The Computer Journal

Journal Information
ISSN / EISSN : 0010-4620 / 1460-2067
Published by: Oxford University Press (OUP) (10.1093)
Total articles ≅ 7,588
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Jin Cao, Liwei Lin, , Haibing Guan, Mengke Tian, Yong Wang
Published: 30 September 2022
With the rapid development of the Internet of Things (IoT), network security challenges are becoming more and more complex, and the scale of intrusion attacks against the network is gradually increasing. Therefore, researchers have proposed Intrusion Detection Systems and constantly designed more effective systems to defend against attacks. One issue to consider is using limited computing power to process complex network data efficiently. In this paper, we take the AWID dataset as an example, propose an efficient data processing method to mitigate the interference caused by redundant data and design a lightweight deep learning-based model to analyze and predict the data category. Finally, we achieve an overall accuracy of 99.77% and an accuracy of 97.95% for attacks on the AWID dataset, with a detection rate of 99.98% for the injection attack. Our model has low computational overhead and a fast response time after training, ensuring the feasibility of applying to edge nodes with weak computational power in the IoT.
Lanlan Rui, Shiyue Dai, Zhipeng Gao, Xuesong Qiu, Xingyu Chen
Published: 24 September 2022
In recent years, Named Data Network (NDN) has become a popular network architecture because of high resource utilization, strong security and high transmission efficiency. Meanwhile, mobile multimedia communication has become the mainstream with the popularization and application of smart terminals. Most of the research on NDN mobility is focused on consumer mobility without taking producer mobility into account. To solve the delay and high cost carried by producer moving, we propose a Double-Lead content search algorithm based on neighbor and proxy and a location prediction algorithm based on traffic features. We use a neural network model to predict a new location of producers and calculate route before switching, which can save the rerouting latency in advance when predicting accurately. In a few cases of inaccurate predictions, we select different search methods according to the distance of the producer’s movement, to complete the Double-Lead search between the producer and the consumer. Experimental results show that DLPNDN can reduce the delay and traffic overhead well in NDN when the producer moves.
Zheng Zhang, Fangguo Zhang
Published: 19 September 2022
Recently, there has been renewed interest in conjunction obfuscations. A conjunction, which is called pattern matching with wildcards sometimes, is associated with a pattern $\mathsf{pat}\in \{0,1,*\}^n$ where * is a wildcard. It accepts if and only if the input bits are the same as the pattern at all non-wildcard positions. The conjunction obfuscation starts to get noticed because it provides the ability to protect these sensitive patterns while preserving its functionality. It is meaningful when the conjunction obfuscation is applied in the pattern matching, biological recognition, resisting SQL injection attacks and so on. In this work, we propose a new candidate of conjunction obfuscation. It not only retains the simplicity of the intuitive scheme in BKM18, but also adds wildcards to the pattern. Besides, we also propose a conjunction obfuscation with multi-bit output. The second obfuscation has the same size of the obfuscated program as the first obfuscation. Both obfuscations provide the distributional virtual black-box security.
Xiong Liqin, Cao Lei, Chen Xiliang, Lai Jun, Luo Xijian
Published: 19 September 2022
Value factorization is a popular method for cooperative multi-agent deep reinforcement learning. In this method, agents generally have the same ability and rely only on individual value function to select actions, which is calculated from total environment reward. It ignores the impact of individual characteristics of heterogeneous agents on actions selection, which leads to the lack of pertinence during training and the increase of difficulty in learning effective policies. In order to stimulate individual awareness of heterogeneous agents and improve their learning efficiency and stability, we propose a novel value factorization method based on Personality Characteristics, PCQMIX, which assigns personality characteristics to each agent and takes them as internal rewards to train agents. As a result, PCQMIX can generate heterogeneous agents with specific personality characteristics suitable for specific scenarios. Experiments show that PCQMIX generates agents with stable personality characteristics and outperforms all baselines in multiple scenarios of the StarCraft II micromanagement task.
Published: 17 September 2022
This is a correction to: Yeting Li, Haiming Chen, Zixuan Chen, Learning Disjunctive Multiplicity Expressions and Disjunctive Generalize Multiplicity Expressions From Both Positive and Negative Examples, The Computer Journal, 2022; bxac037, doi:
Junfeng Tian, Haoyi Jia, Wenqing Bai
Published: 16 September 2022
At present, most of the causal consistency models that rely on cloud storage have problems such as high operation delays and large metadata overhead. To solve these problems, this paper proposes a causal consistency model for edge storage based on hash rings, CCESHP. The proposed model uses two hashes to map the keys and servers on the hash ring for grouping and stores a subset of the complete data set in a replica node located at the edge of the network, thereby realizing a partial geographic replication strategy in the edge storage environment. Operation latency will be reduced since the edge replica is closer to the client. At the same time, it also generates and maintains a combined timestamp to capture causality according to the update type, which can keep the amount of managed metadata in a relatively stable and low state, reduce the overhead of system management metadata, and improve system throughput. The experimental evaluation results under different workloads show that the model has better performance in throughput and operation delay when compared with the existing causal consistency model.
