標(biāo)題: Titlebook: Malware Analysis Using Artificial Intelligence and Deep Learning; Mark Stamp,Mamoun Alazab,Andrii Shalaginov Book 2021 The Editor(s) (if a [打印本頁] 作者: 動詞 時(shí)間: 2025-3-21 16:36
書目名稱Malware Analysis Using Artificial Intelligence and Deep Learning影響因子(影響力)
書目名稱Malware Analysis Using Artificial Intelligence and Deep Learning影響因子(影響力)學(xué)科排名
書目名稱Malware Analysis Using Artificial Intelligence and Deep Learning網(wǎng)絡(luò)公開度
書目名稱Malware Analysis Using Artificial Intelligence and Deep Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Malware Analysis Using Artificial Intelligence and Deep Learning被引頻次
書目名稱Malware Analysis Using Artificial Intelligence and Deep Learning被引頻次學(xué)科排名
書目名稱Malware Analysis Using Artificial Intelligence and Deep Learning年度引用
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書目名稱Malware Analysis Using Artificial Intelligence and Deep Learning讀者反饋
書目名稱Malware Analysis Using Artificial Intelligence and Deep Learning讀者反饋學(xué)科排名
作者: EXUDE 時(shí)間: 2025-3-21 20:26
Malware Detection with Sequence-Based Machine Learning and Deep Learningatatypes extracted from code: static features and dynamic traces of program execution. We review recent research that applies machine learning on opcode and API call sequences, call graphs, system calls, registry changes, information flow traces, as well as hybrid and raw data, to detect and classif作者: 狂亂 時(shí)間: 2025-3-22 04:24 作者: braggadocio 時(shí)間: 2025-3-22 04:42 作者: Implicit 時(shí)間: 2025-3-22 12:36
A Survey of Intelligent Techniques for Android Malware Detectionted with the network and provide the online functionality and services available with the lowest cost. In this context, the Android operating system (OS) is very popular due to its openness. It has major stakeholder in the smart devices but has also become an attractive target for cyber-criminals. T作者: 嚴(yán)厲批評 時(shí)間: 2025-3-22 15:58 作者: 按時(shí)間順序 時(shí)間: 2025-3-22 20:50
Review of Artificial Intelligence Cyber Threat Assessment Techniques for Increased System Survivabilrs of such systems. The notion of survivability in the context of cybersecurity over multi-user distributed information systems is defined, which is set as the target of cyber defense to prevent the adversary from successfully completing their mission. The cyber-attackers’ kill chain is explained. A作者: 憤怒事實(shí) 時(shí)間: 2025-3-22 23:54 作者: 分貝 時(shí)間: 2025-3-23 03:16 作者: Radiculopathy 時(shí)間: 2025-3-23 06:38 作者: NEG 時(shí)間: 2025-3-23 11:27
Word Embedding Techniques for Malware Evolution Detectionive malware detection, it is necessary to determine when malware evolution has occurred so that appropriate countermeasures can be taken. We perform a variety of experiments aimed at detecting points in time where a malware family has likely evolved, and we consider secondary tests designed to confi作者: CROW 時(shí)間: 2025-3-23 17:25 作者: stratum-corneum 時(shí)間: 2025-3-23 18:10
Cluster Analysis of Malware Family Relationshipsies with?1000 samples per family. These families can be categorized into seven different types of malware. We perform clustering based on pairs of families and use the results to determine relationships between families. We perform a similar cluster analysis based on malware type. Our results indica作者: gene-therapy 時(shí)間: 2025-3-24 01:32 作者: 明智的人 時(shí)間: 2025-3-24 05:59 作者: cipher 時(shí)間: 2025-3-24 09:33
A Novel Study on Multinomial Classification of x86/x64 Linux ELF Malware Types and Families Through OS). Several factors caused this, including unobstructed installation of third-party software. Unix-like OS is considerably less susceptible to malware infections. However, there are still a few examples of successful malicious software. The challenge is that there are not that many software tools a作者: 吝嗇性 時(shí)間: 2025-3-24 12:07
