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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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Adaptive Content Forwarding Mechanism for Platoon based Vehicular Named Data Networks

Adaptive Content Forwarding Mechanism for Platoon based Vehi...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Kaushik, Anu Garg, Deepanshu Nehra, Anushka Bali, Rasmeet S Baza, Mohamed Srivastava, Gautam Chandigarh University Computer Science & Engineering Department India Thapar Institute of Engineering and Technology Computer Science & Engineering Department India College of Charleston Department of Computer SC29414 United States Brandon University Department of Math and Computer Science Canada

ISBN: (纸本)9781665480451

Vehicular networking systems rely on Internet Protocol to exchange information among vehicles. With the increasing number of vehicles, the communication overhead has increased significantly a nd h as b ecome m ore c ontent centric. To resolve this problem, the Named data networking-based communication model has been used. This communication is completely based upon the content rather than the location and provides better network coverage comparatively. The vehicles used for communication purposes in a network are moving in some specific p atterns, b ased o n h aving t he s ame destination, with the same speed parameters etc. These vehicles which have common interests form a platoon. This vehicular platoon helps in various fields such as safe driving, energy efficiency and road safety. This paper provides a scheme for the applicability of NDN to the vehicular platoon. Special design features are proposed for communication purposes in V-NDN-based vehicular platoons. The backbone platoon network is used for data dissemination between the vehicles on the highway. To check the efficiency of the proposed scheme, extensive simulations have been performed on the ndnSim simulator. More precisely, different scenarios have been used and analyzed their efficiency i n t erms o f d elay and throughput. © 2022 IEEE.

关键词： Autonomous vehicles

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Secure and Efficient Data Transmission for IoT Devices in Untrusted Environments

Secure and Efficient Data Transmission for IoT Devices in Un...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jain, Upma Smitha, K. Battula, R.S.S. Raju Sarkar, Arindam Martin, Natasha Natrayan, L. Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Adichunchanagiri University Bgs Institute of Technology Department of Artificial Intelligence and Machine Learning India Aditya University Department of Computer Applications India Heritage Institute of Technology Department of Applied Electronics and Instrumentation Engineering Kolkata India Department of Law Symbiosis Law Pune India School of Engineering Simats Department of Mechanical Engineering Saveetha India

ISBN: (纸本)9798350375442

One technological trend that will not go away in the modern world is the Internet of Things (IoT). With the development of the Internet of Things, the world is becoming smarter. The efficacy, flexibility, and simplicity of deployment of IoT make it the preferred choice for most real-time applications. But energy efficiency and security are the big obstacles for the Internet of Things. Presented here are safe, energy-efficient solutions for the Internet of Things (IoT), a problem whose significance this study acknowledges. Since routing is the primary energy consumer, this study presents secure routing strategies that are both energy efficient and reliable. Three distinct safe data transmission techniques for the Internet of Things (IoT) are presented in this paper, all of which aim to reduce energy usage during routing. The first part of this study lays out a clustering-and secure route selection-based architecture for safe and energy-efficient data transmission with the Internet of Things (IoT). The W-HHO technique, which is based on Whale Optimization, is used to identify the secure routes, and fuzzy-based secure clustering is used for that purpose. Encryption protocols that work in tandem with cypher block chaining ensure the safety of sensitive information. Using industry-standard metrics like packet delivery rate, throughput, energy efficiency, network longevity, etc., we assess how well each solution performs. When compared to the current methods, the outcomes produced by the presented works demonstrate promising performance. © 2024 IEEE.

