標(biāo)題: Titlebook: Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities; Sanjay Misra,Amit Kumar Tyagi Book 202 [打印本頁(yè)] 作者: Embolism 時(shí)間: 2025-3-21 16:08
書(shū)目名稱Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities影響因子(影響力)
書(shū)目名稱Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities影響因子(影響力)學(xué)科排名
書(shū)目名稱Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities被引頻次
書(shū)目名稱Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities被引頻次學(xué)科排名
書(shū)目名稱Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities年度引用
書(shū)目名稱Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities年度引用學(xué)科排名
書(shū)目名稱Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities讀者反饋
書(shū)目名稱Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities讀者反饋學(xué)科排名
作者: Inscrutable 時(shí)間: 2025-3-21 21:35 作者: Insensate 時(shí)間: 2025-3-22 02:09
https://doi.org/10.1007/978-3-031-34013-0mages are then matched (one-to-many) against feature points extracted from real fingerprint images using the BOZORTH3 algorithm. This is done in order to appraise the GANs ability to generate data points with features that generalize beyond the precise domain of the training datasets features. The r作者: Abnormal 時(shí)間: 2025-3-22 06:37
,Epilogue: “Becomes a Student”,ing the good classification of the sampled data; the prediction rate is 94.2% for the CRQ-J48. The ROC curve for the na?ve Bayes classifier performs its best when the data is at 80% and performs worse when at 10%. Similarly, the J48 performs best when the data is at 85–90% and worse between 5 and 10作者: Flat-Feet 時(shí)間: 2025-3-22 10:42 作者: 吹氣 時(shí)間: 2025-3-22 14:12 作者: 解開(kāi) 時(shí)間: 2025-3-22 19:08 作者: 陪審團(tuán) 時(shí)間: 2025-3-22 21:12
The Nature of Galaxies and Galaxy Clustersgree and mutual friend attack. (2) Differential privacy technique for node degree publishing. This chapter includes detailed discussion of these privacy preservation techniques. The proposed clustering approach is based on Machine Learning concepts. The graph topological properties are considered as作者: 持續(xù) 時(shí)間: 2025-3-23 03:19
The Nature of Galaxies and Galaxy Clusterss achieved through heatmaps, scroll maps, attention maps, and keyloggers. Then the data has been converted into text and it is stored in the database. The obtained data has been obfuscated and given for futher process. This is achieved through an AI tool called Delphix. Also this chapter gives an ov作者: 積極詞匯 時(shí)間: 2025-3-23 06:58 作者: Negligible 時(shí)間: 2025-3-23 10:39
https://doi.org/10.1007/978-3-658-07593-4 therefore possible to provide a full-bodied . method to assist in threat identification and evaluation, activity prediction, mitigation, and response strategies. Using . procedures, one may identify patterns and matches in the activity of threat players, that combined with the issues of . and . giv作者: altruism 時(shí)間: 2025-3-23 17:24 作者: 平息 時(shí)間: 2025-3-23 21:07
Qingyun Jiang,Lixian Qian,Min Dingn programming language. The relevant features selected were then fed into the LSTM-RNN for classification. The results obtained were compared with past work and our fraud model recorded high classification accuracy as well as reduced false alarm rate. It has 99.58% Prediction Accuracy, 99.6% Precisi作者: 預(yù)定 時(shí)間: 2025-3-23 23:47 作者: 寬大 時(shí)間: 2025-3-24 03:04 作者: 故意釣到白楊 時(shí)間: 2025-3-24 07:43 作者: 抵消 時(shí)間: 2025-3-24 12:15 作者: fulmination 時(shí)間: 2025-3-24 16:17 作者: Pathogen 時(shí)間: 2025-3-24 19:31 作者: MAIZE 時(shí)間: 2025-3-25 02:34 作者: Inexorable 時(shí)間: 2025-3-25 03:43
Biometric E-Voting System for Cybersecurity,res of the system and the inherent cost of implementation were estimated adequately. This will guide Independent National Electoral Commission (INEC) in appropriating the allocation of resources. There is also provision for capital and content insurance cover against the fear of manipulation by elec作者: cataract 時(shí)間: 2025-3-25 10:11
Wrapper Based Approach for Network Intrusion Detection Model with Combination of Dual Filtering Tection accuracy of the minority class under the assumption that the overall distribution is unchanged and the information loss of majority samples. Three different decision tree classifiers were used to compute the performance of the selected subset features. Remarkable and outstanding fair comparison作者: Aggregate 時(shí)間: 2025-3-25 13:20 作者: CAPE 時(shí)間: 2025-3-25 18:52 作者: 完全 時(shí)間: 2025-3-25 20:39
An Obfuscation Technique for Malware Detection and Protection in Sandboxing,s achieved through heatmaps, scroll maps, attention maps, and keyloggers. Then the data has been converted into text and it is stored in the database. The obtained data has been obfuscated and given for futher process. This is achieved through an AI tool called Delphix. Also this chapter gives an ov作者: AMOR 時(shí)間: 2025-3-26 00:10
Secure Data Sharing with Interplanetary File System for Pharmaceutical Data,raphic Smart Contracts for Device Authentication and User Access control scheme are written in Solidity Language and executed on Remix IDE. Transaction and Execution cost are evaluated for Advanced Encryption Standard (AES) algorithm and Rivest-Shamir-Adelman (RSA) algorithm in terms of ether as gas作者: Meditative 時(shí)間: 2025-3-26 08:22 作者: 切碎 時(shí)間: 2025-3-26 09:54
