Soft Computing for Security Applications : Proceedings of ICSCS 2021

• Autore: Ranganathan G
• Pubblicazione: Singapore : , : Springer Singapore Pte. Limited, , 2021; ©2022
• Descrizione fisica: 1 online resource (944 pages)
• Soggetto genere / forma: Electronic books.
• Altri autori: FernandoXavier ShiFuqian El AlliouiYoussouf
• Nota di contenuto: Intro -- Preface -- Acknowledgements -- Contents -- About the Editors -- Facial Image Denoising and Reconstruction -- 1 Introduction -- 2 Related Work -- 2.1 Image Denoising -- 2.2 Image Outpainting -- 3 Dataset -- 3.1 Description -- 3.2 Preprocessing -- 4 Methodology -- 4.1 Image Denoising -- 4.2 Image Reconstruction -- 5 Metrics -- 6 Model Architecture -- 6.1 Image Denoising -- 6.2 Image Reconstruction -- 7 Results -- 7.1 Image Denoising -- 8 Conclusion -- 9 Future Works -- References -- One Method for RC5 Algorithm's Cryptographic Strength Improving -- 1 Introduction -- 2 Priming the Modification to the RC5 Algorithm -- 2.1 Nonlinear Functions to Improve RC5 Cryptocurrency -- 2.2 Obtained Results of Improved RC5 Algorithm Operation -- 3 Cryptoresistance Evaluation of the RC5 Algorithm Improving -- 3.1 Cryptanalytic Stability Evaluation -- 3.2 Determination of Additional Bits When Encoding in a Crypto Algorithm -- 3.3 Simple Search Cryptanalysis (Brute Force) -- 4 Conclusion -- References -- Multiclass Classification of Firewall Log Files Using Shallow Neural Network for Network Security Applications -- 1 Introduction -- 2 Dataset of Internet Firewall -- 3 Action Classification System Internet Firewall -- 4 Development and Experimental Environment -- 5 Results and Discussion -- 6 Conclusions -- References -- On the Possibility of Evasion Attacks with Macro Malware -- 1 Introduction -- 2 Related Work -- 2.1 Detection with Machine Learning -- 2.2 Evasion for Machine Learning-Based Detection -- 2.3 Evasion for Natural Languages -- 3 Background -- 3.1 Bag of Words (BoW) -- 3.2 Latent Semantic Indexing (LSI) -- 4 Proposed Method -- 4.1 Outline -- 4.2 Training Process -- 4.3 Evasion Process -- 4.4 Replacing Method -- 4.5 Inserting Method -- 4.6 Implementation -- 5 Experiment -- 5.1 Dataset -- 5.2 Outline -- 5.3 The Effect of the Replacing Method. ; 5.4 The Effect of the Simple Inserting Method -- 5.5 The Effect of the Modified Inserting Method -- 5.6 Optimizing the Number of LSI Topics -- 6 Discussion -- 6.1 The Risk of Evasion Attacks with VBA Malware -- 6.2 Resilience to the Attacks -- 6.3 Countermeasure Against Evasion Attacks -- 6.4 Limitation of This Study -- 6.5 Ethics of This Study -- 7 Conclusion -- References -- IoT-Based Smart Crop Field Monitoring and Protection System from Heavy Rainfall Utilizing Raspberry Pi -- 1 Introduction -- 2 Literature Survey -- 3 Existing System -- 4 Proposed System -- 5 Results and Discussion -- 6 Conclusion -- References -- A Systematic Review of Adoption of Blockchain and Machine Learning Technology and Its Application -- 1 Introduction -- 2 Research Methodology -- 2.1 Definition of Research Questions -- 2.2 Descriptive Analysis -- 2.3 Result -- 3 Blockchain Application -- 3.1 Blockchain Impact on Various Sectors -- 3.2 Ongoing Projects on Blockchain Technology -- 4 Conclusion -- References -- Review of Malicious URL Detection Using Machine Learning -- 1 Introduction -- 2 Background Study -- 3 Related Works -- 4 Observed Issues -- 5 Research Challenges -- 6 Conclusion -- References -- Securing IoT Using Artificial Intelligence and Feature Engineering -- 1 Introduction -- 2 Related Work -- 3 Botnet and Types of IoT botnet -- 4 Dataset -- 5 Proposed Methodology -- 6 Results and Discussion -- 7 Conclusion -- References -- A Novel Framework for NIDS Using Stacked Ensemble Learning -- 1 Introduction -- 2 Literature Review -- 3 Methods -- 3.1 Framework -- 3.2 Stacking Method -- 4 Experimental Setup -- 4.1 Dataset Description -- 4.2 Performance Metrics -- 5 Results and Discussion -- 6 Conclusion -- References -- Butterfly Algorithm Boosted Deep Random Vector Functional Link Network for Keystroke Dynamics -- 1 Introduction -- 2 Related Work -- 3 Background. ; 3.1 Keystroke Vector -- 3.2 Artificial Neural Network -- 3.3 Multilayer Perceptron -- 3.4 Deep Neural Network -- 4 Methodology: Butterfly Optimization Boosted Deep Random Vector Functional Link Network for Prediction of Continuous Keystroke Dynamic Authentication -- 4.1 Overall Architecture -- 4.2 Random Vector Functional Link Network (RVFLN) -- 4.3 Deep Random Vector Functional Link Network -- 4.4 Butterfly Optimization Algorithm (BOA) -- 5 Results and Discussions -- 6 Conclusion -- References -- Hybrid Context-Aware Recommendation System Using Deep Autoencoder -- 1 Introduction -- 2 Preliminaries -- 2.1 Collaborative Filtering -- 2.2 Deep Autoencoders -- 3 Related Works -- 4 Model -- 5 Experiments and Results -- 5.1 Dataset -- 5.2 Experimental Setup -- 5.3 Side Information -- 5.4 Impact of Contextual Information -- 5.5 Impact of Different Activation Function and Increasing the No. of Layers -- 5.6 Impact of Different Activation Type and no. Of Layers -- 6 General Results -- 7 Conclusion -- References -- Exploration and Implementation of RSA-KEM Algorithm -- 1 Introduction -- 2 RSA (Rivest, Shamir, Adleman) Algorithm -- 2.1 Algorithm -- 2.2 Primality Testing of a Number -- 3 RSA-KEM (Rivest, Shamir, Adleman-Key Encapsulation Mechanism) Algorithm -- 3.1 Algorithm -- 3.2 Password-Based Key Derivation Function (PBKDF) -- 3.3 Advanced Encryption Standard (AES) -- 4 Results -- 5 Conclusions -- References -- Botnet Attack Detection Using Machine Learning Algorithm Integrated With Ensemble Algorithm -- 1 Introduction -- 2 Review of the Literature -- 3 Proposed Botnet Attack Detectıon Method -- 3.1 Machine Learning Model -- 3.2 Ensemble Classifier Model -- 4 Result and Analysis -- 5 Conclusion -- References -- Artificial Intelligence-Based Automation System for Health Care Applications: Medbot -- 1 Introduction -- 2 Experimental Workflow Analysis. ; 3 Module Description -- 3.1 Data Collection -- 3.2 Preprocessing -- 3.3 Intent Identification -- 3.4 Response Selection -- 3.5 Suggestions and Dialogue Management -- 4 Implementation and Result -- 4.1 Validation Process -- 5 Conclusion -- References -- An Approach for Maintaining Safety at Work Places During the COVID-19 Pandemic Using Deep Learning and Contact Tracing Applications -- 1 Introduction -- 2 Literature Survey -- 3 System Description -- 3.1 Data Collection -- 3.2 Classification Model: YOLOv3 Algorithm -- 3.3 Web site and Contact Tracing Application Along with Multipurpose Wearable -- 4 Results and Analysis -- 5 Conclusion -- References -- Online Certificate