关键词： clustering data transmission energy efficiency fuzzy clustering harris hawks optimization (HHO) internet of things (IoT) secure routing security whale optimization algorithm

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Integrating Deep Learning and Machine Translation for Understanding Unrefined Languages

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computers, Materials & Continua 2022年第1期70卷 669-678页

作者： Hong Geun Ji Soyoung Oh Jina Kim Seong Choi Eunil Park Department of Applied Artificial Intelligence Sungkyunkwan UniversitySeoul03063Korea Raon Data Seoul03073Korea Department of Computer Science and Engineering University of MinnesotaMinneapolis55455MNUSA

In the field of natural language processing(NLP),the advancement of neural machine translation has paved the way for cross-lingual ***,most studies in NLP have evaluated the proposed language models on well-refined *** investigatewhether amachine translation approach is suitable for multilingual analysis of unrefined datasets,particularly,chat messages in *** order to address it,we collected the dataset,which included 7,066,854 and 3,365,569 chat messages from English and Korean streams,*** employed several machine learning classifiers and neural networks with two different types of embedding:word-sequence embedding and the final layer of a pre-trained language *** results of the employed models indicate that the accuracy difference between English,and English to Korean was relatively high,ranging from 3%to 12%.For Korean data(Korean,and Korean to English),it ranged from 0%to 2%.Therefore,the results imply that translation from a low-resource language(e.g.,Korean)into a high-resource language(e.g.,English)shows higher performance,in contrast to vice *** implications and limitations of the presented results are also *** instance,we suggest the feasibility of translation from resource-poor languages for using the tools of resource-rich languages in further analysis.

关键词： Twitch multilingual machine translation machine learning

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Research Challenges and Future Facet of Cellular Computing 2

Research Challenges and Future Facet of Cellular Computing

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Alnawayseh, Saif E. A. Khan, Tahir Abbas Farooq, Umer Zulfiqar, Sidra Khan, Shan Al-Kassem, Amer Hani Mutah Univesity Faculty of Engineering Electrical Engineering Department Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan LGU Department of Computer Science Lahore Pakistan College of Business Management Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

cellular computing is promising and exciting domain. As the field continues to evolve, several future facets are expected to emerge. One potential facet is the development of more sophisticated and efficient cellular-based computing systems that can perform complex calculations and data processing tasks. This could lead to the creation of new tools and technologies in various fields, including medicine, biotechnology, and environmental monitoring. Moreover, technology has changed the way of communication, sharing and exchanging data. Mobile computing has grown to be very vital as the number of cellular devices have elevated fairly in the preceding years. In this paper, we have mentioned cellular computing, aspects of cell computing, working of cell computing. Also, we have discussed that what challenges are most common in the development of cellular computing. In last we discussed merits, demerits, application, and its future. © 2023 IEEE.

关键词： Mobile computing

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A Deep Learning-based 6DoF Video Synthesizing Method Using Instant-NGPs

A Deep Learning-based 6DoF Video Synthesizing Method Using I...

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2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023

作者： Choi, Jaeyeol Jeong, Jong-Beom Park, Junhyeong Ryu, Eun-Seok Department of Applied Artificial Intelligence Seoul Korea Republic of Department of Computer Science Education Seoul Korea Republic of Department of Immersive Media Engineering Seoul Korea Republic of

ISBN: (纸本)9798350359855

This paper introduces a new method for synthesizing six degree of freedom (6DoF) videos using neural radiance fields, which allows training from plain 2D images to render 3D scene at arbitrary viewports. Neural network model representing a previous timepoint is fine-Tuned to train models for subsequent timepoints. Additionally, instant neural graphics primitives (Instant-NGP) is applied for speed improvement. The proposed method achieved both improved objective quality for same number of training iterations and enhanced consistency between frames. Furthermore, it shows superiority over other methods for generating 6DoF videos, in terms of quality and time efficiency. © 2023 IEEE.

关键词： Virtual reality

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Real-Time Enhanced GAN-Powered Intrusion Prevention System: Safeguarding Networks with Advanced AI 2

Real-Time Enhanced GAN-Powered Intrusion Prevention System: ...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Sutherlin Subitha, G. Seldev Christopher, C. Wilfred Blessing, N.R. Sheeja Kumari, V. Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Nagercoil India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Saveetha School of Engineering Simats Univeristy Department of Computational Intellegence Chennai600025 India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

