The rapid expansion of the Internet of Things (IoT) brings numerous benefits but further presents fresh difficulties, especially in terms of security. The distributed and interconnected nature of IoT devices makes the...
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Spectrum sensing data falsification (SSDF) attack, i.e., Byzantine attack, is one of the critical threats of the cooperative spectrum sensing where the Byzantine attackers (BAs) forward incorrect local sensing results...
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Spectrum sensing data falsification (SSDF) attack, i.e., Byzantine attack, is one of the critical threats of the cooperative spectrum sensing where the Byzantine attackers (BAs) forward incorrect local sensing results to mislead the fusion center on channel availability decisions. By using traditional voting rule, the cooperative spectrum sensing performance deteriorates significantly due to incorrect local sensing results. Then, reliability weight strategy becomes the popular solution to avoid incorrect sensing results from BAs and unreliable cognitive radio users (CRUs). However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Based on existing techniques, the BAs and CRUs may be assigned with low reliability weights or distinguished from the data fusion account. However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Then, existing techniques still suffer from BAs and negative impact of unreliable CRUs. In this paper, we propose the adaptive cooperative quality weight algorithm for mitigating the Byzantine attack issue by distinguishing the BAs and CRUs from the data fusion account while selecting only useful CRUs since the number of members in the account is also the important factor for cooperative spectrum sensing. In our proposed algorithm, we adopt a stable preference ordering towards ideal solution (SPOTIS) for determining the reliability of SUs which shows low computational complexity as compared to other reliability weight-based techniques. To achieve high sensing performance, our global decision threshold is adapted according to the reliability of reliable users. From the simulation results, our proposed algorithm significantly improves global detection probability and total error probability compared to the traditional votin
- Distributed denial-of-service (DDoS) attacks are the major threat that disrupts the services in the computer system and networks using traffic and targeted sources. So, real-world attack detection techniques are con...
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Network security has continuously been a major focus of research and concern on a global scale. The Intrusion Detection System (IDS), as a crucial defensive measure against network attacks, has undergone multiple iter...
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This article investigates the impact of Artificial Intelligence (AI) and ChatGPT in the business sector. It highlights the evolution of AI, focusing on the integration and applications of technologies like machine lea...
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This work introduces a novel Custom Question Answering (CQA) model leveraging Adam optimized Bidirectional Encoder Representations from Transformers (BERT-AO). This model tackles the challenge of combining textual and...
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The Information Retrieval system aims to discover relevant documents and display them as query responses. However, the ever-changing nature of user queries poses a substantial research problem in defining the necessar...
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The Information Retrieval system aims to discover relevant documents and display them as query responses. However, the ever-changing nature of user queries poses a substantial research problem in defining the necessary data to respond accurately. The Major intention for this study is for enhance the retrieval of relevant information in response to user queries. The aim to develop an advanced IR system that adapts to changing user requirements. By introducing WMO_DBN, we seek to improve the efficiency and accuracy of information retrieval, catering to both general and specific user searches. The proposed methodology comprises three important steps: pre-processing, feature choice, and categorization. Initially, unstructured data subject to pre-processing to transform it into a structured format. Subsequently, relevant features are selected to optimize the retrieval process. The final step involves the utilization of WMO_DBN, a novel deep learning model designed for information retrieval based on the query data. Additionally, similarity calculation is employed to improve the effectiveness for the network training model. The investigational evaluation for the suggested model was conducted, and its performance is measured regarding the metrics of recall, precision, accuracy, and F1 score, the present discourse concerns their significance within the academic realm. The results prove the superiority of WMO_DBN in retrieving relevant information compared to traditional approaches. This research introduces novel method for addressing the challenges in information retrieval with the integration of WMO_DBN. By applying pre-processing, feature selection, and a deep belief neural network, the proposed system achieves more accurate and efficient retrieval of relevant information. The study contributes to the advancement of information retrieval systems and emphasizes the importance of adapting to users' evolving search queries. The success of WMO_DBN in retrieving relevant inform
Software defect prediction (SDP) is considered a dynamic research problem and is beneficial during the testing stage of the software development life cycle. Several artificial intelligence-based methods were avai...
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Software defect prediction (SDP) is considered a dynamic research problem and is beneficial during the testing stage of the software development life cycle. Several artificial intelligence-based methods were available to predict these software defects. However, the detection accuracy is still low due to imbalanced datasets, poor feature learning, and tuning of the model's parameters. This paper proposes a novel attention-included Deep Learning (DL) model for SDP with effective feature learning and dimensionality reduction mechanisms. The system mainly comprises ‘6’ phases: dataset balancing, source code parsing, word embedding, feature extraction, dimensionality reduction, and classification. First, dataset balancing was performed using the density peak based k-means clustering (DPKMC) algorithm, which prevents the model from having biased outcomes. Then, the system parses the source code into abstract syntax trees (ASTs) that capture the structure and relationship between different elements of the code to enable type checking and the representative nodes on ASTs are selected to form token vectors. Then, we use bidirectional encoder representations from transformers (BERT), which converts the token vectors into numerical vectors and extracts semantic features from the data. We then input the embedded vectors to multi-head attention incorporated bidirectional gated recurrent unit (MHBGRU) for contextual feature learning. After that, the dimensionality reduction is performed using kernel principal component analysis (KPCA), which transforms the higher dimensional data into lower dimensions and removes irrelevant features. Finally, the system used a deep, fully connected network-based SoftMax layer for defect prediction, in which the cross-entropy loss is utilized to minimize the prediction loss. The experiments on the National Aeronautics and Space Administration (NASA) and AEEEM show that the system achieves better outcomes than the existing state-of-the-art models f
1 Introduction In Natural Language Processing(NLP),topic modeling is a class of methods used to analyze and explore textual corpora,i.e.,to discover the underlying topic structures from text and assign text pieces to ...
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1 Introduction In Natural Language Processing(NLP),topic modeling is a class of methods used to analyze and explore textual corpora,i.e.,to discover the underlying topic structures from text and assign text pieces to different *** NLP,a topic means a set of relevant words appearing together in a particular pattern,representing some specific *** is beneficial for tracking social media trends,constructing knowledge graphs,and analyzing writing *** modeling has always been an area of extensive research in *** methods like Latent Semantic Analysis(LSA)and Latent Dirichlet Allocation(LDA),based on the“bag of words”(BoW)model,often fail to grasp the semantic nuances of the text,making them less effective in contexts involving polysemy or data noise,especially when the amount of data is small.
Vehicular Named Data Networks (VNDN) is a content centric approach for vehicle networks. The fundamental principle of addressing the content rather than the host, suits vehicular environment. There are numerous challe...
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