Firewall monitoring using intrusion detection systems

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2005Includes bibliographical references (leaves: 79-81)Text in English Abstract: Turkish and Englishviii,79 leavesMost organizations have intranet, they know the benefits of connecting their private LAN to the Internet. However, Internet is inherently an insecure network. That makes the security of the computer systems an imported problem. The first step of network security is firewalls. Firewalls are used to protect internal networks from external attacks through restricting network access according to the rules. The firewall must apply previously defined rules to each packet reaching to its network interface. If the application of rules are prohibited due to malfunction or hacking, internal network may be open to attacks and this situation should be recovered as fast as possible. In order to be sure about the firewall working properly, we proposed to use Intrusion Detection Systems (IDS)to monitor firewall operation. The architecture of our experimental environment is composed of a firewall and two IDSs. One IDS is between external network and firewall, while the other is between firewall and private network. Those two IDSs are invisible to the both networks and they send their information to a monitoring server, which decides, based on two observations, whether the firewall is working properly or not

Stepping-stone detection technique for recognizing legitimate and attack connections

A stepping-stone connection has always been assumed as an intrusion since the first research on stepping-stone connections twenty years ago. However, not all stepping-stone connections are malicious.This paper proposes an enhanced stepping-stone detection (SSD) technique which is capable to identify legitimate connections from stepping-stone connections.Stepping-stone connections are identified from raw network traffics using timing-based SSD approach.Then, they go through an anomaly detection technique to differentiate between legitimate and attack connections.This technique has a promising solution to accurately detecting intrusions from stepping-stone connections.It will prevent incorrect responses that punish legitimate users

Intelligent Network-Based Stepping Stone Detection Approach.

This research intends to introduce a new usage of Artificial Intelligent (AI) approaches in Stepping Stone Detection (SSD) fields of research

Fuzzy intrusion detection

Visual data mining techniques are used to assess which metrics are most effective at detecting different types of attacks. The research confirms that data aggregation and data reduction play crucial roles in the formation of the metrics. Once the proper metrics are identified, fuzzy rules are constructed for detecting attacks in several categories. The attack categories are selected to match the different phases that intruders frequently use when attacking a system. A suite of attacks tools is assembled to test the fuzzy rules. The research shows that fuzzy rules applied to good metrics can provide an effective means of detecting a wide variety of network intrusion activity. This research is being used as a proof of concept for the development of system known as the Fuzzy Intrusion Recognition Engine (FIRE).This thesis examines the application of fuzzy systems to the problem of network intrusion detection. Historically, there have been two primary methods of performing intrusion detection: misuse detection and anomaly detection. In misuse detection, a database of attack signatures is maintained that match known intrusion activity. While misuse detection systems are very effective, they require constant updates to the signature database to remain effective or to detect distinctly new attacks. Anomaly detection systems attempt to discover suspicious behavior by comparing system activity against past usage profiles. In this research, network activity is collected and usage profiles established for a variety of metrics. A network data gathering and data analysis tool was developed to create the metrics from the network stream. Great care is given to identifying the metrics that are most suitable for detecting intrusion activity

Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor

Statistical methods used for intrusion detection

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2006Includes bibliographical references (leaves: 58-64)Text in English; Abstract: Turkish and Englishx, 71 leavesComputer networks are being attacked everyday. Intrusion detection systems are used to detect and reduce effects of these attacks. Signature based intrusion detection systems can only identify known attacks and are ineffective against novel and unknown attacks. Intrusion detection using anomaly detection aims to detect unknown attacks and there exist algorithms developed for this goal. In this study, performance of five anomaly detection algorithms and a signature based intrusion detection system is demonstrated on synthetic and real data sets. A portion of attacks are detected using Snort and SPADE algorithms. PHAD and other algorithms could not detect considerable portion of the attacks in tests due to lack of sufficiently long enough training data

Detection of advanced persistent threat using machine-learning correlation analysis

As one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale. APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss. The accurate detection and prediction of APT is an ongoing challenge. This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way. The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. MLAPT is experimentally evaluated and the presented sy