Bayesian event classification for intrusion detection

An intrusion detection system (IDS) attempts to identify attacks by comparing collected data to predefined signatures known to be malicious (signature-based IDS) or to a model of legal behaviour (anomaly-based IDS). Anomaly-based approaches have the advantage of being able to detect previously unknown attacks, but they suffer from the difficulty of building robust models of acceptable behaviour which may result in a large number of false alarms. Two reasons for the large number of false alarms, caused by incorrect classification of events in current systems, one is the simplistic aggregation of model outputs inthe decision phase. The other reason is the lack of integration of additional information into the decision process. To mitigate these shortcomings, this paper proposes a two stratum Bayesian networks based anomaly detection and decision model for intrusion detection system. Bayesian networks improve the aggregation of outputs, such as empirical data and allow one to seamlessly incorporate additional information. Experimental results clearly demonstrate the efficiency of our approach to improve the accuracy of the intrusion detection and decision process in an anomaly based IDS.

Malware authors are continuously developing crime toolkits. This has led to the situation of zero-day attacks, where malware harm computer systems despite the protection from existing Intrusion Detection Systems (IDSs). We propose an Intelligent Intrusion Detection and Prevention System (IIDPS) approach that combines the Signature based Intrusion Detection system (SIDS), Anomaly based Intrusion Detection System (AIDS) and Response Intrusion Detection System (RIDS). We used a risk assessment approach to determine an appropriate response action against each attack event. We also demonstrated the IIDPS make the detection and prevention of malware more effective.

To defend a network system from security risks, intrusion detection systems (IDSs) have been playing an important role in recent years. There are two types of detection algorithms of IDSs: misuse detection and anomaly detection. Because misuse detection is based on a signature which is created from the features of attack traffic by security experts, it can achieve accurate and stable detection. However, its weakness is the difficulty of detecting new attacks (i.e., 0-day attack), and the cost of maintaining the latest signature version. Thinking of the increase of the skillful intrusion, e.g., intrusion showing similar access behavior to normal, misuse detection cannot handle these critical attacks, which results in a large number of false alarms. To cope with these problems, we present a clustering algorithm based on an unsupervised anomaly detection. We evaluated our system using Kyoto2006+ data set and KDD Cup 1999 data set. Evaluation results show that our approach achieved a higher detection rate in the region of very low false positive rate and real-time preprocessing capability.

Internet attacks have become more sophisticated over time, and they can now circumvent basic security measures like antivirus scanners and firewalls. Identifying, detecting, and avoiding breaches is essential for network security in today's computing world. Adding an extra layer of defence to the network infrastructure through an Intrusion Detection System is one approach to improve network security. Anomaly-based or signature-based detection algorithms are used by existing Intrusion Detection Systems (IDS). Signature-based IDS, for example, detects attacks based on a set of signatures but is unable to detect zero day attacks. In contrast, anomaly-based IDS analyses deviations in behaviour and can detect unexpected attacks. This study suggests designing and developing an Advanced signature-based Intrusion Detection System for Improved Performance by Combining Signature and Anomaly-Based Approaches. It includes three essential stages, first Signature-based IDS used for checking the attacks from the Signature Ruleset using Decision Tree received accuracy 96.96%, and the second stage Anomaly-based IDS system used Deep learning technique ResNet50. The model relies on ResNet50, a Convolutional Neural Network with 50 layers that received an accuracy of 97.25%. By classifying all network packets into regular and attack categories, the combination of both detect known and unknown attacks is the third stage and generates signature from anomaly-based IDS. It gives the accuracy of 98.98% for detection of intrusion. Here findings show that the suggested intrusion detection system may efficiently detect real-world intrusions.KeywordsNetwork securityIntrusion detection systemMachine learningDecision tree

The acceptability and usability of Intrusion Detection Systems get seriously affected with the data skewness in network traffic. A large number of false alarms mean a lot in terms of the acceptability of Intrusion Detection Systems. The reason for the increase in false alerts is that the normal traffic abound. Even with highly accurate Intrusion Detection Systems, the effective detection rate of the minority attack types will be unacceptably low and those attack types are often the most serious ones. Thus high accuracy is not necessarily an indicator of high model quality, and therein lies the accuracy paradox of predictive analytics. The cost of missing an attack is higher than the cost of false alarms. The data-dependent sensor fusion architecture presented in this paper learns from the data and then appropriately gives weighting to the decisions of various Intrusion Detection Systems. The fusion enriches these weighted decisions to provide a single decision, which is better than those of the existing Intrusion Detection Systems. This method reduces the false positive rate and improves the overall detection rate and also the detection rate of minority class types in particular.

