Performance Study of the Running Times of well known Pattern Matching Algorithms for Signature-based Intrusion Detection Systems

Intrusion detection system (IDS) is the basic component of any network defense scheme. Signature based intrusion detection techniques are widely used in networks for fast response to detect threats. One of the main challenges faced by signature-based IDS is that every signature requires an entry in the database, and so a complete database might contain hundreds or even thousands of entries. Each packet is to be compared with all the entries in the database. This can be highly resource-consuming and doing so will slow down the throughput and making the IDS vulnerable. Since pattern matching computations dominate in the overall performance of a Signature-based IDS, efficient pattern matching algorithms should be used which use minimal computer storage and which minimize the searching response time. In this paper we present a performance study of the running times of different well known pattern matching algorithms using multiple sliding windows approach. DOI: 10.17762/ijritcc2321-8169.150613

Sistem Pendeteksian Penyusupan Jaringan Komputer dengan Active Response Menggunakan Metode Hybrid Intrusion Detection, Signatures dan Anomaly Detection

The progress of internet technology increase the need of security data. The progress of tools which have intrusion ability, also influence these needed. The methods of Intrusion Detection System (IDS) implementation and methods of analyze intrusion have excess and lack, which mutually completes. There are a lot of IDS now, but just an IDS open source based is snort. Method of snort implementation is network based restricted. This Final Task\u27s system used Hybrid Intrusion Detection System, Signatures and Anomaly Detection Methods. The indicator which used to detect intrusion are IP Address and Port Number. This system use TCP, UDP and ICMP protocols. This system also, is completed by active response, like blocking access for intruder. This System Implementation with Java Programming Language for engine perform and Java Server Pages (JSP) to develop user interface, The database which used is MYSQL. There are two of development test; Link system test and intrusion test. The link system test show the connect each interface. Intrusion is executed by host detection which used DoS HTTP tools and network detection which used Ping of Death\u27s scripts. The intrusion testing conclusions are; can be detected, analyze and active response for intrusion

Sistem Pendeteksian Penyusupan Jaringan Komputer dengan Active Response Menggunakan Metode Hybrid Intrusion Detection, Signatures dan Anomaly Detection

The progress of internet technology increase the need of security data. The progress of tools which have intrusion ability, also influence these needed. The methods of Intrusion Detection System (IDS) implementation and methods of analyze intrusion have excess and lack, which mutually completes. There are a lot of IDS now, but just an IDS open source based is snort. Method of snort implementation is network based restricted. This Final Task's system used Hybrid Intrusion Detection System, Signatures and Anomaly Detection Methods. The indicator which used to detect intrusion are IP Address and Port Number. This system use TCP, UDP and ICMP protocols. This system also, is completed by active response, like blocking access for intruder. This System Implementation with Java Programming Language for engine perform and Java Server Pages (JSP) to develop user interface, The database which used is MYSQL. There are two of development test; Link system test and intrusion test. The link system test show the connect each interface. Intrusion is executed by host detection which used DoS HTTP tools and network detection which used Ping of Death's scripts. The intrusion testing conclusions are; can be detected, analyze and active response for intrusion

An intrusion detection system on network security for web application

For the last 15 years, significant amount of resources are invested to enhance the security at system and network level, such as firewalls, IDS, anti-virus, etc. IT infrastructure tends to be more and more secure than ever before. As an ever-increasing number of businesses move to take advantage of the Internet, web applications are becoming more prevalent and increasingly more sophisticated, and as such they are critical to almost all major online businesses. The very nature of web applications, their abilities to collect, process and disseminate information over the Internet, exposes thern to rnalicious hackers. However, the traditional security solutions such as firewall, network and host IDS, do not provide comprehensive protection against the attacks common in the web applications. The thesis concentrates on the research of an advanced intrusion detection framework. An intrusion detection framework was designed which works along with any custom web application to collect and analyze HTTP traffic with various advanced algorithms. Two intrusion detection algorithms are tested and adopted in the framework. Pattern Matching is the most popular intrusion detection technology adopted by most of the commercial intrusion detection system. Behavior Modeling is a new technology that can dynamically adapt the detection algorithms in accordance with the application behavior. The combination of the two intrusion technologies has dramatically reduced false positive and false negative alarms. Moreover, a Servlet filter-based Web Agent is used to capture HTTP request. An isolated Response Module is developed to execute pre-defined action according to the analysis result. A database is involved to provide persistence support for the framework. Also, several simulation experiments are developed for evaluating the efficiency of detecting capability.\ud _____________________________________________________________________________

