標(biāo)題: Titlebook: Cyber Threat Intelligence; Ali Dehghantanha,Mauro Conti,Tooska Dargahi Book 2018 Springer International Publishing AG, part of Springer Na [打印本頁(yè)] 作者: Holter-monitor 時(shí)間: 2025-3-21 17:23
書目名稱Cyber Threat Intelligence影響因子(影響力)
作者: FIS 時(shí)間: 2025-3-21 21:42 作者: ALIBI 時(shí)間: 2025-3-22 03:11
Adaptive Traffic Fingerprinting for Darknet Threat Intelligence,ts to reduce the candidate data set to eliminate client-side traffic that is most unlikely to be responsible for server-side connections of interest. Our test results show that MITM manipulated server responses lead to expected changes received by the Tor client. Using simulation data generated by s作者: 現(xiàn)暈光 時(shí)間: 2025-3-22 07:36 作者: 值得 時(shí)間: 2025-3-22 10:05 作者: FELON 時(shí)間: 2025-3-22 15:34
Education as a Key Factor in Fighting AIDS2-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and作者: FELON 時(shí)間: 2025-3-22 19:35
Triangles in Heterosexual HIV Transmissionreduction. Using the CorrelationAttributeEval method close to 100% precision can be maintained with a feature reduction of 59.5%. The CFSSubset filter achieves the highest feature reduction of 97.7% however with a slightly lower precision at 94.2%..Using a ranking method applied across the attribute作者: 現(xiàn)暈光 時(shí)間: 2025-3-22 21:33
Keynote Address: AIDS in the United Kingdomts to reduce the candidate data set to eliminate client-side traffic that is most unlikely to be responsible for server-side connections of interest. Our test results show that MITM manipulated server responses lead to expected changes received by the Tor client. Using simulation data generated by s作者: Exclaim 時(shí)間: 2025-3-23 02:41 作者: obstinate 時(shí)間: 2025-3-23 07:21 作者: ANTI 時(shí)間: 2025-3-23 11:38
https://doi.org/10.1007/978-3-319-42199-5 attack evidences. In this introductory chapter we first discuss the notion of cyber threat intelligence and its main challenges and opportunities, and then briefly introduce the chapters of the book which either address the identified challenges or present opportunistic solutions to provide threat intelligence.作者: Inscrutable 時(shí)間: 2025-3-23 14:50
Triangles in Heterosexual HIV Transmissioniques. In this paper we introduce ., a machine learning evaluation study for consistent detection of Windows ransomware network traffic. Using a dataset created from conversation-based network traffic features we achieved a True Positive Rate (TPR) of 97.1% using the Decision Tree (J48) classifier.作者: 導(dǎo)師 時(shí)間: 2025-3-23 21:46 作者: braggadocio 時(shí)間: 2025-3-24 01:27 作者: 過去分詞 時(shí)間: 2025-3-24 05:55 作者: 傲慢人 時(shí)間: 2025-3-24 08:44
Leveraging Machine Learning Techniques for Windows Ransomware Network Traffic Detection,iques. In this paper we introduce ., a machine learning evaluation study for consistent detection of Windows ransomware network traffic. Using a dataset created from conversation-based network traffic features we achieved a True Positive Rate (TPR) of 97.1% using the Decision Tree (J48) classifier.作者: Thyroid-Gland 時(shí)間: 2025-3-24 12:07 作者: glucagon 時(shí)間: 2025-3-24 15:36
A Model for Android and iOS Applications Risk Calculation: CVSS Analysis and Enhancement Using Caseinst risks associated with several Android and iOS applications and discuss achieved improvements and advantages of our modelling, such as the importance and the impact of time on the overall CVSS score calculation.作者: 可卡 時(shí)間: 2025-3-24 21:55 作者: 神經(jīng) 時(shí)間: 2025-3-25 00:57
Keynote Address: AIDS in the United Kingdomet mobile-specific users. This research investigates mobile-specific phishing attacks through the dissection of phishing kits used for the attacks, presentation of real world phishing campaigns, and observations about PayPal’s insight into mobile web-based phishing numbers.作者: chandel 時(shí)間: 2025-3-25 04:45 作者: ligature 時(shí)間: 2025-3-25 11:26
A Practical Analysis of the Rise in Mobile Phishing,et mobile-specific users. This research investigates mobile-specific phishing attacks through the dissection of phishing kits used for the attacks, presentation of real world phishing campaigns, and observations about PayPal’s insight into mobile web-based phishing numbers.作者: Ingratiate 時(shí)間: 2025-3-25 13:16 作者: profligate 時(shí)間: 2025-3-25 19:49 作者: 原始 時(shí)間: 2025-3-25 23:22
