標(biāo)題: Titlebook: Artificial Intelligence for Cybersecurity; Mark Stamp,Corrado Aaron Visaggio,Fabio Di Troia Book 2022 The Editor(s) (if applicable) and Th [打印本頁] 作者: Hypothesis 時間: 2025-3-21 20:04
書目名稱Artificial Intelligence for Cybersecurity影響因子(影響力)
書目名稱Artificial Intelligence for Cybersecurity影響因子(影響力)學(xué)科排名
書目名稱Artificial Intelligence for Cybersecurity網(wǎng)絡(luò)公開度
書目名稱Artificial Intelligence for Cybersecurity網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Intelligence for Cybersecurity被引頻次
書目名稱Artificial Intelligence for Cybersecurity被引頻次學(xué)科排名
書目名稱Artificial Intelligence for Cybersecurity年度引用
書目名稱Artificial Intelligence for Cybersecurity年度引用學(xué)科排名
書目名稱Artificial Intelligence for Cybersecurity讀者反饋
書目名稱Artificial Intelligence for Cybersecurity讀者反饋學(xué)科排名
作者: 上釉彩 時間: 2025-3-21 22:05 作者: parasite 時間: 2025-3-22 02:47 作者: Project 時間: 2025-3-22 04:49
https://doi.org/10.1007/978-1-349-15821-8ieving state-of-the-art results in many areas, it also has drawbacks exploited by many with white-box attacks. Although the white-box scenario is possible in malware detection, the detailed structure of antivirus is often unknown. Consequently, we focused on a pure black-box setup where no informati作者: 克制 時間: 2025-3-22 10:05 作者: 猛烈責(zé)罵 時間: 2025-3-22 12:58 作者: 世俗 時間: 2025-3-22 20:28
https://doi.org/10.1007/978-3-642-35822-7ich uniquely distinguishes it from other typical malware threats. The C&C server sends commands to the botnets to execute malicious activities using common Internet protocols, such as Hypertext transfer (HTTP), and Internet Relay Chat (IRC). Since these protocols are common, detecting botnet activit作者: 陪審團(tuán) 時間: 2025-3-23 00:05
Class D Results and Simulations,r hand, the results can be hard to understand as to why a model classified a given file as malicious or benign. This paper focuses on the interpretability of machine learning models’ results using decision lists generated by two rule-based classifiers, I-REP and RIPPER. We use the EMBER dataset, whi作者: BALE 時間: 2025-3-23 03:09
Class D Results and Simulations,ntrol the propagation in mobile devices. According to Damballa’s Q4 State of Infections report, the antivirus products overlooked 70% of malware signatures within the first hour (Q4 2014 State of Infections Report. Q4 2014 state of infections report. ., accessed August 2021). This is despite the fac作者: 靈敏 時間: 2025-3-23 07:01
https://doi.org/10.1007/978-3-031-40419-1gs generated by BERT. We extract the “words” directly from the malware samples to achieve multi-class classification. In fact, the attention mechanism of a pre-trained BERT model can be used in malware classification by capturing information about the relation between each opcode and every other opc作者: 菊花 時間: 2025-3-23 12:13
Part 3: History and More Information,o that appropriate countermeasures can be undertaken. In this research, we perform a variety of experiments on a significant number of malware families to determine when malware evolution is likely to have occurred. All of the evolution detection techniques that we consider are based on machine lear作者: 嚴(yán)厲批評 時間: 2025-3-23 14:59 作者: 侵害 時間: 2025-3-23 21:28
Magdalena Stefańska,Gra?yna ?migielska techniques have relied on classical machine learning techniques, but recently, deep learning techniques have been applied this problem. Although prior research has shown that deep learning models are vulnerable to adversarial attacks, relatively little research has been done in the adversarial doma作者: 描繪 時間: 2025-3-24 00:30 作者: 針葉類的樹 時間: 2025-3-24 02:57
Mark Hudson,Ian Hudson,Mara Fridelles such as work and healthcare are evolving so as to exploit the capabilities of computers and networks. At the same time, malicious cyber activities are becoming ever more often and more destructive as criminals also exploit technological progress. In this context, the necessity for system survivab作者: 權(quán)宜之計 時間: 2025-3-24 06:59
Mark Hudson,Ian Hudson,Mara Fridelleasibility of user authentication and identification utilizing keystroke dynamics. In this research, we consider a wide variety of machine learning and deep learning techniques based on fixed-text keystroke-derived features, we optimize the resulting models, and we compare our results to those obtai作者: chronology 時間: 2025-3-24 12:27
