Titlebook: Cyber Threat Intelligence; Ali Dehghantanha,Mauro Conti,Tooska Dargahi Book 2018 Springer International Publishing AG, part of Springer Na

虚假

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.

大量

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.

BUOY

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

狂热文化

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

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贿赂

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

不能平静

小步舞

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

黑豹

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