Snort Network Intrusion Detection System Research Articles

The Next-Generation NIDS Platform: Cloud-Based Snort NIDS Using Containers and Big Data

Snort is a well-known, signature-based network intrusion detection system (NIDS). The Snort sensor must be placed within the same physical network, and the defense centers in the typical NIDS architecture offer limited network coverage, especially for remote networks with a restricted bandwidth and network policy. Additionally, the growing number of sensor instances, followed by a quick increase in log data volume, has caused the present system to face big data challenges. This research paper proposes a novel design for a cloud-based Snort NIDS using containers and implementing big data in the defense center to overcome these problems. Our design consists of Docker as the sensor’s platform, Apache Kafka, as the distributed messaging system, and big data technology orchestrated on lambda architecture. We conducted experiments to measure sensor deployment, optimum message delivery from the sensors to the defense center, aggregation speed, and efficiency in the data-processing performance of the defense center. We successfully developed a cloud-based Snort NIDS and found the optimum method for message-delivery from the sensor to the defense center. We also succeeded in developing the dashboard and attack maps to display the attack statistics and visualize the attacks. Our first design is reported to implement the big data architecture, namely, lambda architecture, as the defense center and utilize rapid deployment of Snort NIDS using Docker technology as the network security monitoring platform.

Intrusion Detection by Forensic Method in Private Cloud using Eucalyptus

computing has become the mature term which has dealt from single user to large enterprises. The private cloud platform building framework Eucalyptus has great pace of development within short span of time. Achieving AWS (Amazon Web Services) compatible features development along with scalability and sustainability has introduced several issues have an adverse effect on the cloud system. In continuing with this, the chances of intrusion also increase evading traditional mechanism of security. Issues have been introduced due to seamless integration of such structure with computing technologies and so on. By taking advantage of such flaws, the Cybercrime is rapidly increasing in this field. The proposed work is regarded with Digital forensics technique and intrusion detection mechanism. In this scope of work, an experimental setup of Eucalyptus with Snort NIDS (Network Intrusion Detection System) to detect attacks using snort rules has been created. The Eucalyptus Cloud components and Snort logs are exported to outside cloud network to rSyslog server which would be later analyzed by the Awstats log analyzer. Accompanied to above, this scope of work also addresses toward the issue of Eucalyptus to export its logs to the remote rSyslog server. This system will definitely help to reduce the strain on the Cloud forensics.

An improved model of Snort system based on data mining

An improved model of the Snort network intrusion detection system based on the theory of data mining was proposed,regarding the problem that Snort is powerless to new types of intrusion.In the new model,normal behavior patterns mining module,anomaly detection engine module and new rules generating module were added to the Snort system.By these improvements the system has double capacity of learning rules from new intrusions and learning normal behavior patterns from normal data.The test result shows that new types of intrusion can be detected effectively,the false negative of Snort can be reduced,and the detection efficiency of the system has been enhanced.

Building intrusion pattern miner for Snort network intrusion detection system

In this paper, we enhance the functionalities of Snort network-based intrusion detection system to automatically generate patterns of misuse from attack data, and the ability of detecting sequential intrusion behaviors. To that, we implement an intrusion pattern discovery module which applies data mining technique to extract single intrusion patterns and sequential intrusion patterns from a collection of attack packets, and then converts the patterns to Snort detection rules for on-line intrusion detection. In order to detect sequential intrusion behavior, the Snort detection engine is accompanied with our intrusion behavior detection engine. Intrusion behavior detection engine will create an alert when a series of incoming packets match the signatures representing sequential intrusion scenarios.