Generation and Verification Using Blockchain Framework -- 1 Introduction -- 2 Preliminary Concept -- 2.1 Blockchain -- 2.2 Ethereum -- 2.3 SVG-Scalar Vector Graphics -- 3 Literature Survey -- 3.1 Shanmuga Priya R, Swetha N ``Online Certificate Validation Using Blockchain'' ch16b6 -- 3.2 Nitin Kumavat, Swapnil Mengade, Dishant Desai, JesalVarolia ``Certificate Verification System Using Blockchain'' ch16b7 -- 4 Proposed System Architecture -- 4.1 Methodology -- 4.2 Certificate Generation -- 4.3 Certificate Validation -- 4.4 Working of Application -- 5 Conclusion -- References -- A Survey on LiDAR-Based SLAM Technique for an Autonomous Model Using Particle Filters -- 1 Introduction -- 2 Overview of Different Slam Techniques -- 2.1 EKF-Based SLAM -- 2.2 GridMap-Based SLAM -- 2.3 Graph-Based SLAM -- 2.4 Comparison Between Different SLAM Techniques -- 3 Implementation of RBPF-Based SLAM with LiDAR -- 3.1 Hardware Used -- 3.2 Software Used -- 4 Results -- 4.1 Maps-From the Simulation -- 4.2 Maps from 2D LiDAR Scan Data -- 5 Conclusion -- 6 Limitations and Discussion -- References -- TrafficNN: CNN-Based Road Traffic Conditions Classification -- 1 Introduction -- 2 Related Works. ; 3 Convolutional Neural Network (CNN) -- 3.1 Convolutional Layer -- 3.2 Pooling Layer -- 3.3 Flatten Layer -- 3.4 Fully Connected Layer -- 3.5 Padding -- 3.6 Rectified Linear Units (ReLU) -- 3.7 Batch Normalization -- 3.8 Dropout -- 3.9 Softmax -- 4 Proposed Methodology -- 4.1 Data Collection -- 4.2 Data Augmentation -- 4.3 Data Preprocessing -- 4.4 TrafficNN Architecture -- 5 Performance Evaluation -- 5.1 Training the Model -- 5.2 Result Discussion -- 5.3 Comparison Using Transfer Learning -- 6 Future Work -- 7 Conclusions -- References -- Review of Malware Detection Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Deep Learning Over Machine Learning -- 4 Discussion -- 5 Future Work -- 6 Conclusion -- References -- ANN Based Hybrid RSSI-TDOA DOA Estimation and Beamforming -- 1 Introduction -- 2 Problem Formulation -- 3 System Model for DOA Estimation -- 3.1 ANN Optimized Hybrid RSSI-TDOA Model Based DOA Estimation -- 4 System Model for Beamforming -- 4.1 Recursive Least Squares (RLS) -- 5 Hardware Platform -- 5.1 Antenna -- 5.2 Eight Element Uniform Linear Array (ULA) -- 5.3 System Configuration -- 6 Results and Discussion -- 7 Conclusion -- References -- FPGA Implementation of Nested Binary Phase Codes Using DDS Approach for Radar Pulse Compression -- 1 Introduction -- 2 Binary Phase Coded Waveforms -- 2.1 Nested Barker Codes -- 3 Generation of a Barker and Nested Barker Codes Using FPGA -- 3.1 Direct Digital Synthesizers (DDS) -- 3.2 The Proposed Method to Generate Barker Codes Using DDS -- 3.3 Generation and Implementation of 13-Bit Barker Code Using FPGA -- 3.4 Generation and Implementation of Nested Barker Code Using FPGA -- 4 Conclusion -- References -- Cloud Computing-Based Li-Ion Battery-BMS Design for Constant DC Load Applications -- 1 Introduction -- 2 Literature Survey -- 3 Experimental Setup -- 4 Results and Discussion. ; 4.1 Charging Cycle.
• Titolo autorizzato: Soft Computing for Security Applications
• ISBN: 981-16-5301-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910506400903321
• Lo trovi qui: Univ. Federico II
• Serie: Advances in Intelligent Systems and Computing Ser.