Signature-based anomaly intrusion detection using integrated data mining classifiers

As the influence of Internet and networking technologies as communication medium advance and expand across the globe, cyber attacks also grow accordingly. Anomaly detection systems (ADSs) are employed to scrutinize information such as packet behaviours coming from various locations on network to find those intrusive activities as fast as possible with precision. Unfortunately, besides minimizing false alarms; the performance issues related to heavy computational process has become drawbacks to be resolved in this kind of detection systems. In this work, a novel Signature-Based Anomaly Detection Scheme (SADS) which could be applied to scrutinize packet headers' behaviour patterns more precisely and promptly is proposed. Integrating data mining classifiers such as Naive Bayes and Random Forest can be utilized to decrease false alarms as well as generate signatures based on detection results for future prediction and reducing processing time. Results from a number of experiments using DARPA 1999 and ISCX 2012 benchmark dataset have validated that SADS own better detection capabilities with lower processing duration as contrast to conventional anomaly-based detection method

A survey of intrusion detection techniques in Cloud

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks

Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS) such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusiondetection model by integrating the principal component analysis (PCA) and support vector machine (SVM). The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor (C) and kernel parameter gamma (γ), thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed

Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine

Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Feature selection and visualization techniques for network anomaly detector

Intrusion detection systems have been widely used as burglar alarms in the computer security field. There are two major types of detection techniques: misuse detection and anomaly detection. Although misuse detection can detect known attacks with lower false positive rate, anomaly detection is capable of detecting any new or varied attempted intrusion as long as the attempted intrusions disturb the normal states of the systems. The network anomaly detector is employed to monitor a segment of network for any suspicious activities based on the sniffered network traffic. The fast speed of network and wide use of encryption techniques make it almost unpractical to read payload information for the network anomaly detector. This work tries to answer the question: What are the best features for network anomaly detector? The main experiment data sets are from 1999 DARPA Lincoln Library off-line intrusion evaluation project since it is still the most comprehensive public benchmark data up to today. Firstly, 43 features of different levels and protocols are defined. Using the first three weeks as training data and last two weeks as testing data, the performance of the features are testified by using 5 different classifiers. Secondly, the feasibility of feature selection is investigated by employing some filter and wrapper techniques such as Correlation Feature Selection, etc. Thirdly, the effect of changing overlap and time window for the network anomaly detector is investigated. At last, GGobi and Mineset are utilized to visualize intrusion detections to save time and effort for system administrators. The results show the capability of our features is not limited to probing attacks and denial of service attacks. They can also detect remote to local attacks and backdoors. The feature selection techniques successfully reduce the dimensionality of the features from 43 to 10 without performance degrading. The three dimensional visualization pictures provide a straightforward view of normal network traffic and malicious attacks. The time plot of key features can be used to aid system administrators to quickly locate the possible intrusions