A prototype for network intrusion detection system using danger theory

Network Intrusion Detection System (NIDS) is considered as one of the last defense mechanisms for any organization. NIDS can be broadly classified into two approaches: misuse-based detection and anomaly-based detection. Misuse-based intrusion detection builds a database of the well-defined patterns of the attacks that exploit weaknesses in systems and network protocols, and uses that database to identify the intrusions. Although this approach can detect all the attacks included in the database, it leads to false negative errors where any new attack not included in that database can’t be detected. The other approach is the anomaly-based NIDS which is developed to emulate the Human Immune System (HIS) and overcome the limitation of the misuse-based approach. The anomaly-based detection approach is based on Negative Selection (NS) mechanism. NS is based on building a database of the normal self patterns, and identifying any pattern not included in that database as a non-self pattern and hence the intrusion is detected. Unfortunately, NS concept has also its drawbacks. Although any attack pattern can be detected as a non-self pattern and this leads to low false negative rate, non-self patterns would not necessarily indicate the existence of intrusions. So, NS has a high false positive error rate caused from that assumption. Danger Theory (DT) is a new concept in HIS, which shows that the response mechanism in HIS is more complicated and beyond the simple NS concept. So, is it possible to utilize the DT to minimize the high false positive detection rate of NIDS? This paper answers this question by developing a prototype for NIDS based on DT and evaluating that prototype using DARPA99 Intrusion Detection dataset

Hierarchical Design Based Intrusion Detection System For Wireless Ad hoc Network

In recent years, wireless ad hoc sensor network becomes popular both in civil and military jobs. However, security is one of the significant challenges for sensor network because of their deployment in open and unprotected environment. As cryptographic mechanism is not enough to protect sensor network from external attacks, intrusion detection system needs to be introduced. Though intrusion prevention mechanism is one of the major and efficient methods against attacks, but there might be some attacks for which prevention method is not known. Besides preventing the system from some known attacks, intrusion detection system gather necessary information related to attack technique and help in the development of intrusion prevention system. In addition to reviewing the present attacks available in wireless sensor network this paper examines the current efforts to intrusion detection system against wireless sensor network. In this paper we propose a hierarchical architectural design based intrusion detection system that fits the current demands and restrictions of wireless ad hoc sensor network. In this proposed intrusion detection system architecture we followed clustering mechanism to build a four level hierarchical network which enhances network scalability to large geographical area and use both anomaly and misuse detection techniques for intrusion detection. We introduce policy based detection mechanism as well as intrusion response together with GSM cell concept for intrusion detection architecture.Comment: 16 pages, International Journal of Network Security & Its Applications (IJNSA), Vol.2, No.3, July 2010. arXiv admin note: text overlap with arXiv:1111.1933 by other author

DCDIDP: A distributed, collaborative, and data-driven intrusion detection and prevention framework for cloud computing environments

With the growing popularity of cloud computing, the exploitation of possible vulnerabilities grows at the same pace; the distributed nature of the cloud makes it an attractive target for potential intruders. Despite security issues delaying its adoption, cloud computing has already become an unstoppable force; thus, security mechanisms to ensure its secure adoption are an immediate need. Here, we focus on intrusion detection and prevention systems (IDPSs) to defend against the intruders. In this paper, we propose a Distributed, Collaborative, and Data-driven Intrusion Detection and Prevention system (DCDIDP). Its goal is to make use of the resources in the cloud and provide a holistic IDPS for all cloud service providers which collaborate with other peers in a distributed manner at different architectural levels to respond to attacks. We present the DCDIDP framework, whose infrastructure level is composed of three logical layers: network, host, and global as well as platform and software levels. Then, we review its components and discuss some existing approaches to be used for the modules in our proposed framework. Furthermore, we discuss developing a comprehensive trust management framework to support the establishment and evolution of trust among different cloud service providers. © 2011 ICST

An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions

Today\u27s predominantly-employed signature-based intrusion detection systems are reactive in nature and storage-limited. Their operation depends upon catching an instance of an intrusion or virus after a potentially successful attack, performing post-mortem analysis on that instance and encoding it into a signature that is stored in its anomaly database. The time required to perform these tasks provides a window of vulnerability to DoD computer systems. Further, because of the current maximum size of an Internet Protocol-based message, the database would have to be able to maintain 25665535 possible signature combinations. In order to tighten this response cycle within storage constraints, this thesis presents an Artificial Immune System-inspired Multiobjective Evolutionary Algorithm intended to measure the vector of trade-off solutions among detectors with regard to two independent objectives: best classification fitness and optimal hypervolume size. Modeled in the spirit of the human biological immune system and intended to augment DoD network defense systems, our algorithm generates network traffic detectors that are dispersed throughout the network. These detectors promiscuously monitor network traffic for exact and variant abnormal system events, based on only the detector\u27s own data structure and the ID domain truth set, and respond heuristically. The application domain employed for testing was the MIT-DARPA 1999 intrusion detection data set, composed of 7.2 million packets of notional Air Force Base network traffic. Results show our proof-of-concept algorithm correctly classifies at best 86.48% of the normal and 99.9% of the abnormal events, attributed to a detector affinity threshold typically between 39-44%. Further, four of the 16 intrusion sequences were classified with a 0% false positive rate