Intrusion Detection Systems
Intrusion Detection Systems
| Seminare | 2 SWS / 5,0 ECTS (Kursbeschreibung) |
| Veranstalter: | Mohammad Reza Norouzian |
| Zeit und Ort: | Tuesday, 14-16:00 Uhr 01.08.033, Besprechungsraum (5608.01.033) |
| Beginn: | 2018-04-17 |
News
- Kick-off meeting slide can be found here . If you could not attend the meeting, no problem. You can also apply by sending your short CV to Mohammad Norouzian (norouzian@sec.in.tum.de) and choosing the course on the matching system.
- Bachelor students can take the seminar as well.
- Introduction slides can be found here .
Preliminary meeting
Preliminary meeting: Tuesday, January 29, 2018 at 13:30 in room 01.08.033.
Registration
Participants are registered by the instructor based on the results of matching.
Contents
An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity. The most common classification is either in network (NIDS) or host-based (HIDS) intrusion detection systems, in reference to what is monitored by the IDS. Network based intrusion detection attempts to identify unauthorized, illicit, and anomalous behavior based solely on network traffic. A network IDS, using either a network tap, span port, or hub collects packets that traverse a given network. Using the captured data, the IDS system processes and flags any suspicious traffic. One approach to classify attacks is using anomaly detection method based on machine learning algorithms. Students involve reading and writing papers regarding the basis and state-of-the-art of IDS specially in anomaly detection domain.
Prerequisites
Basics of IT security
Objective
The goal for students is to be acquainted with methods, algorithms and technologies in intrusion detection systems, how to identify malicious activities and how to address the challenges in this domain.
Schedule for Presentations
| Title | Speaker | Date |
|
Mohammad Norouzian |
29.01.18 | |
|
Introductory information |
Mohammad Norouzian |
17.04.18 |
| Anomaly Detection: A Survey | Christian von Pentz |
05.06.18 |
| iDeFEND: Intrusion Detection Framework for Encrypted Network Data BlindBox: Deep Packet Inspection over Encrypted Traffic |
Ali Sami Kardaslar | |
| A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection | Ehsaan Qadir | |
| A Novel Hybrid Intrusion Detection Method Integrating Anomaly Detection with Misuse Detection | Mohamed Khalil Ayari |
|
| SCADA-specific Intrusion Detection Prevention Systems: A Survey and Taxonomy | Dana Novanova | |
| Exploiting Traffic Periodicity in Industrial Networks | Leon Imhof |
19.06.18 |
| Towards Learning Normality for Anomaly Detection in Industrial Control Networks | Felix Hoops | |
| Adversarial Attacks Against Intrusion Detection Systems: Taxonomy, Solutions and Open Issues | Tobias Feil | |
| Network Intrusion Detection Based on Semi-supervised Variational Auto-Encoder | Michael Hesse |
26.06.18 |
| Stealthy Deception Attacks Against SCADA Systems | Robert Junge | |
| Analysis of Network Traffic Features for Anomaly Detection | Philipp Eichstetter |
03.07.18 |
| A Deep Learning Approach to Network Intrusion Detection | Sirus Shahbakhti | |
| Fast Portscan Detection Using Sequential Hypothesis Testing | Jonas Donhauser |
10.07.18 |
Topics
Surveys:
Anomaly Detection: A Survey
Anomaly-based network intrusion detection: Techniques, systems and challenges
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Network Anomaly Detection:Methods, Systems and Tools
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
ICS domain:
SENAMI: Selective Non-Invasive Active Monitoring for ICS Intrusion Detection
Accurate Modeling of the Siemens S7 SCADA Protocol for Intrusion Detection and Digital Forensics
iDeFEND: Intrusion Detection Framework for Encrypted Network Data
BlindBox: Deep Packet Inspection over Encrypted Traffic
SCADA-specific Intrusion Detection Prevention Systems: A Survey and Taxonomy
Exploiting Traffic Periodicity in Industrial Networks
Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network
Sequence-aware Intrusion Detection in Industrial Control Systems
Towards Learning Normality for Anomaly Detection in Industrial Control Networks
On Cyber Attacks and Signature Based Intrusion Detection for MODBUS Based Industrial Control Systems
Stealthy Deception Attacks Against SCADA Systems
Analyzing Cyber-Physical Attacks on Networked Industrial Control Systems
Exploiting Siemens Simatic S7 PLCs
Others:
An Overview of IP Flow-Based Intrusion Detection
Bro: A System for Detecting Network Intruders in Real-Time
Network Intrusion Detection Based on Semi-supervised Variational Auto-Encoder
Intrusion Detection in Computer Networks by a Modular Ensemble of One-Class Classifiers
A novel hybrid intrusion detection method integrating anomaly detection with misuse detection
Toward an efficient and scalable feature selection approach for internet traffic classification
Analysis of Network Traffic Features for Anomaly Detection
Adversarial Attacks Against Intrusion Detection Systems: Taxonomy, Solutions and Open Issues
A Deep Learning Approach to Network Intrusion Detection
Fast Portscan Detection Using Sequential Hypothesis Testing
Report Guidelines
How to write a seminar report (link)
Students are strongly encouraged to use Springer LNCS manuscript submission guidelines
Avoid making common report writing mistakes: Download the general guidelines