標(biāo)題: Titlebook: Handbook of Big Data Analytics and Forensics; Kim-Kwang Raymond Choo,Ali Dehghantanha Book 2022 Springer Nature Switzerland AG 2022 cyber [打印本頁(yè)] 作者: hearken 時(shí)間: 2025-3-21 19:24
書(shū)目名稱Handbook of Big Data Analytics and Forensics影響因子(影響力)
書(shū)目名稱Handbook of Big Data Analytics and Forensics影響因子(影響力)學(xué)科排名
書(shū)目名稱Handbook of Big Data Analytics and Forensics網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Handbook of Big Data Analytics and Forensics網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Handbook of Big Data Analytics and Forensics被引頻次
書(shū)目名稱Handbook of Big Data Analytics and Forensics被引頻次學(xué)科排名
書(shū)目名稱Handbook of Big Data Analytics and Forensics年度引用
書(shū)目名稱Handbook of Big Data Analytics and Forensics年度引用學(xué)科排名
書(shū)目名稱Handbook of Big Data Analytics and Forensics讀者反饋
書(shū)目名稱Handbook of Big Data Analytics and Forensics讀者反饋學(xué)科排名
作者: 舊病復(fù)發(fā) 時(shí)間: 2025-3-21 22:17
Die Erythema multiforme-Gruppe,ontemporary approaches and techniques, including those based on machine and deep learning. A number of research challenges and opportunities are also presented in the book, which hopefully will motivate further research in this area.作者: 連系 時(shí)間: 2025-3-22 02:02 作者: Solace 時(shí)間: 2025-3-22 08:14 作者: ingestion 時(shí)間: 2025-3-22 12:03 作者: 馬賽克 時(shí)間: 2025-3-22 15:28 作者: 兩種語(yǔ)言 時(shí)間: 2025-3-22 19:13 作者: mutineer 時(shí)間: 2025-3-22 23:47
analysis of big data and IoT applications, as well as future.This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT s作者: COM 時(shí)間: 2025-3-23 01:26 作者: indenture 時(shí)間: 2025-3-23 08:10
,Erkrankungen der endokrinen Drüsen,ccuracy rate and 0% false-negative and false-positive rates. The true-positive and true-negative rates were both 100%. These results show that adaptive neural trees borrow from deep neural networks and decision trees to deliver exceptional results.作者: Inexorable 時(shí)間: 2025-3-23 13:00
https://doi.org/10.1007/978-3-662-10465-1lements a fair and scalable k-median clustering algorithm with near-linear runtime. We test our system on 4 new datasets belonging to IoT and Water distribution systems and evaluate the performance and accuracy of our results.作者: 線 時(shí)間: 2025-3-23 16:02
IoT Privacy, Security and Forensics Challenges: An Unmanned Aerial Vehicle (UAV) Case Study,These discussions raise awareness to the relationship between the challenges in forensics, security and privacy of IoT devices and offer opportunities for the development of safer and more secure technology.作者: 花束 時(shí)間: 2025-3-23 19:03 作者: 瑪瑙 時(shí)間: 2025-3-24 00:51 作者: Kernel 時(shí)間: 2025-3-24 04:09
978-3-030-74755-8Springer Nature Switzerland AG 2022作者: 要素 時(shí)間: 2025-3-24 07:50 作者: 協(xié)迫 時(shí)間: 2025-3-24 13:33 作者: narcotic 時(shí)間: 2025-3-24 17:22 作者: 小平面 時(shí)間: 2025-3-24 20:54 作者: Progesterone 時(shí)間: 2025-3-25 02:43 作者: 斷言 時(shí)間: 2025-3-25 03:29
Detection of Enumeration Attacks in Cloud Environments Using Infrastructure Log Data,ignificant number of enterprises are migrating to cloud-based environments to save costs and resources. This indicates that traditional approaches to confront cyber threats are not effective in the cloud environment. Considering the vast size and distributed nature of cloud computing systems, deep l作者: 極肥胖 時(shí)間: 2025-3-25 09:38
Cyber Threat Attribution with Multi-View Heuristic Analysis,. The APT groups are the nation-state actors or well-resourced groups that target to compromise and exploit individuals and public or private organizations. If the source of the malware can be identified at an early stage, then it will significantly help the cybersecurity specialists to know what th作者: 平常 時(shí)間: 2025-3-25 12:44 作者: Thymus 時(shí)間: 2025-3-25 17:23 作者: Repetitions 時(shí)間: 2025-3-25 22:03
Evaluating Performance of Scalable Fair Clustering Machine Learning Techniques in Detecting Cyber Ain the machine learning techniques. Over the years, many fair machine learning algorithms have been established to reduce the discrimination factor in machine learning. The fair variants of machine learning techniques such as fair clustering models provide a solution to the biased data analysis prob作者: ARBOR 時(shí)間: 2025-3-26 04:02
Fuzzy Bayesian Learning for Cyber Threat Hunting in Industrial Control Systems,usly seen data. This is most effectively accomplished by machine learning algorithms which are designed to detect abnormal activity, because a system under attack is likely to exhibit anomalous behavior. Due to the fact that anomalous behavior is not guaranteed to be caused by an attacker, false pos作者: GRE 時(shí)間: 2025-3-26 08:23
