Effectiveness Of Intrusion Detection Systems Research Articles

Effectiveness of an ensemble technique based on the distributivity equation in detecting suspicious network activity

With the growing complexity and frequency of cyber threats, there is a pressing need for more effective defense mechanisms. Machine learning offers the potential to analyze vast amounts of data and identify patterns indicative of malicious activity, enabling faster and more accurate threat detection. Ensemble methods, by incorporating diverse models with varying vulnerabilities, can increase resilience against adversarial attacks. This study covers the usage and evaluation of the relevance of an innovative approach of ensemble classification for identifying intrusion threats on a large CICIDS2017 dataset. The approach is based on the distributivity equation that appropriately aggregates the underlying classifiers. It combines various standard supervised classification algorithms, including Multilayer Perceptron Network, k-Nearest Neighbors, and Naive Bayes, to create an ensemble. Experiments were conducted to evaluate the effectiveness of the proposed hybrid ensemble method. The performance of the ensemble approach was compared with individual classifiers using measures such as accuracy, precision, recall, F-score, and area under the ROC curve. Additionally, comparisons were made with widely used state-of-the-art ensemble models, including the soft voting method (Weighted Average Probabilities), Adaptive Boosting (AdaBoost), and Histogram-based Gradient Boosting Classification Tree (HGBC) and with existing methods in the literature using the same dataset, such as Deep Belief Networks (DBN), Deep Feature Learning via Graph (Deep GFL). Based on these experiments, it was found that some ensemble methods, such as AdaBoost and Histogram-based Gradient Classification Tree, do not perform reliably for the specific task of identifying network attacks. This highlights the importance of understanding the context and requirements of the data and problem domain. The results indicate that the proposed hybrid ensemble method outperforms traditional algorithms in terms of classification precision and accuracy, and offers insights for improving the effectiveness of intrusion detection systems.

Enhancing Intrusion Detection Systems Using a Deep Learning and Data Augmentation Approach

Cybersecurity relies heavily on the effectiveness of intrusion detection systems (IDSs) in securing business communication because they play a pivotal role as the first line of defense against malicious activities. Despite the wide application of machine learning methods for intrusion detection, they have certain limitations that might be effectively addressed by leveraging different deep learning architectures. Furthermore, the evaluation of the proposed models is often hindered by imbalanced datasets, limiting a comprehensive assessment of model efficacy. Hence, this study aims to address these challenges by employing data augmentation methods on four prominent datasets, the UNSW-NB15, 5G-NIDD, FLNET2023, and CIC-IDS-2017, to enhance the performance of several deep learning architectures for intrusion detection systems. The experimental results underscored the capability of a simple CNN-based architecture to achieve highly accurate network attack detection, while more complex architectures showed only marginal improvements in performance. The findings highlight how the proposed methods of deep learning-based intrusion detection can be seamlessly integrated into cybersecurity frameworks, enhancing the ability to detect and mitigate sophisticated network attacks. The outcomes of this study have shown that the intrusion detection models have achieved high accuracy (up to 91% for the augmented CIC-IDS-2017 dataset) and are strongly influenced by the quality and quantity of the dataset used.

A Novel Approach to Android Malware Intrusion Detection Using Zero-Shot Learning GANs

This study proposes an innovative intrusion detection system for Android malware based on a zero-shot learning GAN approach. Our system achieved an accuracy of 99.99%, indicating that this approach can be highly effective for identifying intrusion events. The proposed approach is particularly valuable for analyzing complex datasets such as those involving Android malware. The results of this study demonstrate the potential of this method for improving the accuracy and efficiency of intrusion detection systems in real-world scenarios. Future work could involve exploring alternative feature selection techniques and evaluating the performance of other machine learning classifiers on larger datasets to further enhance the accuracy of intrusion detection systems. The study highlights the importance of adopting advanced machine learning techniques such as zero-shot learning GANs to enhance the effectiveness of intrusion detection systems in cybersecurity. The proposed system presents a significant contribution to the field of intrusion detection, providing an effective solution for detecting malicious activities in Android malware, which can improve the security of mobile devices.

