Number Of Intrusion Detection Systems Research Articles

Review of Counter-Based Digester ACK's Effectiveness in MANET Packet Loss Prevention

a wireless peer-to-peer network with a mobile component adaptability and self-formation characteristics that lacks infrastructure. The network is ideal for use in emergency and disaster assistance applications that need reliable communication.. Finding the channel between communicative entities and sending information across it enables communication. There are several routing protocols in the literature that are developed to achieve reliable communication. Routing protocols are susceptible to packet losses because they consider network nodes to be receptive to communication. Due to malicious behavior or a shortage of resources, nodes trash packets. To address the problem, a number of intrusion detection systems have been suggested in the literature. In order to prevent packet losses in MANET, the newly created counter-based digested Acknowledgement (CDACK) was developed. This study's objective is to assess the CDACK protocol's performance in relation to current MANET intrusion detection systems. The findings demonstrate that, in terms of avoiding packet dropping nodes from the routing path, the CDACK protocol is a good fit for MANET..

Analyzing ML-Based IDS over Real-Traffic

The rapid growth of computer networks has caused a significant increase in malicious traffic, promoting the use of Intrusion Detection Systems (IDSs) to protect against this ever-growing attack traffic. A great number of IDS have been developed with some sort of weaknesses and strengths. Most of the development and research of IDS is purely based on simulated and non-updated datasets due to the unavailability of real datasets, for instance, KDD '99, and CIC-IDS-18 which are widely used datasets by researchers are not sufficient to represent real-traffic scenarios. Moreover, these one-time generated static datasets cannot survive the rapid changes in network patterns. To overcome these problems, we have proposed a framework to generate a full feature, unbiased, real-traffic-based, updated custom dataset to deal with the limitations of existing datasets. In this paper, the complete methodology of network testbed, data acquisition and attack scenarios are discussed. The generated dataset contains more than 70 features and covers different types of attacks, namely DoS, DDoS, Portscan, Brute-Force and Web attacks. Later, the custom-generated dataset is compared to various available datasets based on seven different factors, such as updates, practical-to-generate, realness, attack diversity, flexibility, availability, and interoperability. Additionally, we have trained different ML-based classifiers on our custom-generated dataset and then tested/analyzed it based on performance metrics. The generated dataset is publicly available and accessible by all users. Moreover, the following research is anticipated to allow researchers to develop effective IDSs and real traffic-based updated datasets.

Data Mining Techniques for Providing Network Security through Intrusion Detection Systems: a Survey

Intrusion Detection Systems are playing major role in network security in this internet world. Many researchers have been introduced number of intrusion detection systems in the past. Even though, no system was detected all kind of attacks and achieved better detection accuracy. Most of the intrusion detection systems are used data mining techniques such as clustering, outlier detection, classification, classification through learning techniques. Most of the researchers have been applied soft computing techniques for making effective decision over the network dataset for enhancing the detection accuracy in Intrusion Detection System. Few researchers also applied artificial intelligence techniques along with data mining algorithms for making dynamic decision. This paper discusses about the number of intrusion detection systems that are proposed for providing network security. Finally, comparative analysis made between the existing systems and suggested some new ideas for enhancing the performance of the existing systems.

Intrusion Detection Systems of ICMPv6-based DDoS attacks

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are thorny and a grave problem of today’s Internet, resulting in economic damages for organizations and individuals. DoS and DDoS attacks that are using Internet Control Message Protocol version six (ICMPv6) messages are the most common attacks against the Internet Protocol version six (IPv6). They are common because of the necessary inclusion of the ICMPv6 protocol in any IPv6 network to work properly. Intrusion Detection Systems (IDSs) of the Internet Protocol version four (IPv4) can run in an IPv6 environment, but they are unable to solve its security problems such as ICMPv6-based DDoS attacks due to the new characteristics of IPv6, such as Neighbour Discovery Protocol and auto-configuration addresses. Therefore, a number of IDSs have been either exclusively proposed to detect IPv6 attacks or extended from existing IPv4 IDSs to support IPv6. This paper reviews and classifies the detection mechanisms of the existing IDSs which are either proposed or extended to tackle ICMPv6-based DDoS attacks. To the best of the authors’ knowledge, it is the first review paper that explains and clarifies the problems of ICMPv6-based DDoS attacks and that classifies and criticizes the existing detection.

INTRUSION RECOGNITION USING NEURAL NETWORKS

Intrusion detection techniques are of great importance for computer network protecting because of increasing the number of remote attack using TCP/IP protocols. There exist a number of intrusion detection systems, which are based on different approaches for anomalous behavior detection. This paper focuses on applying neural networks for attack recognition. It is based on multilayer perceptron. The 1999 KDD Cup data set is used for training and testing neural networks. The results of experiments are discussed in the paper.

Intrusion Detection Architecture Utilizing Graphics Processors

With the thriving technology and the great increase in the usage of computer networks, the risk of having these network to be under attacks have been increased. Number of techniques have been created and designed to help in detecting and/or preventing such attacks. One common technique is the use of Intrusion Detection Systems (IDS). Today, number of open sources and commercial IDS are available to match enterprises requirements. However, the performance of these systems is still the main concern. This paper examines perceptions of intrusion detection architecture implementation, resulting from the use of graphics processor. It discusses recent research activities, developments and problems of operating systems security. Some exploratory evidence is presented that shows capabilities of using graphical processors and intrusion detection systems. The focus is on how knowledge experienced throughout the graphics processor inclusion has played out in the design of intrusion detection architecture that is seen as an opportunity to strengthen research expertise.

Integrating software specifications into intrusion detection

There exist a number of Intrusion Detection Systems (IDSs) that detect computer attacks based on some defined attack scenarios. The attack scenarios or security requirements in some of these IDSs are specified in attack specification languages that are different from software specification languages. The use of two different languages for software specification and attack specification may generate redundant and conflicting requirements. The advantage of using the same language for both functional specifications and attacks specifications is that software designers can address the two different issues without learning two types of languages. We present a method of integrating Abstract State Machine Language (AsmL) and Unified Modeling Language (UML) state charts that are extended finite state machine based software specification languages, with an open source IDS Snort. This work provides AsmL and UML users an IDS that they can use without knowing how to write Snort rules. We automatically translate attack scenarios written in AsmL and UML state charts into Snort rules with context information. The original Snort is modified so that it can use the rules automatically generated by the translator. Adding context information to Snort rules improves the detection capability of Snort. To show the efficacy of the presented approach, we have built a prototype and evaluated it using a number of well-known attack scenarios.