Detecting and identifying intrusions in a network is a challenging research area in the network security domain. Intrusion detection plays an essential role in computer network security since long. An Intrusion Detection System (IDS) is mainly used to detect an unauthorized access to a computer system or network. Moreover, it is capable to detect all types of malicious and harmful attacks in a network. The drawbacks of existing IDS are it can detect only the known attacks and it produces a large number of false alarms due to the unpredictable behavior of users and networks. It also requires extensive training sets in order to characterize the normal behavior of the nodes. In order to overcome these issues, an integration of Hidden Markov Model (HMM)–Relevance Vector Machine (RVM) algorithm namely, Probabilistic Relevance Classification (PRC) is proposed to detect intrusions in Supervisory Control and Data Acquisition (SCADA) network. Here, the power system attack dataset is used to detect the attacks in an SCADA network. In the preprocessing stage, the given data is preprocessed to segregate the relays as R1, R2, R3n and R4. Each relay contains the date, timestamp, control panel log report, relay log report, snort log report, marker, fault location, and load condition information. Then, the Boyer–Moore (BM) technique is employed to perform the string matching operation. After that, the PRC technique is implemented to classify the attack as known or unknown. The novelty of this paper is it manually trains the data and features for unknown attacks. The main intention of this work is to reduce the set of features, amount of database, and to increase the detection rate. The experimental results evaluate the performance in terms of False Acceptance Rate (FAR), False Rejection Rate (FRR), Genuine Acceptance Rate (GA), sensitivity, specificity, accuracy, error rate, and recall.

Two main approaches for network intrusion detection are misuse detection [6] and anomaly detection [11]. The limitation of the misuse approach is that cannot effectively detect new patterns of intrusions that are not precisely encoded in the system [11]. The anomaly detection approach usually produces a large number of false alarms [1, 7]. In addition, anomaly detection requires intensive computations on a large amount of training data to characterize normal behavior patterns.In this paper, we try to apply interval technology to enhance network intrusion detection systems (IDS). By storing network state data into interval valued bi-temporal database, we better sample the stream of network states. We represent the likelihood of intrusions associated with an m x n interval valued rule matrix that can be obtained from the database with relatively low computational complexity. By grouping nearby patterns with intervals, we may significantly reduce false alarms. The O(n) computational cost of maintaining the rules makes it possible to integrate the IDS with network management systems for almost real-time automatic network control. Our probabilistic approach with the rule matrix model can be further applied to study the pattern evolution of network intrusions.

With the increasing rate and types of cyber attacks against information systems and communication infrastructures, many tools are needed to detect and mitigate against such attacks, for example, Intrusion Detection Systems (IDSs). Unfortunately, traditional Signature‐based IDSs (SIDSs) perform poorly against previously unseen adversarial attacks. Anomaly‐based IDSs (AIDSs) use Machine Learning (ML) and Deep Learning (DL) approaches to overcome these limitations. However, AIDS performance can be poor when trained on imbalanced datasets. To address the challenge of AIDS performance caused by these unbalanced training datasets, generative adversarial models are proposed to obtain adversarial attacks from one side and analyse their quality from another. According to extensive usage and reliability criteria for generative adversarial models in different disciplines, Generative Adversarial Networks (GANs) , Bidirectional GAN (BiGAN) , and Wasserstein GAN (WGAN) are employed to serve AIDS. The authors have extensively assessed their abilities and robustness to deliver high‐quality attacks for AIDS. AIDSs are constructed, trained, and tuned based on these models to measure their impacts. The authors have employed two datasets: NSL‐KDD and CICIDS‐2017 for generalisation purposes, where ML and DL approaches are utilised to implement AIDSs. Their results show that the WGAN model outperformed GANs and BiGAN models in binary and multiclass classifications for both datasets.

One of the most straightforward techniques for detecting changes in an image involves forming the difference between a test image and a reference image. Unfortunately, such a technique can give rise to a large number of false alarms due to the statistical variability of the underlying pixel values, as has been well established within the radar community over the years. One method for dealing with this large number of false alarms involves forming the ratio, rather than the difference, of two synthetic aperture radar (SAR) images. We introduce a modified version of the standard differencing technique to overcome problems associated with pixel value variability. The new (modified) differencing approach utilizes assumptions about the statistics of the image background and the object being sought (target) to reduce the number of false alarms due to highly variable background (clutter) regions, and it includes the standard ratio test as a special case. In fact, we find that the modified difference approach can also be viewed as a modified version of the ratio test with a threshold that varies as a function of the background clutter radar cross section (RCS). We also present an abridged, albeit suboptimal, version of this approach that eliminates assumptions regarding the target's probability distribution, and we analyze both of the approaches. We then compare these results with those obtained with a standard ratio test, and illustrate how the modified difference test reduces to the ratio test under certain operating conditions. The abridged version of the modified approach is applied to high resolution synthetic aperture radar imagery and compared with results obtained with the classical differencing technique, and following this, the modified difference technique is compared with the standard ratio test. Results suggest that under appropriate conditions the abridged, modified technique can successfully detect changes without the need for any image segmentation.

