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SDN controller selection and secure resource allocation

IEEE Access 2025年 13卷 77278-77290页

作者： Pappu, Sasibhushana Rao Chilukuri, Kalyana Chakravarthy JNTUK Department of Computer Science and Engineering Andhra Pradesh Kakinada533003 India GITAM School of Technology Department of Computer Science and Engineering Visakhapatnam India MVGR College of Engineering Department of Computer Science and Engineering Andhra Pradesh Vizianagaram535005 India

Software Defined Networking (SDN) has emerged as a promising paradigm for network management. However, in energy-effective task scheduling and security, the centralized control architecture of SDN brings challenges. This research proposes a new approach for blockchain-based secure resource allocation with controller selection in SDN, utilizing the Entropy Oppositional Based Learning-Interpolation Blue Monkey Optimization Algorithm (EOBL-IBMOA). By establishing a blockchain-centric secure resource allocation with controller selection, the proposed technique addresses challenges in SDN. Here, user registration, load balancing, attack detection, controller selection, and resource allocation phases are included. Utilizing XOR Left Shift (XORLS), the user details are secured by IP traceback and hash codes are generated using the Mid Square-based KECCAK 512 (MS-KECCAK 512) algorithm. For effective traffic balancing, the load balancer uses the Minshev-KMeans algorithm. Attack classification is attained through the Quantile Transformer Scaling based SoftmaxGELU Gated Recurrent Units (QTS-SGGRU) approach. EOBL-IBMOA is used by controller selection and resource allocation for optimal Virtual Machine (VM) selection. The proposed technique’s superiority is described by experimental comparisons. The proposed approach attains effective resource allocation with reduced response time and high throughput, outperforming the prevailing works. © 2013 IEEE.

关键词： Resource allocation

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Performance analysis of naive vehicular named data networks

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International Journal of Wireless and Mobile Computing 2025年第3期28卷 234-244页

作者： Nithin Rao, R. Sharma, Rinki Department of Computer Science and Engineering Ramaiah University of Applied Sciences Karnataka Bangalore India

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 challenges such as interest/data flooding, routing, naming schemes, channel constraints and security, among others. The proposed work aims to analyse the behaviour of the naive VNDN under diverse scenarios, which is crucial for devising effective forwarding, caching and security strategies to enhance VNDN’s performance. In this paper, the naive VNDN architecture is implemented and its performance is evaluated by considering the metrics such as Throughput, Packet Delivery Ratio (PDR), Interest Satisfaction Delay (ISD), Interest Satisfaction Ratio (ISR), Copies of Data Packets Processed (CDPP) and Hop Count Difference (HCD). These metrics are tested against varying vehicle speed, network size and interests generated by each vehicle in the network. The simulation results provide insights that should be considered while developing the forwarding strategies and caching mechanisms that will improve the performance of VNDN. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： caching content forwarding data packets delay naming timestamp

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An optimized multi-scale convolutional autoencoder for efficient abnormal event detection using rgb, depth and optical flow data

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Multimedia Tools and Applications 2025年 1-35页

作者： Alqahtani, Abdullah Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University KSA Al-Kharj Saudi Arabia

In this study, we propose a novel framework for detecting abnormal events in surveillance videos, a critical yet challenging task in security applications. This research introduces a robust and efficient solution for video anomaly detection, offering substantial improvements in surveillance systems' ability to detect abnormal events, thereby contributing to enhanced security measures in public spaces. The proposed framework utilizes a Multiscale Convolutional Autoencoder (MSCAE) that processes inputs from RGB, depth, and optical flow video clips, enhancing the detection accuracy in complex scenes characterized by varying object scales, aspect ratios, and occlusions. To address the challenge of noise and preserve edges in video data, we implement a two-pass bilateral smooth filtering method, which is effective for noise-invariant, edge-preserving image smoothing. For object detection within these complex scenes, an enhanced Faster R-CNN model is employed. This model's performance is further refined through transfer learning on a dataset specifically composed of abnormal event videos. We also introduce significant improvements to the region proposal network (RPN) of the Faster R-CNN, particularly in non-maximum suppression (NMS) and anchor generation techniques, to better detect anomalies in diverse and complex environments. Furthermore, the MSCAE is integrated with Long Short-Term Memory (LSTM) neural networks to classify the detected anomalies, creating an end-to-end solution for video anomaly detection. Hyperparameter optimization for our deep learning models is performed using the Chameleon Swarm Algorithm, ensuring optimal model performance. Our framework was rigorously tested on the CUHK Avenue dataset, where it achieved a remarkable 99.5% accuracy, significantly outperforming existing methods and demonstrating the effectiveness of our approach. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Optical flows

