A Survey of Intrusion Detection Techniques in Computer Network

As advances in the networking technology help to connect distant corners of the globe and as the Internet continues to expand its influence as a medium for communication, the threat from attackers and criminal enterprises has also grown accordingly. The increasing occurrence of network attacks is a very big issue to the network services. So, Intrusion Detection System has become a necessary component of network security. It is used for detection of many known and unknown network vulnerabilities in wired networks. While the Internet service for any purpose is used, normally who are attacking on the computer network is not known by us. Those network attacks can cause network services slow, temporarily unavailable, or down for a long period of time. The concern on this work is to perusal various methods of networking attacks detection and compare them against these methods by considering their pros and cons

Evaluating Sequential Combination of Two Light-Weight Genetic Algorithm based Solutions to Intrusion Detection

In this work we have presented a genetic algorithm approach for classifying normal connections and intrusions. We have created a serial combination of two light-weight genetic algorithm-based intrusion detection systems where each of the systems exhibits certain deficiency. In this way we have managed to mitigate the deficiencies of both of them. The model was verified on KDD99 intrusion detection dataset, generating a solution competitive with the solutions reported by the state-ofthe- art, while using small subset of features from the original set that contains forty one features. The most significant features were identified by deploying principal component analysis and multi expression programming. Furthermore, our system is adaptable since it permits retraining by using new data

Evaluating Machine Learning Classifiers for Hybrid Network Intrusion Detection Systems

Existing classifier evaluation methods do not fully capture the intended use of classifiers in hybrid intrusion detection systems (IDS), systems that employ machine learning alongside a signature-based IDS. This research challenges traditional classifier evaluation methods in favor of a value-focused evaluation method that incorporates evaluator-specific weights for classifier and prediction threshold selection. By allowing the evaluator to weight known and unknown threat detection by alert classification, classifier selection is optimized to evaluator values for this application. The proposed evaluation methods are applied to a Cyber Defense Exercise (CDX) dataset. Network data is processed to produce connection-level features, then labeled using packet-level alerts from a signature-based IDS. Seven machine learning algorithms are evaluated using traditional methods and the value-focused method. Comparing results demonstrates fallacies with traditional methods that do not consider evaluator values. Classifier selection fallacies are revealed in 2 of 5 notional weighting schemes and prediction threshold selection fallacies are revealed in 5 of 5 weighting schemes

Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection

Constant evolution and the emergence of new cyberattacks require the development of advanced techniques for defense. This paper aims to measure the impact of a supervised filter (classifier) in network anomaly detection. We perform our experiments by employing a hybrid anomaly detection approach in network flow data. For this purpose, we extended a state-of-the-art autoencoder-based anomaly detection method by prepending a binary classifier acting as a prefilter for the anomaly detector. The method was evaluated on the publicly available real-world dataset UGR'16. Our empirical results indicate that the hybrid approach does offer a higher detection rate of known attacks than a standalone anomaly detector while still retaining the ability to detect zero-day attacks. Employing a supervised binary prefilter has increased the AUC metric by over 11%, detecting 30% more attacks while keeping the number of false positives approximately the same

Spatiotemporal patterns and predictability of cyberattacks

A relatively unexplored issue in cybersecurity science and engineering is whether there exist intrinsic patterns of cyberattacks. Conventional wisdom favors absence of such patterns due to the overwhelming complexity of the modern cyberspace. Surprisingly, through a detailed analysis of an extensive data set that records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses, we successfully uncover intrinsic spatiotemporal patterns underlying cyberattacks, where the term "spatio" refers to the IP address space. In particular, we focus on analyzing {\em macroscopic} properties of the attack traffic flows and identify two main patterns with distinct spatiotemporal characteristics: deterministic and stochastic. Strikingly, there are very few sets of major attackers committing almost all the attacks, since their attack "fingerprints" and target selection scheme can be unequivocally identified according to the very limited number of unique spatiotemporal characteristics, each of which only exists on a consecutive IP region and differs significantly from the others. We utilize a number of quantitative measures, including the flux-fluctuation law, the Markov state transition probability matrix, and predictability measures, to characterize the attack patterns in a comprehensive manner. A general finding is that the attack patterns possess high degrees of predictability, potentially paving the way to anticipating and, consequently, mitigating or even preventing large-scale cyberattacks using macroscopic approaches

Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System

In this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the WEKA software to work with the classifiers on a well-known IDS (Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset of network attacks was created in a military network by MIT Lincoln Labs. Then we will discuss and experiment some of the hybrid AI (Artificial Intelligence) classifiers that can be used for IDS, and finally we developed a Java software with three most efficient classifiers and compared it with other options. The outputs would show the detection accuracy and efficiency of the single and combined classifiers used

Toward an efficient ontology-based event correlation in SIEM

Cooperative intrusion detection use several intrusion detection systems (IDS) and analyzers in order to build a reliable overview of the monitored system trough a central security information and event management system (SIEM). In such environment, the definition of a shared vocabulary describing the exchanged information between tools is prominent. Since these pieces of information are structured, we propose in this paper to use an ontological representation based on Description Logics (DLs) which is a powerful tool for knowledge representation. Moreover, DLs are able to ensure a decidable reasoning. An alert correlation prototype is presented using this ontology, and an illustrative attack scenario is carried out to show the usefulness of the proposed ontolog

An aspect-oriented model to monitor misuse

The efficacy of the aspect-oriented paradigm has been well established within several areas of software security as aspect-orientation facilitates the abstraction of these security-related tasks to reduce code complexity. The aim of this paper is to demonstrate that aspect-orientation may be used to monitor the information flows between objects in a system for the purposes of misuse detection. Misuse detection involves identifying behavior that is close to some previously defined pattern signature of a known intrusion

Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems

Most of the currently existing intrusion detection systems (IDS) use machine learning algorithms to detect network intrusion. Machine learning algorithms have widely been adopted recently to enhance the performance of IDSs. While the effectiveness of some machine learning algorithms in detecting certain types of network intrusion has been ascertained, the situation remains that no single method currently exists that can achieve consistent results when employed for the detection of multiple attack types. Hence, the detection of network attacks on computer systems has remain a relevant field of research for some time. The support vector machine (SVM) is one of the most powerful machine learning algorithms with excellent learning performance characteristics. However, SVM suffers from many problems, such as high rates of false positive alerts, as well as low detection rates of rare but dangerous attacks that affects its performance; feature selection and parameters optimization are important operations needed to increase the performance of SVM. The aim of this work is to develop an improved optimization method for IDS that can be efficient and effective in subset feature selection and parameters optimization. To achieve this goal, an improved Teaching Learning-Based Optimization (ITLBO) algorithm was proposed in dealing with subset feature selection. Meanwhile, an improved parallel Jaya (IPJAYA) algorithm was proposed for searching the best parameters (C, Gama) values of SVM. Hence, a hybrid classifier called ITLBO-IPJAYA-SVM was developed in this work for the improvement of the efficiency of network intrusion on data sets that contain multiple types of attacks. The performance of the proposed approach was evaluated on NSL-KDD and CICIDS intrusion detection datasets and from the results, the proposed approaches exhibited excellent performance in the processing of large datasets. The results also showed that SVM optimization algorithm achieved accuracy values of 0.9823 for NSL-KDD dataset and 0.9817 for CICIDS dataset, which were higher than the accuracy of most of the existing paradigms for classifying network intrusion detection datasets. In conclusion, this work has presented an improved optimization algorithm that can improve the accuracy of IDSs in the detection of various types of network attack