With the ever increasing use of computers for critical systems, computer security, the protection of data and computer systems from intentional, malicious intervention, is attracting much attention. Among the methods for defence, intrusion detection , i.e. the application of a tool to help the operator identify ongoing or already perpetrated attacks has been the subject of considerable research in the past ten years. A key problem with current intrusion detection systems is the high number of false alarms they produce. This thesis presents research into why false alarms are and will remain a problem and proposes to apply results from the field of information visualisation to the problem of intrusion detection. This was thought to enable the operator to correctly identify false (and true) alarms, and also aid the operator in identifying other operational characteristics of intrusion detection systems. Four different visualisation approaches were tried, mainly on data from web server access logs. Two direct approaches were tried; where the system puts the onus of identifying the malicious access requests on the operator by way of the visualisation. Two indirect approaches were also tried; where the state of two self learning automated intrusion detection systems were visualised to enable the operator to examine their inner workings. This with the hope that in doing so, the operator would gain an understanding of how the intrusion detections systems operated and whether that level of operation, and the quality of the output, was satisfactory. Several experiments were performed and many different attacks in web access data from publicly available web servers were found. The visualisation helped the operator either detect the attacks herself and more importantly the false alarms. It also helped her determine whether other aspects of the operation of the self learning intrusion detection systems were satisfactory.

An Intrusion Detection System (IDS) is a software used to monitor a single computer or a network of computers to combat network attacks, block access to certain websites, or otherwise the attack on network protocols. The dynamic and complicated nature of cyber attacks on computer systems is beyond the capabilities of the majority of current IDS solutions. By combating intrusion attempts, network security professionals ensure that the services can be accessed at all times. One of the tools at disposal to detect and categorize suspicious behaviour is IDS. For almost 40 years, researchers in both academia and industry have been proposing strategies for detecting and avoiding such security breaches, and even constructing systems to do so. Intrusion detection systems that rely on attack signatures examine network traffic for suspicious activity. Anomaly-based intrusion detection systems, on the other hand, build a model to tell good users apart from bad ones, allowing them to spot previously unseen attacks. Machine learning (ML) techniques are increasingly being employed as a means of categorizing normal and abnormal activities. Many other ML-based intrusion detection systems have been proposed in the past few years. The purpose of this work is to provide a critical overview of the state of the art in machine learning (ML)-based intrusion detection methods. Researchers focusing on ML-based systems that detect intrusions could use this survey as a supplement to previous generic surveys on intrusion detection and a source to recent work done in the area.

Intrusion detection systems play increasingly essential roles in modern society. With all various approaches used to Anomaly Detection, we investigate Artificial Immune Systems (AIS) to model anomaly detection. One new field of AIS is called Danger Theory. Since traditional Anomaly Detection Methods produce a large number of false alarms, AIS methods based on Danger Theory are suitable options for intrusion detection that can handle the above problem. So in this paper, we propose a novel method in danger theory field for anomaly detection and decrease the amount of system false alarms by using danger signals which are produced through a KNN classifier. Our experimental results show that this proposed method has low false alarm, by using the danger signals. Use of this danger signal that created by danger theory can improved our Artificial Immune Network and help us to achieve low false alarm in our results.

With the advent of anomaly-based intrusion detection systems, many approaches and techniques have been developed to track novel attacks on the systems.High detection rate of 98% at alow alarm rate of 1% can be achieved by using these techniques.Though anomaly-based approaches are efficient, signaturebased detection is preferred for mainstream implementation of intrusion detection systems.As a variety of anomaly detection techniques were suggested, it is difficult tocompare the strengths, weaknesses of these methods.The reason why industries don"t favor the anomaly-based intrusion detection methods can be well understood by validating the efficiencies of the all the methods.To investigate this issue, the current state of the experiment practice in the field of anomaly-based intrusion detection is reviewed and survey recent studies in this.This paper contains summarization study and identification of the drawbacks of formerly surveyed works..

Chapter 5 - Intrusion Prevention and Detection Systems

With the wide application of information technolo-gies in communication based train control (CB TC) systems, the information security risks it confronts to are ever increasing. It is very difficult to design an intrusion detection system (IDS) which can detect both known and unknown cyber attacks with high true positive rate (TPR) and low false positive rate (FPR), simultaneously. In this paper, we propose a hybrid intrusion detection system (RIDS) which is composed of a signature-based intrusion detection system (SIDS) and an anomaly-based intrusion detection system (AIDS). An improved stacking ensemble algorithm (ISEA) is proposed to enhance the TPR of the SIDS which uses weighted classification probabilities outputted by base-classifiers to generate meta-features for the meta-classifier. The FPR of the AIDS is significantly reduced through adopting the proposed false positive elimination strategy (FPES). In addition, a Bayesian optimization algorithm is used for hyper-parameters adjustment to optimize the overall performance of the RIDS. The proposed RIDS is evaluated using both a KDD99 dataset and a CBTC dataset collected on a hardware-in-the-Ioop simulation platform. For the KDD99 dataset, the TPR and FPR of the proposed RIDS are 98.6% and 1.3%, respectively. For the CBTC dataset, the TPR and FPR are 98.1% and 1.1 %, respectively. Based on the performance comparison among different RIDSs, it is can be concluded that the proposed RIDS is superior to the existing RIDS in both TPR and FPR.