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- Efficient Machine Learning for Attack Detection
- Healthcare Industry Cybersecurity Attack Trends
- Download Security Report 2018/2019
- A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
- Developing Network Forensic Mechanisms for the Botnet of Things • Work Carried out by Slay, Sitnikova, Moustafa and Koroniotis
- Threat Landscape Report (Light) – 2Nd Quarter 2018
- D2.1 Threat Landscape: Trends and Methods
- Avoiding the Internet of Insecure Industrial Things
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- On the Economics of Ransomware
- Most Infamous Botnets of the 21St Century
- Defenders Disrupting Adversaries: Framework, Dataset, and Case Studies of Disruptive Counter-Cyber Operations
- Ddos Attacks
- Testing and Hardening Iot Devices Against the Mirai Botnet
- The Weaponization of Iot Devices Rise of the Thingbots
- Hacking the Internet of Things: Vulnerabilities, Dangers, and Legal Responses*
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- Hardware Security & Latest R&D
- Intelligent Detection of Iot Botnets Using Machine Learning and Deep Learning
- Forensic Acquisition and Analysis of Volatile Data in Memory
- The Rise of the Botnets: Mirai & Hajime
- Fancy Bear by the FBI 9 Nationstate Iot Attacks
- Hajime – Everything You Wanted to Know but Were Afraid to Discover
- UNIVERSITY of CALIFORNIA RIVERSIDE Towards a Systematic
- Cyber Flash a Spotlight on Cyber and Privacy Trends Edition 1, December 2016
- Review of the Year 2017 Kaspersky Security Bulletin: Review of the Year 2017
- Licking Residential Care, 225 W
- A Dynamic Cyber-Based View of the Firm
- Assessing the Threat of Blockchain-Based Botnets
- The Circle of Life: a Large-Scale Study of the Iot Malware Lifecycle
- The First Step Towards Modeling Unbreakable Malware
- 1 Avoiding the Internet of Insecure Industrial Things Dr Lachlan
- Early Detection of Iot Malware Network Activity Using Machine Learning Techniques