Keynote Address: AIDS in the United Kingdomta. The framework is validated through experimenting two use-cases on a virtual SDN running on Mininet. Analysis and comparison of Southbound PCAP files and the memory images of switches enabled successful acquisition of forensic evidential artefacts pertaining to these use cases.作者: RENAL 時(shí)間: 2025-3-26 01:01 作者: 溝通 時(shí)間: 2025-3-26 06:05
Stephan Dressler,Matthias Wienoldssment of cloud forensics research trends between 2009 and 2016. Moreover, we provide a classification of cloud forensics process to detect the most profound research areas and highlight remaining challenges.作者: 駭人 時(shí)間: 2025-3-26 11:30
PDF-Malware Detection: A Survey and Taxonomy of Current Techniques,eys existing state of the art about systems for the detection of malicious PDF files and organizes them in a taxonomy that separately considers the used approaches and the data analyzed to detect the presence of malicious code.作者: obeisance 時(shí)間: 2025-3-26 13:13 作者: 代理人 時(shí)間: 2025-3-26 18:32
Forensics Investigation of OpenFlow-Based SDN Platforms,ta. The framework is validated through experimenting two use-cases on a virtual SDN running on Mininet. Analysis and comparison of Southbound PCAP files and the memory images of switches enabled successful acquisition of forensic evidential artefacts pertaining to these use cases.作者: 虛假 時(shí)間: 2025-3-26 23:45
Mobile Forensics: A Bibliometric Analysis,dvances investigators have made over time on the subject, the possible future technologies that could influence more changes in the field of mobile forensics and its impact, covering also the difference between mobile forensics and computer forensics.作者: 大量 時(shí)間: 2025-3-27 03:35
Emerging from the Cloud: A Bibliometric Analysis of Cloud Forensics Studies,ssment of cloud forensics research trends between 2009 and 2016. Moreover, we provide a classification of cloud forensics process to detect the most profound research areas and highlight remaining challenges.作者: BUOY 時(shí)間: 2025-3-27 06:14
Book 2018er threat intelligence concepts against a range of threat actors and threat tools (i.e. ransomware) in cutting-edge technologies, i.e., Internet of Things (IoT), Cloud computing and mobile devices. This book also provides the technical information on cyber-threat detection methods required for the r作者: 狂熱文化 時(shí)間: 2025-3-27 10:46
1568-2633 ne of the first books that focuses on cyber threat intellige.This book provides readers with up-to-date research of emerging cyber threats and defensive mechanisms, which are timely and essential. It covers cyber threat intelligence concepts against a range of threat actors and threat tools (i.e. ra作者: Esalate 時(shí)間: 2025-3-27 14:18 作者: blithe 時(shí)間: 2025-3-27 21:50 作者: 賄賂 時(shí)間: 2025-3-28 01:04
Application of Machine Learning Techniques to Detecting Anomalies in Communication Networks: Datase threats has received considerable attention in research literature. Anomalies of Border Gateway Protocol (BGP) affect network operations and their detection is of interest to researchers and practitioners. In this Chapter, we describe main properties of the protocol and datasets that contain BGP re作者: 不能平靜 時(shí)間: 2025-3-28 02:44 作者: 小步舞 時(shí)間: 2025-3-28 06:38
Leveraging Machine Learning Techniques for Windows Ransomware Network Traffic Detection,for high revenues creating a viable criminal business model. Individuals, private companies or public service providers e.g. healthcare or utilities companies can all become victims of ransomware attacks and consequently suffer severe disruption and financial loss. Although machine learning algorith作者: 黑豹 時(shí)間: 2025-3-28 13:24
Leveraging Support Vector Machine for Opcode Density Based Detection of Crypto-Ransomware,orating the use of opcode characteristics and Support Vector Machine have been demonstrated to be a successful method for general malware detection. This research focuses on crypto-ransomware and uses static analysis of malicious and benign Portable Executable files to extract 443 opcodes across all作者: 緩和 時(shí)間: 2025-3-28 17:56 作者: BANAL 時(shí)間: 2025-3-28 21:57
