標(biāo)題: Titlebook: Cyber Malware; Offensive and Defens Iman Almomani,Leandros A. Maglaras,Nick Ayres Book 2024 The Editor(s) (if applicable) and The Author(s) [打印本頁(yè)] 作者: exterminate 時(shí)間: 2025-3-21 17:51
書目名稱Cyber Malware影響因子(影響力)
書目名稱Cyber Malware影響因子(影響力)學(xué)科排名
書目名稱Cyber Malware網(wǎng)絡(luò)公開度
書目名稱Cyber Malware網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Cyber Malware被引頻次
書目名稱Cyber Malware被引頻次學(xué)科排名
書目名稱Cyber Malware年度引用
書目名稱Cyber Malware年度引用學(xué)科排名
書目名稱Cyber Malware讀者反饋
書目名稱Cyber Malware讀者反饋學(xué)科排名
作者: 啪心兒跳動(dòng) 時(shí)間: 2025-3-21 22:20 作者: TAG 時(shí)間: 2025-3-22 01:59
A Princely Pandect on Astronomyng issue due to their simple yet powerful concealment of malicious network services. This book chapter comprehensively discusses FFSNs, focusing on fast-flux architecture, operation, and characterization. Also, it provides a review of fast-flux detection mechanisms and highlights the main challenges and future research directions.作者: 多山 時(shí)間: 2025-3-22 05:58
Fast-Flux Service Networks: Architecture, Characteristics, and Detection Mechanisms,ng issue due to their simple yet powerful concealment of malicious network services. This book chapter comprehensively discusses FFSNs, focusing on fast-flux architecture, operation, and characterization. Also, it provides a review of fast-flux detection mechanisms and highlights the main challenges and future research directions.作者: Detain 時(shí)間: 2025-3-22 08:53 作者: Certainty 時(shí)間: 2025-3-22 14:28 作者: Certainty 時(shí)間: 2025-3-22 19:59
A Deep-Vision-Based Multi-class Classification System of Android Malware Apps, must be used to differentiate between benign and malware Android apps. Unfortunately, conventional malware detection and classification techniques based on traditional static- or dynamic-based machine learning (ML) algorithms are not the best choices for malware analysis applications. These traditi作者: 沖突 時(shí)間: 2025-3-22 21:37
Android Malware Detection Based on Network Analysis and Federated Learning,cation, becoming a serious threat to network security and user privacy. In addition, with a large-scale Android system deployment and the raising of privacy concerns, data heterogeneity, availability, and privacy preservation are presenting major challenges when applying traditional cloud-based and 作者: 陰險(xiǎn) 時(shí)間: 2025-3-23 03:16 作者: drusen 時(shí)間: 2025-3-23 07:01
Fast-Flux Service Networks: Architecture, Characteristics, and Detection Mechanisms,highly resilient service for their malicious servers while remaining hidden from direct access. This is achieved by configuring many botnet machines to work as proxies that relay traffic between end users and malicious servers controlled by botherders. FFSNs are becoming popular for hosting maliciou作者: CREEK 時(shí)間: 2025-3-23 13:28 作者: 終端 時(shí)間: 2025-3-23 14:55 作者: STERN 時(shí)間: 2025-3-23 21:30
Malware Analysis for IoT and Smart AI-Based Applications, require a reliable and secure communication structure. The enormous growth in wireless communication and the development of 5G brings an opportunity for many emerging IoT and smart AI-based applications. These networks have increased traffic and increased the level of complexity. It brings many new作者: 吞沒 時(shí)間: 2025-3-23 22:23 作者: scotoma 時(shí)間: 2025-3-24 04:48 作者: Cirrhosis 時(shí)間: 2025-3-24 07:29
Modeling for Prey-Predator Relation different DL algorithms for malware detection and recognition. The vision-based classification system was evaluated comprehensively using two open-source Android datasets (CICAndMal2017 and CICMalDroid2020). The binary formats of the android apps included in these datasets were first converted into作者: 花費(fèi) 時(shí)間: 2025-3-24 11:51
A Primer on Population Dynamics Modelingfectively while maintaining data privacy. Specifically, we trained a convolutional neural network model using FL-based decentralized optimization for detecting Android malware based on network behavior. Then, we evaluated the proposed training methodology with the benchmark dataset AAGM-2017 under d作者: Orgasm 時(shí)間: 2025-3-24 16:29 作者: 粗鄙的人 時(shí)間: 2025-3-24 20:38 作者: 高深莫測(cè) 時(shí)間: 2025-3-25 01:37