tional scientists..Also available online in www.springermateThe Landolt-B?rnstein Volume 27 deals with the magnetic properties of non-metallic inorganic compounds based on transition elements, such as there are pnictides, chalcogenides, oxides, halides, borates, and finally phosphates and silicates,作者: 雜役 時(shí)間: 2025-3-24 15:13 作者: 鈍劍 時(shí)間: 2025-3-24 19:05 作者: Bouquet 時(shí)間: 2025-3-25 00:01 作者: 熱情的我 時(shí)間: 2025-3-25 04:45 作者: LAP 時(shí)間: 2025-3-25 09:17 作者: Scintillations 時(shí)間: 2025-3-25 14:41
Rajesh Kumars,Mamoun Alazab,WenYong Wangtional scientists..Also available online in www.springermateThe Landolt-B?rnstein Volume 27 deals with the magnetic properties of non-metallic inorganic compounds based on transition elements, such as there are pnictides, chalcogenides, oxides, halides, borates, and finally phosphates and silicates,作者: 完成才能戰(zhàn)勝 時(shí)間: 2025-3-25 17:26
everal subvolumes I1, I2, etc. . - In each chapter the different groups of minerals and synthetic silicates were distinctly analyzed in various sections. For each group, additional silicate minerals, more recen978-3-540-71211-4Series ISSN 1615-1844 Series E-ISSN 1616-9522 作者: ingestion 時(shí)間: 2025-3-25 22:15
Mark Stampeveral subvolumes I1, I2, etc. . - In each chapter the different groups of minerals and synthetic silicates were distinctly analyzed in various sections. For each group, additional silicate minerals, more recen978-3-540-71211-4Series ISSN 1615-1844 Series E-ISSN 1616-9522 作者: Neuropeptides 時(shí)間: 2025-3-26 00:08
William B. Andreopouloseveral subvolumes I1, I2, etc. . - In each chapter the different groups of minerals and synthetic silicates were distinctly analyzed in various sections. For each group, additional silicate minerals, more recen978-3-540-71211-4Series ISSN 1615-1844 Series E-ISSN 1616-9522 作者: 討人喜歡 時(shí)間: 2025-3-26 08:15
Aiman Al-Sabaawi,Khamael Al-Dulaimi,Ernest Foo,Mamoun Alazabeveral subvolumes I1, I2, etc. . - In each chapter the different groups of minerals and synthetic silicates were distinctly analyzed in various sections. For each group, additional silicate minerals, more recen978-3-540-71211-4Series ISSN 1615-1844 Series E-ISSN 1616-9522 作者: 改革運(yùn)動 時(shí)間: 2025-3-26 11:04
Rajesh Kumars,Mamoun Alazab,WenYong Wangeveral subvolumes I1, I2, etc. . - In each chapter the different groups of minerals and synthetic silicates were distinctly analyzed in various sections. For each group, additional silicate minerals, more recen978-3-540-71211-4Series ISSN 1615-1844 Series E-ISSN 1616-9522 作者: 壯麗的去 時(shí)間: 2025-3-26 14:50
Nikolaos Doukas,Peter Stavroulakis,Nikolaos Bardis作者: Chagrin 時(shí)間: 2025-3-26 18:10
Andrew McDole,Maanak Gupta,Mahmoud Abdelsalam,Sudip Mittal,Mamoun Alazab作者: Mediocre 時(shí)間: 2025-3-27 00:19 作者: 羊齒 時(shí)間: 2025-3-27 03:35 作者: 形狀 時(shí)間: 2025-3-27 05:28 作者: 阻止 時(shí)間: 2025-3-27 09:43 作者: 法律的瑕疵 時(shí)間: 2025-3-27 15:30
A Survey of Intelligent Techniques for Android Malware Detectionsses the basic methodology and frameworks which classify, cluster, or extract Android malware features. (3) Exploring the dataset, harmful features, and classification results. (4) Discussing the current challenges and issues. Moreover, it discusses the most important factors, data-mining algorithms作者: somnambulism 時(shí)間: 2025-3-27 18:20 作者: 不連貫 時(shí)間: 2025-3-28 01:15 作者: 變白 時(shí)間: 2025-3-28 05:35
On Ensemble Learningframework and empirical results are an effort to bring some sense of order to the chaos that is evident in the evolving field of ensemble learning—both within the narrow confines of the malware analysis problem, and in the larger realm of machine learning in general.作者: Medicare 時(shí)間: 2025-3-28 06:19
Optimizing Multi-class Classification of Binaries Based on Static Featurespiler optimized code, and both ELF and PE-files and demonstrate methods for optimizing storage and computational complexity when classifying executable files. Our findings show that a higher size N-gram is only preferable for some code simplifications, and that some code simplifications can give a v作者: osculate 時(shí)間: 2025-3-28 13:14 作者: admission 時(shí)間: 2025-3-28 17:36 作者: 騎師 時(shí)間: 2025-3-28 19:37 作者: 山頂可休息 時(shí)間: 2025-3-29 00:22 作者: interrogate 時(shí)間: 2025-3-29 06:45
