Intrusion Detection System Rules Research Articles

Implementation of signature-based intrusion detection system using SNORT to prevent threats in network servers

Security is an important factor in today's digital era. In a network, implementing a security system is the focus of a network developer. One of the most basic network securities is in the form of access. To manage the security of a system must be known in advance who is involved in the system and what activities are carried out. Just like a security alarm, which monitors work conditions, this is the function of the Intrusion Detection System (IDS). IDS has several effective methods for detecting threats, one of which is the Signature-based method. IDS can be implemented through the open-source SNORT application, and the method works with rules which are commands to IDS to recognize various attacks. IDS rules will be included in the signature matching process, which means matching between rules and incoming attacks and views of both protocols, then the IDS will generate alerts that contain notifications. This study conducted a reading of the MIT-DARPA 1999 dataset on 1,252,412 packages and tested alerting with Network Scanning and DoS attacks. Analyze Package Data runs at a speed of 83,494 packets /second and gets a true positive percentage reaching 100% and an accuracy of 98.10%.

Model-based IDS design for ICSs

Industrial systems present security risk factors related to their cyber vulnerabilities. These systems spread out over the world, continue to be targets of attacks. While industrial systems share common vulnerabilities with Information Technology (IT) systems, they tend to have more constraints due to the interaction between cyber and physical systems. Intrusion detection systems give visibility to the system and are considered to be one of the solutions to detect targeting attacks. Hence, it seems relevant to rely on a physical model of the cyber–physical system to obtain an intrusion detection system (IDS) for industrial systems. Most IDSs are based on rules that define how potential attacks are detected. These rules are generally used to describe either the normal system behavior or potential attack scenarios. However, manually creating and maintaining rules for a complex Cyber–Physical system or ICS can prove to be a very tedious and difficult task. This paper proposes a solution to model ICSs and design specific IDSs for ICSs. A model-based IDS rules generator is proposed, which converts a system model into anomaly-based IDS rules. Finally, the effectiveness of the generated rules is evaluated.

WITHDRAWN: Testing performance of RaspberryPi as IDS using SNORT

Now a day’s world is moving fast towards more advancement where internet usage has become essential to carry out daily activities. Almost all IoT based devices in our home like fans, lights, air conditioners etc. are being connected and accessed over the internet with own IP address can be open to hackers. Definitely IoT makes its mark of significance in our life at the other coin the major issue of IoT is security which makes security analysts to move their focus towards security of IoT devices and networks. Performance of RaspberryPi is tested in this paper to justify which kit works well to safeguard from intrusion when Snort IDS (Intrusion Detection System) is installed. Comparison of RaspberryPi is carried out using snort IDS with custom rule set specified in the case study done in 2013 and default set of rules of Snort IDS tested on IOT kits RaspberryPi 4B and RaspberryPi 3 A+ and comparison on non functional requirements CPU load, throughput &memory load was made to find out which IOT kit is preferable to safeguard from hacking attack.

DESIGNING RULES TO IMPLEMENT RECONNAISSANCE AND UNAUTHORIZED ACCESS ATTACKS FOR INTRUSION DETECTION SYSTEM

Abstract- Network attacks are classified according to their objective into three types: Denial of Services (DOS), reconnaissance and unauthorized access. A base signature Intrusion Detection System (IDS) which gives an alarm when the monitor network traffic meets a previously specified set of criteria of attack traffic. This paper will focus on design, compose, and process IDS rules, and then to decide whether that packet is intrusive or not, by examining the signatures of the attacks in both incoming packets headers and payload to networks. Packet sniffer is performs capturing, decoding and reassembling of the network packet traffic, then passes it to the programmed rules. Linux backtrack tools was used to implement an IDS scenario for two types of attacks (Reconnaissance and Unauthorized access). The results show that IDS rules are able to detect large numbers of various attacks.

LARGen: Automatic Signature Generation for Malwares Using Latent Dirichlet Allocation

As the quantity and complexity of network threats grow, Intrusion Detection Systems (IDSs) have become critical for securing networks. Achieving computer network intrusion detection with these IDSs requires high-level information technology and security expertise because malicious traffic has to be rigorously analyzed and the appropriate IDS rules written to effectively detect vulnerabilities that may potentially be exploited. However, incorrect IDS rules may produce numerous false positives, thereby degrading the performance of the IDS, and even worse, paralyzing the network. In this paper, we present a novel approach that exploits the Latent Dirichle Allocation (LDA) algorithm to generate IDS rules. Our proposed method, called L DA-based A utomatic R ule Gen eration ( LARGen ), automatically performs an analysis of the malicious traffic and extracts the appropriate attack signatures that will be used for IDS rules. LARGen first extracts multiple signature strings embedded in network flows. Then, the flows are classified based on the extracted signature strings, and key content strings for malicious traffic are identified through the LDA inferential topic model. Those key content strings are the core of an IDS rule that can detect malicious traffic. We study the effectiveness of LDA in the context of network attack signature generation via extensive experiments with real network trace data, consisting of both benign and malicious traffic. Experimental results confirm that threat rules generated from LARGen accurately detect every cyber attack with high accuracy.

Improving Intrusion Detection System Based on Snort Rules for Network Probe Attacks Detection with Association Rules Technique of Data Mining

The intrusion detection system (IDS) is an important network security tool for securing computer and network systems. It is able to detect and monitor network traffic data. Snort IDS is an open-source network security tool. It can search and match rules with network traffic data in order to detect attacks, and generate an alert. However, the Snort IDS Â can detect only known attacks. Therefore, we have proposed a procedure for improving Snort IDS rules, based on the association rules data mining technique for detection of network probe attacks. Â We employed the MIT-DARPA 1999 data set for the experimental evaluation. Since behavior pattern traffic data are both normal and abnormal, the abnormal behavior data is detected by way of the Snort IDS. The experimental results showed that the proposed Snort IDS rules, based on data mining detection of network probe attacks, proved more efficient than the original Snort IDS rules, as well as icmp.rules and icmp-info.rules of Snort IDS. Â The suitable parameters for the proposed Snort IDS rules are defined as follows: Min_sup set to 10%, and Min_conf set to 100%, and through the application of eight variable attributes. As more suitable parameters are applied, higher accuracy is achieved.

An Intrusion Detection Method Based on Shuffle Frog Leaping Algorithm

This paper presents an intrusion detection model based on shuffled frog leaping algorithm, the model search speed, high accuracy based on shuffled frog leaping algorithm, using the shuffled frog leaping algorithm generates a set of classification rules from the KDD99 data network audit data collection, quality and the use of objective function shuffled frog leaping algorithm to control the production rule, and then application of the rule of dynamically generated to rule based intrusion detection system, achieve the purpose of detection. The experimental results show that, this method of detection rules generation based on shuffled frog leaping algorithm, can significantly improve quality of generation rules, reduce the rules of intrusion detection system based on the false negative rate.

On Cyber Attacks and Signature Based Intrusion Detection for Modbus Based Industrial Control Systems

Industrial control system communication networks are vulnerable to reconnaissance, response injection, command injection, and denial of service attacks. Such attacks can lead to an inability to monitor and control industrial control systems and can ultimately lead to system failure. This can result in financial loss for control system operators and economic and safety issues for the citizens who use these services. This paper describes a set of 28 cyber attacks against industrial control systems which use the MODBUS application layer network protocol. The paper also describes a set of standalone and state based intrusion detection system rules which can be used to detect cyber attacks and to store evidence of attacks for post incident analysis. All attacks described in this paper were validated in a laboratory environment. The detection rate of the intrusion detection system rules presented by attack class is also presented.