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B01=Georgios Kambourakis
B01=Marios Anagnostopoulos
B01=Peng Zhou
B01=Weizhi Meng
Bot Herder
Botnet Detection
C2 Channel
Category1=Non-Fiction
Category=UBL
Category=UR
Category=URD
Category=URH
Category=UT
Category=UYF
COP=United Kingdom
Covert Channels
Data Set
Delivery_Pre-order
Destination IP Address
DNS Traffic
DoS Attack
eq_computing
eq_isMigrated=2
eq_non-fiction
Evasion Mechanisms
ICMP
Information Hiding Techniques
IoT Device
IP Address
IRC Protocol
IRC Server
Language_English
Launch DDOS Attack
Mobile Botnets
Network Steganography
P2P Botnets
PA=Temporarily unavailable
Port Tcp
Price_€100 and above
PS=Active
Public IP Address
Social Bots
softlaunch
TCP Connection
Unsupervised Machine Learning
Unsupervised Ml Algorithm

Botnets

English

This book provides solid, state-of-the-art contributions from both scientists and practitioners working on botnet detection and analysis, including botnet economics. It presents original theoretical and empirical chapters dealing with both offensive and defensive aspects in this field. Chapters address fundamental theory, current trends and techniques for evading detection, as well as practical experiences concerning detection and defensive strategies for the botnet ecosystem, and include surveys, simulations, practical results, and case studies.

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Current price €127.99
Original price €128.99
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Age Group_Uncategorizedautomatic-updateB01=Georgios KambourakisB01=Marios AnagnostopoulosB01=Peng ZhouB01=Weizhi MengBot HerderBotnet DetectionC2 ChannelCategory1=Non-FictionCategory=UBLCategory=URCategory=URDCategory=URHCategory=UTCategory=UYFCOP=United KingdomCovert ChannelsData SetDelivery_Pre-orderDestination IP AddressDNS TrafficDoS Attackeq_computingeq_isMigrated=2eq_non-fictionEvasion MechanismsICMPInformation Hiding TechniquesIoT DeviceIP AddressIRC ProtocolIRC ServerLanguage_EnglishLaunch DDOS AttackMobile BotnetsNetwork SteganographyP2P BotnetsPA=Temporarily unavailablePort TcpPrice_€100 and abovePS=ActivePublic IP AddressSocial BotssoftlaunchTCP ConnectionUnsupervised Machine LearningUnsupervised Ml Algorithm

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Product Details
  • Weight: 780g
  • Dimensions: 156 x 234mm
  • Publication Date: 08 Oct 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Language: English
  • ISBN13: 9780367191542

About

Dr. Marios Anagnostopoulos received his Ph.D. degree in information and

communication systems engineering from the Department of Information and

Communication Systems Engineering, University of the Aegean, Greece, in 2016.

The title of his doctoral thesis was “DNS as a multipurpose attack vector.”

Currently, he is Post-Doctoral Research Fellow in the Norwegian University of

Science and Technology (NTNU). Prior to joining NTNU, he worked as Post-

Doctoral Research Fellow in the Singapore University of Technology and Design

(SUTD). His research interests are in the fields of network security and privacy,

mobile and wireless networks security, cyber-physical security, and blockchain in

security and privacy.

Dr. Georgios Kambourakis received the Ph.D. degree in information and communication

systems engineering from the Department of Information and Communications

Systems Engineering, University of the Aegean, Greece, where he is

currently an associate professor, and the head of the department. His research

interests are in the fields of mobile and wireless networks security and privacy. He

has over 120 refereed publications in the aforementioned fields of study. For more

information, please visit http://www.icsd.aegean.gr/gkamb.

Dr. Weizhi Meng is currently an assistant professor in the Cyber Security Section,

Department of Applied Mathematics and Computer Science, Technical University

of Denmark (DTU), Denmark. He received his Ph.D. degree in computer science

from the City University of Hong Kong (CityU), China. Prior to joining DTU, he

worked as a research scientist in Institute for Infocomm Research, A*Star, Singapore,

and as a senior research associate in CS Department, CityU. He won the Outstanding

Academic Performance Award during his doctoral study and is a recipient of

the Hong Kong Institution of Engineers (HKIE) Outstanding Paper Award for

Young Engineers/Researchers in both 2014 and 2017. He is also a recipient of Best

Paper Award from ISPEC 2018 and Best Student Paper Award from NSS 2016. His

primary research interests are cyber security and intelligent technology in security,

including intrusion detection, smartphone security, biometric authentication, HCI

security, trust management, blockchain in security, and malware analysis.

Dr. Peng Zhou is currently an associate professor at Shanghai University. He has

received his Ph.D. degree from the Hong Kong Polytechnic University and

worked as a research fellow in Singapore Nanyang Technological University for

one year. His research interests include network security, computer worms and

propagation, and machine learning.

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