Kexin Xu, Benjamin Hong Meng Tan, Li-Ping Wang, Khin Mi Mi Aung, Huaxiong Wang
Published: 11 September 2022
Homomorphic Encryption (HE) supports computation on encrypted data without the need to decrypt, enabling secure outsourcing of computing to an untrusted cloud. Motivated by application scenarios where private information is offered by different data owners, Multi-Key Homomorphic Encryption (MKHE) and Threshold Homomorphic Encryption (ThHE) were proposed. Unlike MKHE, ThHE schemes do not require expensive ciphertext extension procedures and are therefore as efficient as their underlying single-key HE schemes. In this work, we propose a novel NTRU-type ThHE scheme which caters to the computation scenarios with pre-defined participants. In addition to inheriting the simplicity of NTRU scheme, our construction has no expensive relinearization and correspondingly no costly evaluation keys. Controlling noise to make it increase linearly and then using a wide key distribution, our scheme is immune to the subfield lattice attacks and its security follows from the hardness of the standard R-LWE problem. Finally, based on the {0,1}-linear secret sharing and noise flooding techniques, we design a single round distributed threshold decryption protocol, where the decryption is able to be completed even when only given a subset (say $t$-out-of-$k$) of partial decryptions. To the best of our knowledge, our construction is the first NTRU-type ThHE scheme.
Wooyoung Park, SeungBum Jo, Srinivasa Rao Satti
Published: 10 September 2022
We design a practical variant of an encoding for range Top-2 query (RT2Q) and evaluate its performance. Given an array $A[1,n]$ of $n$ elements from a total order, the range Top-2 encoding problem is to construct a data structure that answers ${\textsf{RT2Q}}{}$, which returns the positions of the first and second largest elements within a given range of $A$, without accessing the array $A$ at query time. We design the following two implementations: (i) an implementation based on an alternative representation of Davoodi et al.’s [Phil. Trans. Royal Soc. A, 2016] data structure, which supports queries efficiently. Experimental results show that our implementation is efficient in practice and gives improved time-space trade-offs compared with the indexing data structures (which keep the original array $A$ as part of the data structure) for range maximum queries. (ii) Another implementation based on Jo et al.’s ${\textsf{RT2Q}}{}$ encoding on $2 \times n$ array [CPM, 2016], which can be constructed in $O(n)$ time. We compare our encoding with Gawrychowski and Nicholson’s optimal encoding [ICALP, 2015] and show that in most cases, our encoding shows faster construction time while using a competitive space in practice.
Shuai Liu,
Published: 9 September 2022
In this paper, an image hashing scheme combining 3D space contour (TDSC) features with vector angle (VA) features is proposed. The proposed algorithm extracts the 3D contours of the local component variation features of the image and the expression changes of the local component of the image in the form of a 3D VA to improve the performance. First, the gray component of the color image is used to construct a 3D space and the contour change features of the local component of the gray image are extracted using multi-perspectives. Then, the opposite color component and the brightness component Y of the YCbCr color space are extracted from the input image. The angular features of several image components are, respectively, extracted in the 3D space. Finally, the TDSC features are combined with the VA features to obtain image hashing. The simulations demonstrate and validate that the proposed image hashing scheme not only has better classification performance compared with the other image hashing techniques but is also equipped with the performance of tamper localization.
Zirui Qiao, Qiliang Yang, Yanwei Zhou, Bo Yang, Mingwu Zhang
Published: 9 September 2022
To ensure privacy and security of healthcare wireless medical sensor networks (HWMSNs), several concrete constructions of efficient certificateless aggregate signature (CLAS) scheme without bilinear pairing were proposed in the last few years. However, many previous constructions of CLAS scheme were found to be impractical, which either fail to meet the claimed security or contain design flaws. For example, in some of the previous proposals, any adversary can forge a valid signature on any new message. In this paper, we first demonstrate some security issues and design flaws in the previous proposals of CLAS scheme. As follows, to further address the above deficiencies, a new construction of CLAS scheme with improved security is presented, and the formal security proof is given using Forking Lemma in the random oracle model, assuming that the discrete logarithm problem is hard. Compared with the previous CLAS schemes, our construction has similar computational costs, and it provides better security guarantees. Therefore, compared with the existing solutions, our proposal with strong security and high computational efficiency is more suitable for use in HWMSNs.
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