Fair Trial and Judicial Independenceystroke dynamics can be used to analyze the way that a user types based on various keyboard input. Previous work has shown that user authentication and classification can be achieved based on keystroke dynamics. In this research, we consider the problem of user classification based on keystroke dyna作者: cathartic 時間: 2025-3-24 17:59 作者: 過時 時間: 2025-3-24 20:04 作者: Interstellar 時間: 2025-3-25 01:16 作者: 本能 時間: 2025-3-25 05:40
Mark Stamp,Corrado Aaron Visaggio,Fabio Di TroiaPresents new and novel applications for AI technology within the context of cybersecurity.Explores and conquers issues and obstacles that the AI field is tackling within the context of cybersecurity.T作者: plasma 時間: 2025-3-25 11:25
Advances in Information Securityhttp://image.papertrans.cn/b/image/162364.jpg作者: chandel 時間: 2025-3-25 15:01
https://doi.org/10.1007/978-1-349-15821-8samples. While the AC-GAN generated images often appear to be very similar to real malware images, we conclude that from a deep learning perspective, the AC-GAN generated samples do not rise to the level of deep fake malware images.作者: 小臼 時間: 2025-3-25 17:43
https://doi.org/10.1007/978-3-031-40419-1ation algorithms, we used and compared Support Vector Machines (SVM), Logistic Regression, Random Forests, and Multi-Layer Perceptron (MLP). We found that the classification accuracy obtained by the word embeddings generated by BERT is effective in detecting malware samples, and superior in accuracy when compared to the ones created by Word2Vec.作者: 滔滔不絕的人 時間: 2025-3-25 20:17 作者: Ancillary 時間: 2025-3-26 01:11
BERT for Malware Classificationation algorithms, we used and compared Support Vector Machines (SVM), Logistic Regression, Random Forests, and Multi-Layer Perceptron (MLP). We found that the classification accuracy obtained by the word embeddings generated by BERT is effective in detecting malware samples, and superior in accuracy when compared to the ones created by Word2Vec.作者: instulate 時間: 2025-3-26 08:15 作者: 搖擺 時間: 2025-3-26 11:58
Assessing the Robustness of an Image-Based Malware Classifier with Smali Level Perturbations Techniqtector and evaluate its resilience when morphed samples are considered. The experiments were conducted on 16384 real-world Android Malware, and the experimental analysis demonstrates that standard image-based malware classifiers are vulnerable to simple perturbations attacks.作者: 含水層 時間: 2025-3-26 15:41 作者: ostracize 時間: 2025-3-26 17:53 作者: 事情 時間: 2025-3-26 23:38
Fair Trial and Judicial Independencemics features collected from free-text. We implement and analyze a novel a deep learning model that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU). We optimize the resulting model and consider several relevant related problems. Our model is competitive with the best results obtained in previous comparable research.作者: 擋泥板 時間: 2025-3-27 02:42 作者: modest 時間: 2025-3-27 07:03 作者: anthesis 時間: 2025-3-27 11:13
Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamicsned in related research. We find that models based on extreme gradient boosting (XGBoost) and multi-layer perceptrons (MLP) perform well in our experiments. Our best models outperform previous comparable research.作者: 爆米花 時間: 2025-3-27 16:34
Machine Learning-Based Analysis of Free-Text Keystroke Dynamicsmics features collected from free-text. We implement and analyze a novel a deep learning model that combines a convolutional neural network (CNN) and a gated recurrent unit (GRU). We optimize the resulting model and consider several relevant related problems. Our model is competitive with the best results obtained in previous comparable research.作者: 吸氣 時間: 2025-3-27 19:42
1568-2633 e AI field is tackling within the context of cybersecurity.T.This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniqu作者: Cerumen 時間: 2025-3-27 23:54 作者: debunk 時間: 2025-3-28 03:22
Clickbait Detection for YouTube Videostly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the challenging problem of detecting clickbait YouTube videos. We experiment with multiple state-of-the-art machine learning techniques using a variety of textual features.作者: Alveoli 時間: 2025-3-28 09:14
