A Taxonomy of Malicious Traffic for Intrusion Detection Systems

With the increasing number of network threats it is essential to have a knowledge of existing and new network threats in order to design better intrusion detection systems. In this paper we propose a taxonomy for classifying network attacks in a consistent way, allowing security researchers to focus their efforts on creating accurate intrusion detection systems and targeted datasets.

[1]  Robert C. Atkinson,et al.  Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey , 2017, ArXiv.

[2]  Norman Wilde,et al.  Preventing unauthorized islanding: cyber-threat analysis , 2006, 2006 IEEE/SMC International Conference on System of Systems Engineering.

[3]  Peter Reiher,et al.  A taxonomy of DDoS attack and DDoS defense mechanisms , 2004, CCRV.

[4]  Jugal K. Kalita,et al.  Network attacks: Taxonomy, tools and systems , 2014, J. Netw. Comput. Appl..

[5]  Hannes Holm.  Performance of automated network vulnerability scanning at remediating security issues , 2012, Comput. Secur..

[6]  Georgios Kambourakis,et al.  DDoS in the IoT: Mirai and Other Botnets , 2017, Computer.

[7]  Levente Buttyán,et al.  Embedded systems security: Threats, vulnerabilities, and attack taxonomy , 2015, 2015 13th Annual Conference on Privacy, Security and Trust (PST).

[8]  Alessandro Orso,et al.  A Classification of SQL Injection Attacks and Countermeasures , 2006, ISSSE.

[9]  S. Shankar Sastry,et al.  A Taxonomy of Cyber Attacks on SCADA Systems , 2011, 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing.

[10]  R. Sunitha,et al.  DATA-PROVENANCE VERIFICATION FOR SECURE HOSTS , 2013 .

[11]  Dave Evans,et al.  How the Next Evolution of the Internet Is Changing Everything , 2011 .

[12]  Karl N. Levitt,et al.  Buttercup: on network-based detection of polymorphic buffer overflow vulnerabilities , 2004, 2004 IEEE/IFIP Network Operations and Management Symposium (IEEE Cat. No.04CH37507).

[13]  Philip A. Legg.  Visual Analytics for Non-Expert Users in Cyber Situation Awareness , 2016, Int. J. Cyber Situational Aware..

[14]  Travis Boraten,et al.  Mitigation of Hardware Trojan based Denial-of-Service attack for secure NoCs , 2018, J. Parallel Distributed Comput..

[15]  Robert C. Atkinson,et al.  GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing , 2014, SIN.

[16]  Tyler Wrightson,et al.  Advanced Persistent Threat Hacking: The Art and Science of Hacking Any Organization , 2014 .

[17]  Kamila Nieradzinska,et al.  A Study on Situational Awareness Security and Privacy of Wearable Health Monitoring Devices , 2016, Int. J. Cyber Situational Aware..

[18]  Hossain Shahriar,et al.  Fuzzy Rule-Based Vulnerability Assessment Framework for Web Applications , 2016, Int. J. Secur. Softw. Eng..

[19]  Pierre-Francois Marteau,et al.  Sequence Covering for Efficient Host-Based Intrusion Detection , 2017, IEEE Transactions on Information Forensics and Security.

[20]  Kris Mikael Krister.  Automated Analyses of Malicious Code , 2009 .

[21]  Farinaz Koushanfar.  Trusting the open latent IC backdoors , 2011, STC '11.

[22]  Poonam Verma,et al.  Graphical Password Using an Intuitive Approach , 2018 .

[23]  Franco Callegati,et al.  Man-in-the-Middle Attack to the HTTPS Protocol , 2009, IEEE Security & Privacy Magazine.

[24]  Robert C. Atkinson,et al.  Machine Learning Approach for Detection of nonTor Traffic , 2017, ARES.

[25]  bellekens xavier,et al.  Cyber-Physical-Security Model for Safety-Critical IoT Infrastructures , 2015 .

[26]  G. Manimaran,et al.  Internet infrastructure security: a taxonomy , 2002, IEEE Netw..