In an era of escalating cyber threats and evolving networks, the demand for robust intrusion prevention is paramount. Introducing the Real-Time Enhanced GAN-Powered Intrusion Prevention System (RT-EGAN-IPS), a state-of-the-art solution fortified with advanced AI. RT-EGAN-IPS harnesses the power of Generative Adversarial Networks (GANs) to revolutionize real-time intrusion detection. GANs, renowned for their data synthesis capabilities, excel at uncovering subtle anomalies and emerging threats within network data. Its real-time adaptability enables it to swiftly identify novel attack patterns by continuously learning from incoming network data. Deep reinforcement learning and predictive analytics empower RT-EGAN-IPS to not only detect intrusions but autonomously respond, bolstering network protection. Versatile in handling multi-modal data streams, from text logs to multimedia content, RT-EGAN-IPS thrives on a collaborative approach. Security experts collaborate with the system for real-time validation and feedback. Dynamic thresholding ensures responsiveness to evolving threats, supported by low-latency responses and an ensemble approach. This abstract unveils RT-EGAN-IPS, a holistic AI-driven network security paradigm, promising a new level of protection in the ever-changing realm of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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Characterization of Vertical Wind Speed Profiles Based on Ward's Agglomerative Clustering Algorithm

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1437-1449页

作者： M.C.Bueso A.Molina-García A.P.Ramallo-González A.Fernández-Guillamón Department of Applied Mathematics and Statistics Universidad Politécnica de Cartagena30202 CartagenaSpain Department of Automatics Electrical Engineering and Electronic TechnologyUniversidad Politécnica de Cartagena30202 CartagenaSpain Department of Information and Communications Engineering Computer Science FacultyUniversity of Murcia30100 MurciaSpain Universidad de Castilla La-Mancha Campus Universitario02071 AlbaceteSpain

Wind turbine blades have been constantly increasing since wind energy becomes a popular renewable energy source to generate electricity. Therefore, the wind sector requires a more efficient and representative characterization of vertical wind speed profiles to assess the potential for a wind power plant site. This paper proposes an alternative characterization of vertical wind speed profiles based on Ward's agglomerative clustering algorithm, including both wind speed module and direction data. This approach gives a more accurate incoming wind speed variation around the rotor swept area, and subsequently, provides a more realistic and complete wind speed vector characterization for vertical profiles. Real wind database collected for 2018 in the Forschungsplattformen in Nordund Ostsee(FINO) research platform is used to assess the methodology. A preliminary pre-processing stage is proposed to select the appropriated number of heights and remove missing or incomplete data. Finally, two locations and four heights are selected, and 561588 wind data are characterized. Results and discussion are also included in this paper. The methodology can be applied to other wind database and locations to characterize vertical wind speed profiles and identify the most likely wind data vector patterns.

关键词： Clustering algorithm wind speed data management power generation

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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computer Modeling in engineering & sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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Enhanced Security in Payment Gateways Through Face Detection: An Advanced Approach Using DenseNet 121- BiLSTM Models 1

Enhanced Security in Payment Gateways Through Face Detection...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Gowsic, K. Sowmiya, M. Nirmala Devi, M. Chauhan, Amit Arul Mary Rexy, V. Singh, Dharmendra Department of Computer Science and Engineering Namakkal India Department of Life Sciences Karnataka Bengaluru India St. Martin's Engineering College Department of English Secunderabad India Saveetha College of Liberal Arts and Sciences SIMATS Department of Commerce Kuthambakkam India School of Management Sciences Department of Applied Science Lucknow India

ISBN: (纸本)9798350371802

Because it is one of the most promising applications of image analysis, face recognition has been the subject of intense research and development for many decades. Many modern identification and verification requirements have found a potential new home with the introduction of face recognition (FR) technology. Facial recognition is just one of numerous uses for biometric pattern recognition algorithms. Sequencing is essential for many tasks, including as feature extraction, model training, and preprocessing. Eliminating background noise and obtaining dense vertical edges are part of the preprocessing procedures. Facial feature extraction will be employed to extract features after feature extraction. Use attributes cautiously when training a Desnet121-BiLSTM model. In every respect, the suggested method outperforms two state-of-the-art algorithms, Desnet121 and BiLSTM. An accuracy rating of 97.19% was indicative of a considerable improvement in the figures. © 2024 IEEE.

关键词： Face recognition

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