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The computer journal
British Computer Society
The computer journal

British Computer Society

0010-4620

The computer journal/Journal The computer journal
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    Generating training events for building cyber-physical security skills

    Avanthika Vineetha HarishKimberly TamKevin Jones
    445-459页
    查看更多>>摘要：As the threat of cyber attacks increases, the cyber security sector continues to train professionals as one form of mitigation. Gamification and infrastructure like cyber ranges (CRs) have been proven useful in training cyber security specialists to detect and react to cyber-attacks in information technology. However, a subset of all digital threats target cyber-physical systems (CPS), not just information systems, and these threats have been growing rapidly in recent years. While those threats are increasing, training specific to cyber-physical threats is not growing as quickly as information technology training solutions. While CRs are useful for training and supporting events like cyber defence exercises and capture the flags events, limitations to CRs make it more difficult to make an immersive event for cyber-physical training. This paper demonstrates how to devise a cyber-physical training environment using a combination of CRs and CPS testbeds.and how to devise an accompanying immersive scenario to improve cyber-physical security skills through cooperative learning. To do this, an example training was created and implemented. This paper concludes with the results of running this event four times.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Can LLMs deeply detect complex malicious queries? A framework for jailbreaking via obfuscating intent

    Shang ShangXinqiang ZhaoZhongjiang YaoYepeng Yao...
    460-478页
    查看更多>>摘要：This paper delves into a possible security flaw in large language models (LLMs), particularly in their capacity to identify malicious intent within intricate or ambiguous inquiries. We have discovered that LLMs might overlook the malicious nature of highly veiled requests, even without alterations to the malevolent text in those queries, thus exposing a significant weakness in their content analysis systems. To be specific, we pinpoint and scrutinize two aspects of this vulnerability: (ⅰ) LLMs' diminished capability to perceive maliciousness when parsing extremely obscured queries, and (ⅱ) LLMs' inability to discern malicious intent in queries that have been intentionally altered to increase their ambiguity by modifying the malevolent content itself. To illustrate and tackle this problem, we propose a theoretical framework and analytical strategy, and introduce a novel black-box jailbreak attack technique called IntentObfuscator. This technique exploits the identified vulnerability by concealing the genuine intentions behind user prompts, thereby compelling LLMs to inadvertently produce restricted content and circumvent their inherent content safety protocols. We elaborate on two specific applications within this framework: "Obscure Intention" and "Create Ambiguity," which skillfully manipulate the complexity and ambiguity of queries to effectively dodge the detection of malicious intent. We empirically confirm the efficacy of the IntentObfuscator approach across various models, including ChatGPT-3.5, ChatGPT-4, Qwen, and Baichuan, achieving an average jailbreak success rate of 69.21%. Remarkably, our tests on ChatGPT-3.5, boasting 100 million weekly active users, yielded an impressive success rate of 83.65%. Additionally, we verify our approach across a range of sensitive content categories, including graphic violence, racism, sexism, political sensitivity, cybersecurity threats, and criminal techniques, further highlighting the considerable impact of our findings on refining "Red Team" tactics against LLM content security frameworks.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Admission control algorithm for visible light communication random access network under delay and jitter constraints

    Hongliang SunZhihui LiuChao WangDan Li...
    479-486页
    查看更多>>摘要：This paper proposes a random access network admission control algorithm under delay and jitter constraints to efficiently guarantee the quality of service of a visible light communication system. The multi-packet reception technology is considered to enhance network service, and the interrupted Bernoulli process is used to model the arrival process of packets. The queuing model of the system is constructed from the perspective of a single transmission link, and this paper derives the probability of successful transmission for the terminals. The delay and jitter performance are evaluated according to the state transition matrix and Little's law. The article set up the optimization problem with the goal of maximizing the throughput and the constraint of transmission probability. To solve the optimization problem better, we improve the tunicate swarm algorithm. Moreover, an admission control algorithm is designed under delay and jitter constraints. Simulation results demonstrate the influence of different parameters on the algorithm.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Internet of vehicles intrusion detection method based on CFS-COA feature selection and spatio-temporal feature extraction