A Practical Analysis of the Rise in Mobile Phishing,desire for a greater return on investment from their attacks against the common internet user. The digital landscape has been ever-changing since the emergence of mobile technologies. The intersection of the internet and the growing mobile user-base fueled the natural progression of phishers to targ作者: delegate 時(shí)間: 2025-3-29 02:28
PDF-Malware Detection: A Survey and Taxonomy of Current Techniques,table nature and widespread adoption. The flexibility and power of this format are not only leveraged by benign users, but from hackers as well who have been working to exploit various types of vulnerabilities, overcome security restrictions, and then transform the PDF format in one among the leadin作者: 剝皮 時(shí)間: 2025-3-29 06:34
Adaptive Traffic Fingerprinting for Darknet Threat Intelligence, for organisations to block such traffic, or to try and identify when it is used and for what purposes. However, anonymity in cyberspace has always been a domain of conflicting interests. While it gives enough power to nefarious actors to masquerade their illegal activities, it is also the corner st作者: 危機(jī) 時(shí)間: 2025-3-29 08:46
A Model for Android and iOS Applications Risk Calculation: CVSS Analysis and Enhancement Using Caserelated to software vulnerabilities. However, many threat intelligence platforms and industry-wide standards are relying on CVSS score to evaluate cyber security compliance. This paper suggests several improvements to the calculation of Impact and Exploitability sub-scores within the CVSS, improve i作者: exclusice 時(shí)間: 2025-3-29 13:25
A Honeypot Proxy Framework for Deceiving Attackers with Fabricated Content,ent the attacker’s actions but instead aims to learn about the attacker’s behavior. In this paper, we discuss the idea of deceiving attackers with fake services and fabricated content in order to find out more about malware’s functionality and to hamper cyber intelligence. The effects of false data 作者: Haphazard 時(shí)間: 2025-3-29 19:05
Investigating the Possibility of Data Leakage in Time of Live VM Migration,, VMs can be transferred from a source host to a destination host due to various reasons such as maintenance of the source host or resource requirements of the VMs. The VM migration can happen in two ways, live and offline migration. In time of live VM migration, VMs get transferred from a source ho作者: GROG 時(shí)間: 2025-3-29 22:59 作者: 受傷 時(shí)間: 2025-3-30 03:01 作者: OUTRE 時(shí)間: 2025-3-30 08:01
Emerging from the Cloud: A Bibliometric Analysis of Cloud Forensics Studies,lways-available storage, a lot of private and confidential data are now stored on different cloud platforms. Being such a gold mine of data, cloud platforms are among the most valuable targets for attackers. Therefore, many forensics investigators have tried to develop tools, tactics and procedures 作者: 使出神 時(shí)間: 2025-3-30 10:07
Ali Dehghantanha,Mauro Conti,Tooska DargahiFocuses on cyber threat intelligence of recent threats (i.e. ransomware) within emerging IT environments (i.e. IoT, Cloud, Mobile devices).One of the first books that focuses on cyber threat intellige作者: 財(cái)產(chǎn) 時(shí)間: 2025-3-30 15:39 作者: arabesque 時(shí)間: 2025-3-30 17:15
https://doi.org/10.1007/978-3-319-42199-5ts in almost real-time. In practice, timely dealing with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions—this in essence defines cyber threat intelligence notion. However, such an intelligence would not作者: 敵意 時(shí)間: 2025-3-30 21:27
Education as a Key Factor in Fighting AIDSevade network-based and host-based security protections. The fast growth in variety and number of malware species made it very difficult for forensics investigators to provide an on time response. Therefore, Machine Learning (ML) aided malware analysis became a necessity to automate different aspect作者: 消音器 時(shí)間: 2025-3-31 03:12