and tectosilicates. Due to the huge amount of data these chapters had to be spread over several subvolumes I1, I2, etc. . - In each chapter the different groups of minerals and synthetic silicates were distinctly analyzed in various sections. For each group, additional silicate minerals, more recen作者: Palatial 時(shí)間: 2025-3-29 08:42 作者: medium 時(shí)間: 2025-3-29 14:49 作者: 聽覺 時(shí)間: 2025-3-29 18:31 作者: 大火 時(shí)間: 2025-3-29 21:25
Malware Detection with Sequence-Based Machine Learning and Deep Learningnput formats, such as one-hot encoding and vector embeddings, the architecture of the machine learning models, the training process, and the output formats. Finally, we discuss commercial and open-source tools that are used for data extraction from software.作者: 比賽用背帶 時(shí)間: 2025-3-30 01:23 作者: 放大 時(shí)間: 2025-3-30 08:01
Book 2021sis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain know作者: JOT 時(shí)間: 2025-3-30 11:30
Book 2021ctical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases..作者: Abrade 時(shí)間: 2025-3-30 14:26
A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classificationhat we can obtain better classification accuracy based on these feature embeddings, as compared to HMM experiments that directly use the opcode sequences, and serve to establish a baseline. These results show that word embeddings can be a useful feature engineering step in the field of malware analysis.作者: Density 時(shí)間: 2025-3-30 20:33
tools, frameworks and techniques to enable readers to implem.?This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI a作者: Priapism 時(shí)間: 2025-3-30 23:57 作者: Ige326 時(shí)間: 2025-3-31 02:09
An Empirical Analysis of Image-Based Learning Techniques for Malware Classification work, the results presented in this chapter are based on a larger and more diverse malware dataset, we consider a wider array of features, and we experiment with a much greater variety of learning techniques. Consequently, our results are the most comprehensive and complete that have yet been published.作者: 乞討 時(shí)間: 2025-3-31 05:41
https://doi.org/10.1007/978-3-030-62582-5Malware identification and analysis; Intrusion detection; Computer forensics; Spam detection; Phishing d作者: sacrum 時(shí)間: 2025-3-31 10:29
Mark Stamp,Mamoun Alazab,Andrii ShalaginovExplores how deep learning and artificial intelligence can effectively be used in malware detection and analysis.Showcases state-of-the-art tools, frameworks and techniques to enable readers to implem作者: BRACE 時(shí)間: 2025-3-31 15:33 作者: MAL 時(shí)間: 2025-3-31 20:26
A Selective Survey of Deep Learning Techniques and Their Application to Malware Analysisluding multilayer perceptrons (MLP), convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), residual networks (ResNet), generative adversarial networks (GAN), and Word2Vec. We provide a selective survey of applications of each of these architectures to malware-related problems.作者: candle 時(shí)間: 2025-4-1 00:50
Deep Learning Techniques for Behavioral Malware Analysis in Cloud IaaSThis chapter focuses on online malware detection techniques in cloud IaaS using machine learning and discusses comparative analysis on the performance metrics of various deep learning models.作者: Cervical-Spine 時(shí)間: 2025-4-1 03:01
978-3-030-62584-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Gorilla 時(shí)間: 2025-4-1 06:43
lasting dominance of the founding fathers of the field. Explaining these patterns, requires both focused and explorative elements in the selective attention of citing authors in a community of researchers. The elements combining in citation dynamics are threefold: (1) fast consumption of novel contr