    Zhongjun YangJixue ZhangBeimin Su
    487-501页
    查看更多>>摘要：With the rapid spread of the Internet of Vehicles (IoV) technology, vehicle network security is facing increasingly severe challenges. Intrusion detection technology has become a crucial tool for ensuring the information security of IoV. Since the traffic data of the IoV is large and has spatio-temporal characteristics, most previous studies are based on a single deep learning method to extract temporal or spatial features, which does not fully extract features of IoV data. To address the above issues, a spatio-temporal feature extraction model with feature selection is proposed. First, to solve the problem of long detection time with huge data traffic, a new feature selection method is proposed to screen the optimal feature subset by combining the correlation-based feature selection method with the crayfish optimization algorithm (CFS-COA). Second, the selected optimal features are used in a spatio-temporal feature extraction model that combines a Temporal Convolutional Network and a Bidirectional Gated Recurrent Unit (TCN-BiGRU) for classification. Finally, the performance of the model is evaluated using two types of datasets: the NSL-KDD and UNSW-NB15 datasets for external communications, and the Car-Hacking dataset for in-vehicle networks. The experimental results indicate that the proposed model demonstrates high classification performance and lightweight characteristics, achieving 100% accuracy on the Car-Hacking dataset.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Structure connectivity of folded cross cubes

    Lina BaHeping Zhang
    502-509页
    查看更多>>摘要：Connectivity is an important parameter to measure fault-tolerance of networks. As a generalization, structure connectivity and substructure connectivity of networks were proposed. For connected graphs G and H, the H-structure connectivity κ(G;H) (resp. H-substructure connectivity κ~s(G;H)) of G is the minimum cardinality of a set of subgraphs F of G that each is isomorphic to H (resp. a connected subgraph of H) such that G - F is disconnected or the singleton, n-dimensional folded cross cube, FCQ_n, is a network obtained by adding edges to n-dimensional cross cubes. In this paper, we study star, path, and cycle structure connectivity and substructure connectivity of FCQ_n, where n > 8. For star (K_(1,m)) structure, we get that κ(FCQ_n;K_(1,m)) = κ~s(FCQ_n;K_(1,m)) =「(n+1)/2」 for 2 ≤ m ≤ n/2- For path (P_k) structure, we show that for 3 ≤ k ≤ n + 1, if k is odd, then κ(FCQ_n;P_k) = κ~s(FCQ_n;P_k) = 「(2(n+1))/(k+1)」, if k is even, then κ(FCQ_n;P_k) = κ~s(FCQ_n;P_k) = 「(2(n+1))/k」. For cycle (C_k) structure, we prove that κ(FCQ_n;C_k) = κ~s(FCQ_n;P_k). Further, we calculate κ(FCQ_n; C_(2k-1)) = 「(n+1)/(k-1)」 for 4 ≤ k ≤ n + 2 and C_(2k)-structure connectivity of FCQ_n is 「(n+1)/k」 +1 for 6≤k<n + l and even k.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Verifiable attribute-based multi-keyword ranked search scheme in blockchain

    Yang ChenChunlu ZhaoJin PanYang Liu...
    510-519页
    查看更多>>摘要：In recent years, cloud storage has gradually become a promising tool for providing data sharing and data storage services with privacy protection. However, to enable users to search over encrypted data and to enable data owners to perform fine-grained search authorization on their encrypted files is still a great challenge. Although attribute-based keyword search (ABKS) is a well-received solution to the challenge, the direct adoption of the traditional ABKS schemes in cloud storage suffers two issues. The first problem is the lack of integrity verification of search results. That is, the users cannot verify whether the server has actually performed a search without compromise. The second issue is that the retrieval modes of most ABKS schemes are not flexible enough. Aiming at the above two issues, a verifiable ABKS scheme is proposed herein. Besides, we proposed security definitions for two types of adversaries and proved that the proposed scheme is verifiable and able of resisting outside keyword guessing attack and chosen keyword attack. Finally, we carried out all-round simulations with actual data set, showing that the proposed scheme has advantage in efficiency over other similar schemes.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Algorithm for solving quantum linear systems of equations with coherent superposition and extended applications

    Qiqing XiaQianru ZhuHuiqin XieLi Yang...
    520-538页
    查看更多>>摘要：Many quantum algorithms for attacking symmetric cryptography involve the rank problem of quantum linear equations. In this paper, two quantum algorithms are proposed to solve quantum linear systems of equations with coherent superposition, and their specific quantum circuits are constructed. In contrast to previous related studies, our quantum algorithms are universal, computing both the rank and general solution by one measurement. The difference between them is whether the data register containing the quantum coefficient matrix can be disentangled from the other registers while keeping the data qubits unchanged. On this basis, the two quantum algorithms are applied as subroutines to parallel Simon's algorithm (with multiple periods), Grover Meets Simon algorithm, and Alg-PolyQ2 algorithm. Subsequently, a quantum classifier within Grover Meets Simon algorithm and a detailed test oracle within Alg-PolyQ2 algorithms are constructed, including their respective quantum circuits. To the best of our knowledge, no such specific analysis has been previously performed. The success probability of these algorithms is rigorously analyzed to ensure that the probability of success on the proposed quantum algorithms will not be lower than that of the original algorithms. Finally, we discuss the lower bound of the number of controlled-NOT gates for solving quantum linear systems of equations with coherent superposition. Our analysis indicates that the proposed algorithms are suitable for conducting attacks against lightweight symmetric ciphers within the effective working time of an ion-trap quantum computer.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Enhancing security of medical images using code-based intermittent encryption and convolutional neural network

