Analysis and Design for Intrusion Detection System Based on Data Mining

Network and host Intrusion Detection Systems (IDS) have become a standard component in security infrastructures. As the action of intrusion represents variable, complicated, and uncertainty characteristic, they face so many problems to resolve for intrusion detection. Each approach has its strengths and weaknesses. We propose a hybrid IDS, which combines network and host IDS, with anomaly and misuse detection mode, utilizes auditing programs to extract an extensive set of features that describe each network connection or host session, and applies data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities. We use an association rule to track all relevant data dependency rule sets for different access roles using a hierarchical structure. We identify malicious transactions from the transaction logs in the database using the data dependency rule sets. These rule sets are continuously updated and stored in a repository. The optimized algorithm actually improves the performance of IDS. Our approach is shown to reduce data access bottlenecks, and ensures minimal manual intervention for maintaining a secure database.

Traffic classification is an automated technique that divides computer network traffic into several categories depending on different factors like protocol or port number. In a complicated context, traffic categorization is an important tool for network and system security. A monitoring system called intrusion detection looks for abnormal activity and sends out notifications. In order to safeguard a system from network-based attacks, Network Intrusion Detection Systems (NIDS) play a crucial role in monitoring and analyzing network traffic. Active and passive intrusion detection systems (IDS), network intrusion detection systems (NIDS), host intrusion detection systems (HIDS), knowledge-based (signature-based) IDS, and behaviorbased (anomaly-based) IDS are some of the numerous types of intrusion detection systems (IDS). Passive IDS is just designed to monitor and analyze network traffic behaviour and notify an operator of potential vulnerabilities and attacks, whereas Active IDS is also known as Intrusion Detection and Prevention System. A network's malicious traffic is identified using a network-based intrusion detection system (NIDS). A host-based IDS monitors system activity and seeks for indications of abnormal behaviour. For networks with unidentified traffic, the intrusion detection system designed using flow and payload statistical characteristics and clustering approach needs additional clusters. The present intrusion detection system however is affected by false alarm rate, poor detection rate, imbalanced datasets and response time which lead to misclassification of intrusions in various scenarios. Hence, there is a requirement for developing an automated intrusion detection system that works well in different scenarios. The proposed system uses supervised and unsupervised intrusion detection and classification methods to increase the classification accuracy. To categorize the intrusions, dimensionality reduction strategies are used in conjunction with the classification procedure of logistic regression. Performance of intrusion detection system using PCA as dimensionality reduction algorithm has been evaluated with different classifiers such as Logistic Regression (LR), K-Nearest Neighbors (K-NN), Random Forest (RF), Support Vector Machine (Kernel SVM), Decision Tree (DT) using CIC IDS 2022 dataset. An automated way to detect intrusions has been proposed with cluster formation using adaptive weight butterfly optimization algorithm.

Network security is becoming an increasingly important issue, since the rapid development of the Internet. Network Intrusion Detection System (IDS), as the main security defending technique, is widely used against such malicious attacks. Data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. Association rules and sequence rules are the main technique of data mining for intrusion detection. Considering the classical Apriori algorithm with bottleneck of frequent itemsets mining, we propose a Length-Decreasing Support to detect intrusion based on data mining, which is an improved Apriori algorithm. Experiment results indicate that the proposed method is efficient.

The security over data is now a major concern for all applications. Attacks over data are going to be increasing day by day. Therefore, there is a need of security mechanism over all devices responsible for transfer of data over the network. An Intrusion Detection System (IDS) has been designed in order to detect different types of attacks over the system. IDS may be categorized Network Intrusion Detection System (NIDS) and Host Intrusion Detection System (HIDS). NIDS and HIDS are employed by the user depending on the requirement such as whether the user aims to find attacks over the whole network or just over a host. An IDS best works over Software Defined Networks (SDN) rather than traditional networks. Many of today’s applications reside over SDN. SDN is preferred over traditional because of its flexibility and agile property. This chapter mainly introduces various algorithms of intrusion detection like support vector machine (SVM), random forest (RF), K-means, Principal Component Analysis (PCA) and Self-Organizing Map (SOM), which are basically machine learning (ML) algorithms. ML algorithms may be supervised, unsupervised and semi-supervised learning algorithms. Besides ML algorithms, this chapter also introduces some deep learning algorithms used for intrusion detection. Examples are Recurrent Neural Network (RNN) and Deep Belief Network (DBN) etc.