Cyber-Attack Detection in Cyber-Physical Systems Using Supervised Machine Learning,on of the computer with traditional physical infrastructure can improve the efficiency of such facility-based systems. However, it increases the scope of attack from physical security to a cybersecurity perspective. Thus, it becomes critical for authorities of such systems to be able to identify the作者: MILL 時(shí)間: 2025-3-26 10:37
Evaluation of Scalable Fair Clustering Machine Learning Methods for Threat Hunting in Cyber-Physica used, unsupervised machine learning technique to detect malware from behavior data of control systems. Clustering algorithms can be susceptible to amplifying biases that may be present in the input datasets. Recent works in fair clustering attempt to solve this problem by making them balanced with 作者: Haphazard 時(shí)間: 2025-3-26 15:50 作者: SUGAR 時(shí)間: 2025-3-26 18:52
Evaluation of Machine Learning Algorithms on Internet of Things (IoT) Malware Opcodes,th all technological devices, it is highly prone to malicious attacks such as malware. Several methods have been developed to mitigate these attacks. One of the methods is the use of opcodes to classify malware. These opcodes are generated from disassembled malware programs. Different supervised mac作者: 圓錐 時(shí)間: 2025-3-26 22:03
Mac OS X Malware Detection with Supervised Machine Learning Algorithms,ch more difficult respectively. The traditional approaches for dealing with malware detection are not efficient anymore which paves the way for the adoption of machine learning algorithms as a solution for this issue. Also, there is a significant rise in malware concerned with Mac OS X devices due t作者: 轎車(chē) 時(shí)間: 2025-3-27 04:25 作者: 幾何學(xué)家 時(shí)間: 2025-3-27 09:16 作者: institute 時(shí)間: 2025-3-27 12:40
Mapping CKC Model Through NLP Modelling for APT Groups Reports,entify Advanced Persistent Threats (APT) groups for future risk mitigation by both business and government. Multiple vendors like McAfee and Kaspersky periodically release reports on these APT groups that are absorbed by the security analysts worldwide. These reports identify the Tactics, Techniques作者: Talkative 時(shí)間: 2025-3-27 15:44
Ransomware Threat Detection: A Deep Learning Approach,ransomware have emerged which has created a need for classifying ransomware files into different types, or families. It is important for cybersecurity specialists to be able to identify what family of ransomware they are dealing with, so that they know how to best approach the problem. Machine learn作者: 向外 時(shí)間: 2025-3-27 17:54
Scalable Fair Clustering Algorithm for Internet of Things Malware Classification,g decisions. However, there are concerns about these systems being biased towards a certain population or group. The scalable fair clustering algorithm is one of the variants of the disparate notion of fairness to the clustering problem. It solves this problem by ensuring that each cluster has an ap作者: 的事物 時(shí)間: 2025-3-27 23:56
Die Erythema multiforme-Gruppe,einforce the importance of designing cutting-edge big data analytics, cybersecurity and threat intelligence solutions. It is also important to acknowledge that no security solution is foolproof, and hence we need to have digital forensics and incident response/handling capabilities to help us to ans作者: 人造 時(shí)間: 2025-3-28 02:12 作者: tangle 時(shí)間: 2025-3-28 07:47
Antiarrhythmische Kardiochirurgie,ignificant number of enterprises are migrating to cloud-based environments to save costs and resources. This indicates that traditional approaches to confront cyber threats are not effective in the cloud environment. Considering the vast size and distributed nature of cloud computing systems, deep l作者: 售穴 時(shí)間: 2025-3-28 13:30
https://doi.org/10.1007/978-3-662-10461-3. The APT groups are the nation-state actors or well-resourced groups that target to compromise and exploit individuals and public or private organizations. If the source of the malware can be identified at an early stage, then it will significantly help the cybersecurity specialists to know what th作者: champaign 時(shí)間: 2025-3-28 14:51 作者: 補(bǔ)助 時(shí)間: 2025-3-28 20:40 作者: 發(fā)現(xiàn) 時(shí)間: 2025-3-29 02:49
W. Schr?ter,G. Landbeck,U. G?belin the machine learning techniques. Over the years, many fair machine learning algorithms have been established to reduce the discrimination factor in machine learning. The fair variants of machine learning techniques such as fair clustering models provide a solution to the biased data analysis prob作者: Overdose 時(shí)間: 2025-3-29 06:53