Description of a Network Attack Ontology Presented Formally,tégé, an ontology editor. The Attack Scenario class, a core class of the ontology, represents types of network attacks, for example, a Denial of Service attack. The ontology is evaluated by showing three examples of real attacks that are correctly classified by the presented ontology.作者: Binge-Drinking 時(shí)間: 2025-3-26 16:36
A Long Short Term Memory Model for Credit Card Fraud Detection,n programming language. The relevant features selected were then fed into the LSTM-RNN for classification. The results obtained were compared with past work and our fraud model recorded high classification accuracy as well as reduced false alarm rate. It has 99.58% Prediction Accuracy, 99.6% Precisi作者: OGLE 時(shí)間: 2025-3-26 19:09 作者: Accord 時(shí)間: 2025-3-26 22:51
Machine Learning Algorithm for Cryptocurrencies Price Prediction,ey factors used are available price, close price, high price, low price, volume and market cap with the interdependencies amid some cryptocurrencies thus centers on measuring vital features that influence the trade’s unpredictability by applying the model to increase the effectiveness of the process作者: 不幸的人 時(shí)間: 2025-3-27 02:06
,Metaheuristic Techniques in Attack and Defense Strategies for Cybersecurity: A?Systematic Review,zation in the detection of threats is based on the reduction of the features in the training stage. Metaheuristics play a key role in reducing these features. Our research concludes that researchers must reduce the training stage in order to decrease processing requirements and get closer to real ti作者: staging 時(shí)間: 2025-3-27 09:03
Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities作者: Expostulate 時(shí)間: 2025-3-27 12:50 作者: 拔出 時(shí)間: 2025-3-27 13:50 作者: 解凍 時(shí)間: 2025-3-27 20:23
Leila Jancovich,David Stevenson validated using incoming live network packets for Denial of Service (DoS) type of attack. The experimental results indicate that Na?ve Bayesian yields better accuracy of 88% when compared to the SVM model.作者: overbearing 時(shí)間: 2025-3-28 01:15
Proactive Network Packet Classification Using Artificial Intelligence, validated using incoming live network packets for Denial of Service (DoS) type of attack. The experimental results indicate that Na?ve Bayesian yields better accuracy of 88% when compared to the SVM model.作者: 生意行為 時(shí)間: 2025-3-28 05:36 作者: 漂白 時(shí)間: 2025-3-28 10:06
Book 2021al analytics tools), and an application-oriented approach that can be demonstrated with respect to data analytics using artificial intelligence to make systems stronger (i.e., impossible to breach). We can see many serious cyber breaches on Government databases or public profiles at online social ne作者: ectropion 時(shí)間: 2025-3-28 12:15 作者: 具體 時(shí)間: 2025-3-28 18:05
Blockchain-Enabled Verification System,vices are utilized through a proxy contract that handles all calls directed to it. We believe that this system is a gateway towards secure and transparent verification that, in the FIN4 setting, has the potential of creating a positive impact in the world.作者: 揭穿真相 時(shí)間: 2025-3-28 20:16
Sanjay Misra,Amit Kumar TyagiPresents recent research on Artificial Intelligence for Cyber Security.Conducts analyses, implementation, and discussion of many tools of Artificial Intelligence, Machine Learning, Deep Learning, and 作者: 縮短 時(shí)間: 2025-3-29 02:09 作者: 禁止 時(shí)間: 2025-3-29 05:37 作者: 紅潤(rùn) 時(shí)間: 2025-3-29 09:13 作者: ineluctable 時(shí)間: 2025-3-29 12:59
Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities978-3-030-72236-4Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: Medicare 時(shí)間: 2025-3-29 19:04 作者: 連詞 時(shí)間: 2025-3-29 23:19
Failure to Thrive and Malnutritionide predictions between ham and spam mails. These algorithms predict a class based on the probability estimated from the features of the particular class label. The classifier performance gets degraded if there are large number of features with complex distributions and limited data instances. This 作者: 不理會(huì) 時(shí)間: 2025-3-30 02:00
https://doi.org/10.1007/978-3-031-34013-0ting realistic fake biometric data that may mitigate some of the privacy concerns with using real biometric data. This paper trains a Deep Convolutional Generative Adversarial Network (DCGAN) to generate fake biometric fingerprint images. The quality of the generated images is measured using the NFI作者: 樹(shù)木心 時(shí)間: 2025-3-30 04:42 作者: 不溶解 時(shí)間: 2025-3-30 12:10 作者: dowagers-hump 時(shí)間: 2025-3-30 14:44 作者: 無(wú)底 時(shí)間: 2025-3-30 20:12 作者: GLUE 時(shí)間: 2025-3-31 00:26
Leila Jancovich,David Stevenson unstructured, that can be analyzed using certain algorithms and analytical techniques to handle and retrieve important possessions of such data. However, Information Security and Assurance have become increasingly important in an era in which relevant data is recognized as a key asset by many organ作者: habitat 時(shí)間: 2025-3-31 03:23 作者: Alcove 時(shí)間: 2025-3-31 07:59