The high computational power of graphics processing units (GPU) is used for several purposes nowadays. Factoring integers, computing discrete logarithms, and pattern matching in network intrusion detection systems (IDS) are popular tasks in the field of information security where GPUs are used for acceleration. GPUs are commodity components and are widely available in computer systems which would make them an ideal platform for a wide-spread IDS. We investigate the feasibility to use current GPUs for asynchronous host intrusion detection as proposed in a former work and come to the conclusion that several constraints of GPUs limit the use for concurrent and asynchronous off-CPU processing in host IDSs. GPUs have restrictions in terms of continuity, asynchronism, and unrestricted access to perform this task. We propose an observation mechanism and discuss current constraints on autonomous use of standard GPU components for intrusion detection. Finally, we come to the conclusion that several modifications to graphics cards are necessary to enable our approach.

Chapter 2 - Cisco Intrusion Detection

Chapter 9 - Implementing Intrusion Detection Systems

Security has a significant influence in network management. One of the most common way to secure information in the computer from malicious use is IDS Intrusion detection system(IDS) is most prominent to secure a computer and network against intrusion. IDSs primarily intended to preserve the availability, confidentiality and Integrity(CAI)of network and computer. IDS can be broadly classified in two categories: Network intrusion detection system (NIDS) and Host intrusion detection system(HIDS). NIDS is main part of any network security architecture, which monitors network traffic for predefined suspicious activity or patterns and alert system administrators. Nowadays, many IDSs tools are available such as commercial as well as open source tools. Open source tools promotes a global access through free license. In paper we found study of three popular NIDS tools : Snort, Suricata, Bro.

Accurate identification of network intrusions is one of the biggest challenges of Network Intrusion Detection (NID) systems. In recent years Machine learning classification techniques have been used to precisely identify network intrusion. However, the multi class distribution in network intrusion detection system has found to be highly skewed, leading to classification accuracy problem due to class imbalance data set. The work presented in this paper not only explores the role of the attribute selection in improving classification accuracy but also investigates the problem of class imbalance using the Synthetic Minority Over-sampling (SMOTE) and under sampling of major classes. The classification performance is then evaluated over several types of classifiers. The outcome of this work is that for the class imbalance data set the under-sampling technique is more effective than SMOTE in detecting minor classes. It has also found during this research work that the decision tree algorithms (JRIP) and Naïve Bayes are more accurate classifiers as compared to the Radial basis neural network and support vector machine. However no single algorithm can be used for the classification of multiclass and it is proposed in this research work that combination of classifier consisting of Naïve Bayes and JRIP could be used for the classification of minor classes in an imbalance class data set of intrusion detection system.

The Internet of Things (IoT) introduces significant security vulnerabilities, raising concerns about cyber-attacks. Attackers exploit these vulnerabilities to launch distributed denial-of-service (DDoS) attacks, compromising availability and causing financial damage to digital infrastructure. This study focuses on mitigating DDoS attacks in corporate local networks by developing a model that operates closer to the attack source. The model utilizes Host Intrusion Detection Systems (HIDS) to identify anomalous behaviors in IoT devices and employs network-based intrusion detection approaches through a Network Intrusion Detection System (NIDS) for comprehensive attack identification. Additionally, a Host Intrusion Detection and Prevention System (HIDPS) is implemented in a fog computing infrastructure for real-time and precise attack detection. The proposed model integrates NIDS with federated learning, allowing devices to locally analyze their data and contribute to the detection of anomalous traffic. The distributed architecture enhances security by preventing volumetric attack traffic from reaching internet service providers and destination servers. This research contributes to the advancement of cybersecurity in local network environments and strengthens the protection of IoT networks against malicious traffic. This work highlights the efficiency of using a federated training and detection procedure through deep learning to minimize the impact of a single point of failure (SPOF) and reduce the workload of each device, thus achieving accuracy of 89.753% during detection and increasing privacy issues in a decentralized IoT infrastructure with a near-real-time detection and mitigation system.