Book Four. On Distances and Bodies We provide a detailed taxonomy that classifies these solutions according to various criteria including the analysis task, the nature of the extracted features, the used features representation method, and the used deep learning algorithms. Furthermore, we discuss these solutions with respect to the作者: 使隔離 時(shí)間: 2025-3-25 07:09 作者: 胰臟 時(shí)間: 2025-3-25 08:29 作者: Chauvinistic 時(shí)間: 2025-3-25 14:21
https://doi.org/10.1007/978-1-4613-8214-0 (DDOS) attacks, cookie poisoning attacks, wrapping attacks, etc., where several variants of malware are responsible for most of these attacks. The malware identified includes Trojan horses, worms, backdoors, viruses, rootkits, botnets, etc. Identified detection techniques include malware detection 作者: Carcinogenesis 時(shí)間: 2025-3-25 15:53
A Deep-Vision-Based Multi-class Classification System of Android Malware Apps, different DL algorithms for malware detection and recognition. The vision-based classification system was evaluated comprehensively using two open-source Android datasets (CICAndMal2017 and CICMalDroid2020). The binary formats of the android apps included in these datasets were first converted into作者: Visual-Acuity 時(shí)間: 2025-3-25 22:56 作者: babble 時(shí)間: 2025-3-26 03:11 作者: jovial 時(shí)間: 2025-3-26 04:31 作者: Ige326 時(shí)間: 2025-3-26 10:21 作者: FIN 時(shí)間: 2025-3-26 13:21 作者: 清唱?jiǎng)?nbsp; 時(shí)間: 2025-3-26 18:52 作者: 獎(jiǎng)牌 時(shí)間: 2025-3-26 23:05
Malware Mitigation in Cloud Computing Architecture, (DDOS) attacks, cookie poisoning attacks, wrapping attacks, etc., where several variants of malware are responsible for most of these attacks. The malware identified includes Trojan horses, worms, backdoors, viruses, rootkits, botnets, etc. Identified detection techniques include malware detection 作者: neolith 時(shí)間: 2025-3-27 02:14
Modeling for Prey-Predator Relation must be used to differentiate between benign and malware Android apps. Unfortunately, conventional malware detection and classification techniques based on traditional static- or dynamic-based machine learning (ML) algorithms are not the best choices for malware analysis applications. These traditi作者: Nmda-Receptor 時(shí)間: 2025-3-27 07:59 作者: 處理 時(shí)間: 2025-3-27 09:44 作者: 無能力之人 時(shí)間: 2025-3-27 17:17
A Princely Pandect on Astronomyhighly resilient service for their malicious servers while remaining hidden from direct access. This is achieved by configuring many botnet machines to work as proxies that relay traffic between end users and malicious servers controlled by botherders. FFSNs are becoming popular for hosting maliciou作者: 倒轉(zhuǎn) 時(shí)間: 2025-3-27 18:22 作者: 單片眼鏡 時(shí)間: 2025-3-27 23:22
Book Four. On Distances and Bodiesovernments, and individuals. Thus, malware analysis and detection are of prevalent importance for security analysts in both industry and academia. Early signature-based and conventional machine learning-based solutions have shown their limits against the huge proliferation and sophistication of rece作者: xanthelasma 時(shí)間: 2025-3-28 05:10 作者: 飲料 時(shí)間: 2025-3-28 09:13 作者: justify 時(shí)間: 2025-3-28 14:23
https://doi.org/10.1007/978-1-4613-8214-0s but a vast shared resource that accommodates a large volume of users and offers dynamic access that is dependent on the demands. Despite all the benefits and potentials, cloud computing infrastructure is highly vulnerable to many security challenges, including malware. Therefore, research towards 作者: rheumatism 時(shí)間: 2025-3-28 15:29
Iman Almomani,Leandros A. Maglaras,Nick AyresPresents theoretical, technical, and practical knowledge on defending against malware attacks.Covers malware applications using machine learning algorithms, Blockchain and AI, forensics tools, and muc作者: Mangle 時(shí)間: 2025-3-28 21:16 作者: GRAZE 時(shí)間: 2025-3-29 00:58