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IWM-LSTM encoder for abstractive text summarization

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Multimedia Tools and Applications 2025年第9期84卷 5883-5904页

作者： Gangundi, Ravindra Sridhar, Rajeswari Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli620015 India

Sequence-to-sequence models are fundamental building blocks for generating abstractive text summaries, which can produce precise and coherent summaries. Recently proposed, different text summarization models aimed to enhance summarization performance through the use of copying mechanisms, reinforcement learning, and multiple-level encoders. However, there has been limited research on improving the summarization output by modifying the structure of the long short-term memory (LSTM) cell. We introduced an improved version of LSTM called improved working memory LSTM (IWM-LSTM). IWM-LSTM removes the output gate and enhances the input and forget gates by incorporating cell state information into these gates. In our sequence-to-sequence model for text summarization, we replaced the LSTM encoder with a bi-directional IWM-LSTM, resulting in better summaries with minimal training time and less computational intensiveness. Additionally, we utilized Bidirectional encoder representations from transformers (BERT) embeddings to enhance the rouge score. The CNN/DailyMail dataset is used to train and test the model performance. The proposed model achieves better Recall-oriented understudy for gisting evaluation (ROUGE) scores than state-of-the-art models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Machine learning and data analytics driven model for income tax rebate estimation: A neoteric and benign approach 4th

Machine learning and data analytics driven model for income ...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Viney Kumar, Achal Singh, Archana Kumar, Manish Department of Computer Science & Engineering Sharda University Agra India Department of Computer Science & Engineering Anand Engineering College Agra India Department of Computer Science & Engineering Chandigarh Engineering College Landran India

ISBN: (纸本)9781032911571

The provision of rebate to needy/underprivileged sections of society has been in practice since long in government organizations. The efficacy of such provisions lies in the fact that whether this rebate reaches people for whom it is intended. The biggest problem in awarding relief lies in the fact that people may not disclose their actual income. This paper conducts a study which strives for proposing a model which helps in assessing the financial status of any person given certain information about that person. This paper aims at proposing a Machine learning and Data Analytics driven model that predict the financial status of a person. The striking feature of this study is that results are applicable not only to income tax rebate problem but with minor customization, the model can be extended to work in other problems also where financial status of a person matters. © 2025 the Author(s).

关键词： Decentralized finance

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Malicious iOS Apps Detection Through Multi-Criteria Decision-Making Approach

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 207-220页

作者： Bhatt, Arpita Jadhav Sardana, Neetu Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Indonesia

In today’s era, smartphones are used in daily lives because they are ubiquitous and can be customized by installing third-party apps. As a result, the menaces because of these apps, which are potentially risky for user’s privacy, have increased. Information on smartphones is perhaps, more personal than compared to data stored on desktops or computers, making it an easy target for intruders. After Android, the most prevalently used mobile operating system is Apple’s iOS. Both Android and iOS follow permission-based access control to protect user’s privacy. However, the users are unaware whether the app is breaching the user’s privacy. To combat this problem, in the paper we propose a hybrid approach to detect malicious iOS apps based on its permissions. In the first phase, weights have been assigned to app permissions using multi-criteria decision-making (MCDM) approach namely Analytic Hierarchy Process (AHP), and in the second phase machine learning& ensemble learning techniques have been employed to train the classifiers for detecting malicious apps. To test the efficacy of the proposed method dataset comprising 1150 apps from 12 app categories has been used. The results demonstrate the proposed approach improves the efficacy of detecting malicious iOS apps for majority of categories. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Differential privacy

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Hybrid ECD model for firewall tuning and attack detection

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International Journal of Wireless and Mobile Computing 2025年第1期28卷 86-102页

作者： Thyagarajan, C. Vijay Bhanu S., S.V. Suthir, S. Department of Computer Science and Engineering Annamalai University Tamil Nadu Annamalai Nagar India

The rigorous security requirements and domain experts are necessary for the tuning of firewalls and for the detection of attacks. Those firewalls may create an incorrect sense or state of protection if they are improperly configured. One of the major configuration problems in firewalls is related to misconfiguration in the access control roles added to the firewall that will control network traffic. Furthermore, Software-Defined Networking (SDN) has greatly improved the network management. In this research, a hybrid Deep Learning (DL)-based firewall is designed. The request log is sent to the primary firewall, which tracks the network traffic and restricts the vulnerabilities and undesirable traffic. The EfficientNet-B3-Attn-2 fused Cascade Neuro-Fuzzy Network (ECD) is developed for network security whenever the primary firewall fails to regulate the network traffic. Furthermore, the devised framework is evaluated in terms of accuracy, sensitivity and specificity metrics that yield values like 0.885, 0.946 and 0.915. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： computer system firewalls

