overload. This paper takes the literature data related to the recommendation system theme from 2009 to 2018 and included in the core collection of Web of Science database as the research object, and utilizes bibliometric methods to analyze the theme of recommendation system. To this end, firstly, classify statistics and feature analysis of valid literature data. Secondly, use VOSviewer software to construct various different scientific knowledge graph to discover valuable knowledge. Thirdly, according to keyword co-concurrence graph conclude five main hotspots of current research about recommendation system and discover five main directions that have potential value in research field of recommendation system. Finally, deeply explore five main key issues and propose corresponding solutions.

In 2014, Wu et al. proposed a tag encoding scheme, named KEPTE, to protect network coding against pollution attack. They also carefully analyzed the security of KEPTE based on the transmission of a data file through their key-pre-distributed network. In this article, we point out that their security analysis only holds for single data file transmitted in this network. If multiple files are multicasted though it, then any adversary may completely recover source node's signing key. A concrete example says that, after pre-distributing 90 keys to all the nodes in the network, it only allows to securely transmit (at most) 3 data files. More importantly, this scheme is completely insecure in standard security model for network model since the adversary is allowed to make polynomial times queries on any data files of its choice before outputting its final forgery. Finally, we also propose a twisted KEPTE scheme that is secure against any eavesdropping adversary no matter how many data files it has queried.

Abstract In PKC 2000, Pointcheval presented a generic technique to make a highly secure cryptosystem from any partially trapdoor one-way function in the random oracle model. More precisely, any suitable problem providing a one-way cryptosystem can be efficiently derived into a chosen-ciphertext attack (CCA) secure public key encryption (PKE) scheme. In fact, the overhead only consists of two hashing and a XOR. In this paper, we consider the key-dependent message (KDM) security of the Pointcheval’s transformation. Unfortunately, we do not know how to directly prove its KDM-CCA security because there are some details in the proof that we can not bypass. However, a slight modification of the original transformation (we call twisted Pointcheval’s scheme) makes it possible to obtain the KDM-CCA security. As a result, we prove that the twisted Pointcheval’s scheme achieves the KDM-CCA security without introducing any new assumption. That is, we can construct a KDM-CCA secure PKE scheme from partial trapdoor one-way injective family in the random oracle model.