Generation of Adversarial Malware and Benign Examples Using Reinforcement Learningieving state-of-the-art results in many areas, it also has drawbacks exploited by many with white-box attacks. Although the white-box scenario is possible in malware detection, the detailed structure of antivirus is often unknown. Consequently, we focused on a pure black-box setup where no informati作者: Arable 時間: 2025-3-28 13:10
Auxiliary-Classifier GAN for Malware Analysisd simultaneously. GANs have been used, for example, to successfully generate “deep fake” images. A recent trend in malware research consists of treating executables as images and employing image-based analysis techniques. In this research, we generate fake malware images using auxiliary classifier G作者: Contort 時間: 2025-3-28 16:35
Assessing the Robustness of an Image-Based Malware Classifier with Smali Level Perturbations Techniqf previously known threats, they are not able to catch new malware and also generalize their knowledge to different variants of the same known malware. Deep learning approaches have been adopted to address this problem, and one of the most promising attempts is based on the representation of malware作者: Phonophobia 時間: 2025-3-28 21:43
Detecting Botnets Through Deep Learning and Network Flow Analysisich uniquely distinguishes it from other typical malware threats. The C&C server sends commands to the botnets to execute malicious activities using common Internet protocols, such as Hypertext transfer (HTTP), and Internet Relay Chat (IRC). Since these protocols are common, detecting botnet activit作者: Ringworm 時間: 2025-3-28 23:01 作者: 是剝皮 時間: 2025-3-29 05:56 作者: 和平主義者 時間: 2025-3-29 07:46 作者: 毗鄰 時間: 2025-3-29 12:04
Machine Learning for Malware Evolution Detectiono that appropriate countermeasures can be undertaken. In this research, we perform a variety of experiments on a significant number of malware families to determine when malware evolution is likely to have occurred. All of the evolution detection techniques that we consider are based on machine lear作者: 兩棲動物 時間: 2025-3-29 15:48
Gambling for Success: The Lottery Ticket Hypothesis in Deep Learning-Based Side-Channel Analysisural networks that perform well for any setting. Based on the developed neural network architectures, we can distinguish between small neural networks that are easier to tune and less prone to overfitting but could have insufficient capacity to model the data. On the other hand, large neural network作者: 錯誤 時間: 2025-3-29 23:43
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authenticatio techniques have relied on classical machine learning techniques, but recently, deep learning techniques have been applied this problem. Although prior research has shown that deep learning models are vulnerable to adversarial attacks, relatively little research has been done in the adversarial doma作者: Initiative 時間: 2025-3-30 02:35
Clickbait Detection for YouTube Videosenue for the person who posted the video. This creates an incentive for people to post clickbait videos, in which the content might deviate significantly from the title, description, or thumbnail. In effect, users are tricked into clicking on clickbait videos. In this research, we consider the chall作者: Nibble 時間: 2025-3-30 05:29 作者: 河流 時間: 2025-3-30 10:49
Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamicseasibility of user authentication and identification utilizing keystroke dynamics. In this research, we consider a wide variety of machine learning and deep learning techniques based on fixed-text keystroke-derived features, we optimize the resulting models, and we compare our results to those obtai作者: 使聲音降低 時間: 2025-3-30 16:12
Machine Learning-Based Analysis of Free-Text Keystroke Dynamicsystroke dynamics can be used to analyze the way that a user types based on various keyboard input. Previous work has shown that user authentication and classification can be achieved based on keystroke dynamics. In this research, we consider the problem of user classification based on keystroke dyna