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VoteDroid: a new ensemble voting classifier for malware detection based on fine-tuned deep learning models

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Multimedia Tools and Applications 2025年第12期84卷 10923-10944页

作者： Bakır, Halit Faculty of Engineering and Natural Sciences Department of Computer Engineering Sivas University of Science and Technology Sivas Turkey

In this work, VoteDroid a novel fine-tuned deep learning models-based ensemble voting classifier has been proposed for detecting malicious behavior in Android applications. To this end, we proposed adopting the random search optimization algorithm for deciding the structure of the models used as voter classifiers in the ensemble classifier. We specified the potential components that can be used in each model and left the random search algorithm taking a decision about the structure of the model including the number of each component that should be used and its location in the structure. This optimization method has been used to build three different deep learning models namely CNN-ANN, pure CNN, and pure ANN. After selecting the best structure for each DL model, the selected three models have been trained and tested using the constructed image dataset. Afterward, we suggested hybridizing the fine-tuned three deep-learning models to form one ensemble voting classifier with two different working modes namely MMR (Malware Minority Rule) and LMR (Label Majority Rule). To our knowledge, this is the first time that an ensemble classifier has been fine-tuned and hybridized in this way for malware detection. The results showed that the proposed models were promising, where the classification accuracy exceeded 97% in all experiments. © The Author(s) 2024.

关键词： Android malware

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Attention enabled viewport selection with graph convolution for omnidirectional visual quality assessment

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Multimedia Tools and Applications 2025年第14期84卷 12925-12948页

作者： C, Nandhini M, Brindha Department of Computer Science and Engineering National Institute of Technology TamilNadu Tiruchirappalli620015 India

Omnidirectional images provide an immersive viewing experience in a Virtual Reality (VR) environment, surpassing the limitations of traditional 2D media beyond the conventional screen. This VR technology allows users to interact with visual information in an exciting and engaging manner. However, the storage and transmission requirements for 360-degree panoramic images are substantial, leading to the establishment of compression frameworks. Unfortunately, these frameworks introduce projection distortion and compression artifacts. With the rapid growth of VR applications, it becomes crucial to investigate the quality of the perceptible omnidirectional experience and evaluate the extent of visual degradation caused by compression. In this regard, viewport plays a significant role in omnidirectional image quality assessment (OIQA), as it directly affects the user’s perceived quality and overall viewing experience. Extracting viewports compatible with users viewing behavior plays a crucial role in OIQA. Different users may focus on different regions, and the model’s performance may be sensitive to the chosen viewport extraction strategy. Improper selection of viewports could lead to biased quality predictions. Instead of assessing the entire image, attention can be directed to areas that are more importance to the overall quality. Feature extraction is vital in OIQA as it plays a significant role in representing image content that aligns with human perception. Taking this into consideration, the proposed ATtention enabled VIewport Selection (ATVIS-OIQA) employs attention based view port selection with Vision Transformers(ViT) for feature extraction. Furthermore, the spatial relationship between the viewports is established using graph convolution, enabling intuitive prediction of the objective visual quality of omnidirectional images. The effectiveness of the proposed model is demonstrated by achieving state-of-the-art results on publicly available benchmark datasets, n

关键词： Convolution

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Towards Understanding the Impact of Participant and its Wearable Devices in Federated Learning

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5003-5015页

作者： Mishra, Rahul Gupta, Hari Prabhat Department of Computer Science and Engineering Institute of Technology Patna Patna India Varanasi India

The popularity of wearable smart devices has increased due to their seamless monitoring of vital signs during daily activities. Federated learning leverages these devices along with participants’ smartphones to fine-tune pre-trained models. Moreover, calibrating the differences between wearables and smartphones in terms of sampling rates, orientations, activity correlation, battery power, and other factors is challenging. Thus, the paper introduces a participant and wearable selection cross-device federated learning approach. It leverages criteria such as the activity wearable(s) relationship, data quality, battery life, sampling rate, and so on to perform the wearable selection. The server evaluates and estimates the utility of each participant and selects those with higher utility in each communication round. We then figure out the optimal weighted contribution of each participant to perform robust aggregation. We also use knowledge distillation techniques to develop a high-performing and lightweight wearable model. Finally, we conduct simulation and real-world experiments on existing datasets and compare our approach with state-of-the-art. The result shows an improvement of 3−4% in accuracy via fine-tuning from selected wearable data. © 2002-2012 IEEE.

关键词： Federated learning

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