In the cyber domain, situational awareness of the critical assets is extremely important. For achieving comprehensive situational awareness, accurate sensor information is required. An important branch of sensors are Intrusion Detection Systems (IDS), especially anomaly based intrusion detection systems applying artificial intelligence or machine learning for anomaly detection. This millennium has seen the transformation of industries due to the developments in data based modelling methods. The most crucial bottleneck for modelling the IDS is the absence of publicly available datasets compliant to modern equipment, system design standards and cyber threat landscape. The predominant dataset, the KDD Cup 1999, is still actively used in IDS modelling research despite the expressed criticism. Other, more recent datasets, tend to record data only either from the perimeters of the testbed environment’s network traffic or from the effects that malware has on a single host machine. Our study focuses on forming a set of requirements for a holistic Network and Host Intrusion Detection System (NHIDS) dataset by reviewing existing and studied datasets within the field of IDS modelling. As a result, the requirements for state-of-the-art NHIDS dataset are presented to be utilised for research and development of NHIDS applying machine learning and artificial intelligence.

The risk and severity of network intrusion has clearly received great attention in the last decade. Meanwhile, machine learning methods have been widely employed in the area of cybersecurity. This paper introduces the network intrusion attacks and detection systems and gives an overview of literature on various machine learning models to achieve network intrusion detection, including logistic regression, k-nearest neighbors, neural networks, random forest, decision tree, and k-means clustering. We find that as the dataset gets larger, the machine learning methods yield better performance significantly. Furthermore, we discuss the prospects mentioned in the literature and put forward some key prominent future research directions in network intrusion detection systems.

Data Mining Techniques for Network Intrusion Detection and Prevention Systems

The importance of accurate intrusion detection is growing tremendously as the malicious network traffic activities have also grown significantly. Intrusion Detection Systems (IDSs) provide automatic detection for security violation like denial of service (DoS), virus, port scans, buffer overflows, CGI attacks, clogging or flooding etc. For network and host based systems, the most widely used and effective approach is data analysis with signature-based detection methods. Thus, the success of the detection system depends on the real appearance of the security violation, detection of the violation and response time. We are working on highly efficient real time network intrusion detection systems (NIDS) which will solve the detection efficiency problem such as real time detection rate, false positive etc in distributed environments. In this work, we propose a concept IDS to investigate the experimental performance of Snort based NIDS. We have used an open source network intrusion detection and prevention system Snort to implement our two different indexing methods. We used Snort version 2.9.7.5 which has almost 26k Snort rules and very efficient for online network auditing. We implemented prefix and random indexing method to all Snort rules to create primary patterns that reduce packet inspection time. Since all highly sensitive positive alerts need instant action from network administrator, our concept IDS also reduces the false positive (wrong alert) rate even at high network traffic. By combining the concept IDS and a data mining technique indexing will improve the accuracy of the intrusion detection in real time. We also present our experimental data and results of our IDS prototype.

With the vigorous development of the Internet, more and more people use the network, the computer network to provide convenience, bring benefits, but also make the human face a huge challenge of information security. In the form of the development of modern information technology, a safe network system should not only means of defense, but it is necessary to have a means of firewall, defense, but also be able to real-time monitoring of network security, attack and counter attack, the network intrusion detection system. On the current situation of network security is all kinds of hacker events, network crime, virus has been in the stage of escalating, large portal website, government website, enterprise website, and individual users are these illegal criminals invade object, intrusion types and different, means rich utterly impossible, network security defend without delay. So intrusion detection system came into being. With the development of network intrusion detection technology, so that people have been focusing on the application of data mining in intrusion detection technology, if we can improve the data mining technology into the network intrusion detection, according to the specific characteristics of the intrusion detection system, the basic principle of applying data mining, optimize their combination, this will improve performance of intrusion detection system. This article will use the data mining technology to the network intrusion detection technology in the present situation and the future development trend is discussed.