A Hypertuned Lightweight and Scalable LSTM Model for Hybrid Network Intrusion Detection

Given the increasing frequency of network attacks, there is an urgent need for more effective network security measures. While traditional approaches such as firewalls and data encryption have been implemented, there is still room for improvement in their effectiveness. To effectively address this concern, it is essential to integrate Artificial Intelligence (AI)-based solutions into historical methods. However, AI-driven approaches often encounter challenges, including lower detection rates and the complexity of feature engineering requirements. Finding solutions to overcome these hurdles is critical for enhancing the effectiveness of intrusion detection systems. This research paper introduces a deep learning-based approach for network intrusion detection to overcome these challenges. The proposed approach utilizes various classification algorithms, including the AutoEncoder (AE), Long-short-term-memory (LSTM), Multi-Layer Perceptron (MLP), Linear Support Vector Machine (L-SVM), Quantum Support Vector Machine (Q-SVM), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). To validate the effectiveness of the proposed approach, three datasets, namely IOT23, CICIDS2017, and NSL KDD, are used for experimentation. The results demonstrate impressive accuracy, particularly with the LSTM algorithm, achieving a 97.7% accuracy rate on the NSL KDD dataset, 99% accuracy rate on the CICIDS2017 dataset, and 98.7% accuracy on the IOT23 dataset. These findings highlight the potential of deep learning algorithms in enhancing network intrusion detection. By providing network administrators with robust security measures for accurate and timely intrusion detection, the proposed approach contributes to network safety and helps mitigate the impact of network attacks.

An Optimization Model for Appraising Intrusion-Detection Systems for Network Security Communications: Applications, Challenges, and Solutions

Cyber-attacks are getting increasingly complex, and as a result, the functional concerns of intrusion-detection systems (IDSs) are becoming increasingly difficult to resolve. The credibility of security services, such as privacy preservation, authenticity, and accessibility, may be jeopardized if breaches are not detected. Different organizations currently utilize a variety of tactics, strategies, and technology to protect the systems’ credibility in order to combat these dangers. Safeguarding approaches include establishing rules and procedures, developing user awareness, deploying firewall and verification systems, regulating system access, and forming computer-issue management groups. The effectiveness of intrusion-detection systems is not sufficiently recognized. IDS is used in businesses to examine possibly harmful tendencies occurring in technological environments. Determining an effective IDS is a complex task for organizations that require consideration of many key criteria and their sub-aspects. To deal with these multiple and interrelated criteria and their sub-aspects, a multi-criteria decision-making (MCMD) approach was applied. These criteria and their sub-aspects can also include some ambiguity and uncertainty, and thus they were treated using q-rung orthopair fuzzy sets (q-ROFS) and q-rung orthopair fuzzy numbers (q-ROFNs). Additionally, the problem of combining expert and specialist opinions was dealt with using the q-rung orthopair fuzzy weighted geometric (q-ROFWG). Initially, the entropy method was applied to assess the priorities of the key criteria and their sub-aspects. Then, the combined compromised solution (CoCoSo) method was applied to evaluate six IDSs according to their effectiveness and reliability. Afterward, comparative and sensitivity analyses were performed to confirm the stability, reliability, and performance of the proposed approach. The findings indicate that most of the IDSs appear to be systems with high potential. According to the results, Suricata is the best IDS that relies on multi-threading performance.

Effectiveness Evaluation of Different IDSs Using Integrated Fuzzy MCDM Model

Cyber-attacks are becoming progressively complicated; hence, the functional issues of intrusion-detection systems (IDSs) present ever-growing challenges. Failing to detect intrusions may jeopardize the trustworthiness of security services, such as privacy preservation, authenticity, and accessibility. To fight these risks, different organizations nowadays use a variety of approaches, techniques, and technologies to safeguard the systems’ credibility. Establishing policies and procedures, raising user awareness, implementing firewall and verification systems, controlling system access, and building computer-issue management groups are all examples of safeguarding methods. There is a lack of sufficient emphasis on the effectiveness of intrusion-detection systems. In enterprises, IDS is used to analyze the potentially dangerous activities taking place within the technological settings. The selection of efficient IDS is a challenging task for organizations. This research evaluates the impact of five popular IDSs for their efficiency and effectiveness in information security. The authors used the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS)-based integrated multi-criteria decision-making (MCDM) methodology to evaluate the efficacy of the popular IDSs. The findings of this research suggest that most of the IDSs appear to be highly potential tools. Even though Snort is extensively deployed, Suricata has a substantial advantage over Snort. Suricata uses multi-threading functionality in comparison to Snort to boost the processing performance.