E. W. Keck,M. Bourgeois,H. Meyer,R. Lierschusly seen data. This is most effectively accomplished by machine learning algorithms which are designed to detect abnormal activity, because a system under attack is likely to exhibit anomalous behavior. Due to the fact that anomalous behavior is not guaranteed to be caused by an attacker, false pos作者: 容易生皺紋 時(shí)間: 2025-3-29 07:41
Therapie der Krankheiten des Kindesalterson of the computer with traditional physical infrastructure can improve the efficiency of such facility-based systems. However, it increases the scope of attack from physical security to a cybersecurity perspective. Thus, it becomes critical for authorities of such systems to be able to identify the作者: RECUR 時(shí)間: 2025-3-29 14:43
https://doi.org/10.1007/978-3-662-10465-1 used, unsupervised machine learning technique to detect malware from behavior data of control systems. Clustering algorithms can be susceptible to amplifying biases that may be present in the input datasets. Recent works in fair clustering attempt to solve this problem by making them balanced with 作者: 橡子 時(shí)間: 2025-3-29 16:26
Frühgeborene und hypotrophe Neugeborenewever, the number of attacks for Mac OS has increased exponentially over recent years and new attacks are arising daily which is capable of bypassing the Mac inbuilt security mechanism. Various supervised and unsupervised machine learning classifiers can be used to detect malware samples by comparin作者: fidelity 時(shí)間: 2025-3-29 23:41 作者: fiction 時(shí)間: 2025-3-30 00:53
Heinrich Schmidt Prof. Dr. med.ch more difficult respectively. The traditional approaches for dealing with malware detection are not efficient anymore which paves the way for the adoption of machine learning algorithms as a solution for this issue. Also, there is a significant rise in malware concerned with Mac OS X devices due t作者: 赦免 時(shí)間: 2025-3-30 05:12 作者: 人類(lèi) 時(shí)間: 2025-3-30 09:36
G. Schellong,U. Creutzig,J. Ritterount of the fraudulent transaction can be less than the normal transactions, the impact of a single fraud transaction can be huge in terms of financial loss. Therefore, it is crucial for banks and other financial institutions to be able to detect fraud transactions more effectively. Machine learning作者: 制造 時(shí)間: 2025-3-30 13:45
Therapie der arteriellen Hypertonieentify Advanced Persistent Threats (APT) groups for future risk mitigation by both business and government. Multiple vendors like McAfee and Kaspersky periodically release reports on these APT groups that are absorbed by the security analysts worldwide. These reports identify the Tactics, Techniques作者: Alcove 時(shí)間: 2025-3-30 18:23 作者: Landlocked 時(shí)間: 2025-3-30 21:15
New Results in Coronary Angioscopy,g decisions. However, there are concerns about these systems being biased towards a certain population or group. The scalable fair clustering algorithm is one of the variants of the disparate notion of fairness to the clustering problem. It solves this problem by ensuring that each cluster has an ap作者: 典型 時(shí)間: 2025-3-31 03:29 作者: anagen 時(shí)間: 2025-3-31 05:11
Cyber Threat Attribution with Multi-View Heuristic Analysis,can help attribute the malware to its source with higher accuracy. The experiment uses a multi-view approach similar to a recent work that implements a Fuzzy Consensus Clustering Model for threat attribution. Our experiment is conducted with 3594 malware samples corresponding to 12 different APT gro作者: JOT 時(shí)間: 2025-3-31 13:01
Security of Industrial Cyberspace: Fair Clustering with Linear Time Approximation,subsets called fairlets, maintaining the fairness and the K-median objective. In the second phase, these fairlets are merged using an existing K-median algorithm. The time take for the fairlet decomposition here depends on the first phase and it is super-quadratic in relation to the number of input 作者: CHART 時(shí)間: 2025-3-31 16:52 作者: COW 時(shí)間: 2025-3-31 20:59 作者: Digitalis 時(shí)間: 2025-3-31 23:24 作者: fibula 時(shí)間: 2025-4-1 02:42 作者: 天空 時(shí)間: 2025-4-1 07:27
Mac OS X Malware Detection with Supervised Machine Learning Algorithms,f considering library calls as independent features is employed. The results demonstrate that considering the aforesaid new features increased the best accuracy obtained by about 4% which led to the accuracy of 94.7% achieved by Subspace KNN as an Ensemble classifier.作者: 兩棲動(dòng)物 時(shí)間: 2025-4-1 13:17 作者: NEX 時(shí)間: 2025-4-1 14:44
Hybrid Analysis on Credit Card Fraud Detection Using Machine Learning Techniques,unity is balanced using sampling techniques. The hybrid analysis results show that the supervised ensemble model performs better than the other techniques with an overall accuracy of 99.9% and improved evaluation metrics.