    Prasanth AruchamyM. SundarrajanMani Deepak ChoudhryAkshya Jothi...
    539-551页
    查看更多>>摘要：There has recently been a rise in the demand for telemedicine systems that securely and effectively transmit medical pictures. Cognitive radio (CR) significantly uses the unutilized spectrum by using the notion of spectrum sensing. Like certain other patient records, medical imaging data has strict requirements for security and anonymity. This makes sending healthcare picture information via an exposed system difficult because of the problems identified and the risks associated with massive data spillage. This study suggests a reliable CR technology with an image encryption technique to transmit medical images securely. In the proposed approach, the convolutional neural network method has been employed for complaisant spectrum sensing, where the Fusion Center trains the network for classification tasks using historical sensing data. Due to the proper training, the system runs in a time-slotted fashion. The proposed method provides an actor-critic transfer learning technique for a secondary user to select its processing method to raise confidence level while observing energy constraints. Finally, the numerical simulation results are examined to assess the suggested approaches under various configurations related to peak signal-to-noise ratio and structural similarity index which provide 90% more efficiency than the traditional simulated techniques.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Variations towards an efficient drug-drug interaction

    Yaxun JiaZhu YuanHaoyang WangYunchao Gong...
    552-564页
    查看更多>>摘要：Drug-drug interactions (DDIs) are a crucial research focus in clinical pharmacology and public health. DDIs can lead to reduced drug efficacy or increased adverse reactions, making the effective identification and understanding of drug interactions essential for patient safety and treatment outcomes. With the rapid growth of biomedical literature, automated methods for extracting DDI information have become increasingly necessary. In this paper, we propose BLRG, a novel model that uniquely integrates BioBERT, long short-term memory (LSTM), and relational graph convolutional network (R-GCN) to extract complex DDIs. This combination allows the model to effectively capture both semantic and relational features, outperforming existing methods in handling intricate dependencies in biomedical texts. Specifically, our approach begins by utilizing the BioBERT model to capture deep contextual features of sentences, extracting their semantic information. Following this, an LSTM network processes the sequential features of the sentence to model its contextual dependencies. Finally, an R-GCN is applied to identify and interpret the relationships between drug entities within the sentence, accurately capturing DDI information. Experimental results demonstrate that our model significantly outperforms current state-of-the-art methods across standard datasets, showcasing its effectiveness and potential in complex DDI extraction tasks.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    BioElectra-BiLSTM-Dual Attention classifier for optimizing multilabel scientific literature classification

    Muhammad Inaam ul haqQianmu LiKhalid MahmoodAyesha Shafique...
    565-576页
    查看更多>>摘要：Scientific literature is growing in volume with time. The number of papers published each year by 28 100 journals is 2.5 million. The citation indexes and search engines are used extensively to find these publications. An individual receives many documents in response to a query, but only a few are relevant. The final documents lack structure due to inadequate indexing. Many systems index research papers using keywords instead of subject hierarchies. In the scientific literature classification paradigm, various multilabel classification methods have been proposed based on metadata features. The existing metadata-driven statistical measures use bag of words and traditional embedding techniques, like Word2Vec and BERT, which cannot quantify textual properties effectively. In this paper, we try to solve the limitations of existing classification techniques by unveiling the semantic context of the words using an advanced transformer-based recurrent neural networks (RNN) approach incorporating Dual Attention and layer-wise learning rate to enhance the classification performance. We propose a novel model, BioElectra-BiLSTM-Dual Attention that extracts the semantic features from the titles and abstracts of the research articles using BioElectra-encoder and then BILSTM layer along with Dual Attention label embeddings their correlation matrix and layer-wise learning rate strategy employed for performance enhancement. We evaluated the performance of the proposed model on the multilabel scientific literature LitCovid dataset and the results suggest that it significantly improves the macro-F1 and micro-F1 score as compared to the state-of-the-art baselines (ML-Net, Binary Bert, and LitMCBert).
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press