A Hybrid Classification Technique for Enhancing the Effectiveness of Intrusion Detection Systems Using Machine Learning

The objective of this paper is to propose and develop a hybrid intrusion detection system to handle series and non-series data by applying the two different concepts that are named clustering and autocorrelation function in a single architecture. There is a need to propose and build a system that can handle both types of data whether it is series or non-series. Therefore, the authors used two concepts to generate a robust approach to craft a hybrid intrusion detection system. The authors utilize an unsupervised clustering approach that is used to categorize the data based on domain similarity to handle non-series data and another approach is based on autocorrelation function to handle series data. The approach is consumed in single architecture where it carries data as input from both host-based intrusion detection systems and network-based intrusion detection systems. The result shows that the hybrid intrusion detection system is categorizing data based on the optimal number of clusters obtained through the elbow method in clustering.

Improving the effectiveness of intrusion detection systems for hierarchical data

A high false alarm rate of anomaly-based, on-line, high throughput intrusion detection systems (IDS) is a serious concern, often rendering these IDSs impractical for use in real-world systems. The usual approach to this problem is to try to decrease or limit the false alarm rate. However, IDSs that adopt this approach are usually attack or algorithm specific and are not considered generally applicable. In this paper, we propose a general method for lowering the false positive rate (FPR) of any existing state-of-the-art anomaly-based IDS for hierarchical data, while minimizing the potential decrease in the detection rate. This is done by automatically learning the underlying hierarchy of sub-classes from a dataset of normal instances and iteratively applying the IDS on each sub-class. Compared to previous work, our method is more practical because it does not require users to possess any knowledge about the data’s hierarchical structure or make assumptions about its distribution. We evaluate our method’s ability to improve the effectiveness of recent state-of-the-art IDSs on a variety of attacks on operational networks of IP cameras and IoT devices as well as attacks on the MIL-STD-1553 communication protocol. We test numerous configurations of all IDSs and show that our method can improve detection performance in more than 98% of our tests. We demonstrate that our method can improve IDSs that operate on any type of data, e.g. independent feature vector data instances or sequences of dependent data records. By evaluating on datasets with different attack occurrence rates, we also demonstrate that our ability to improve an IDS’s effectiveness becomes more significant as attacks occur more rarely. This further emphasizes our method’s contribution to real-life intrusion detection scenarios in which the attack occurrence rates can be very low.

Effectiveness of Intrusion Detection Systems in High-speed Networks

Network Intrusion Detection Systems (NIDSs) play a crucial role in detecting malicious activities within networks. Basically, a NIDS monitors network flows and compares them with a set of pre-defined suspicious patterns. To be effective, different intrusion detection algorithms and packet capturing methods have been implemented. With rapidly increasing network speeds, NIDSs face a challenging problem of monitoring large and diverse traffic volumes; in particular, high packet drop rates can have a significant impact on detection accuracy. In this work, we investigate three popular open-source NIDSs: Snort, Suricata, and Bro along with their comparative performance benchmarks. We investigate key factors (including system resource usage, packet processing speed and packet drop rate) that limit the applicability of NIDSs to large-scale networks. Moreover, we also analyse and compare the performance of NIDSs when configurations and traffic volumes are changed.

An Efficient IDS Using Hybrid Magnetic Swarm Optimization in WANETs

Sophisticated Intrusion attacks against various types of networks are ever increasing today with the exploitation of modern technologies which often severely affect wireless networks. In order to improve the effectiveness of intrusion detection systems (IDSs), data analysis methods such as data mining and classification methods are often integrated with IDSs. Though, numerous studies have contributed in various ways to improve the utilization of data mining for IDS, effective solution often depends on the network setting where the IDS is deployed. In this paper, we propose an efficient IDS based on hybrid heuristic optimization algorithm which is inspired by magnetic field theory in physics that deals with attraction between particles scattered in the search space. Our developed algorithm works in extracting the most relevant features that can assist in accurately detecting the network attacks. These features are extracted by tagged index values that represent the information gain out of the training course of the classifier to be used as a base for our developed IDS. In order to improve the accuracy of artificial neural network (ANN) classifier, we have integrated our proposed hybrid magnetic optimization algorithm-particle swarm optimization (MOA-PSO) technique. Experimental results show that using our proposed IDS based on hybrid MOA-PSO technique provides more accuracy level compared to the use of ANN based on MOA, PSO and genetic algorithm. Updated KDD CUP data set is formed and used during the training and testing phases, where this data set consists of mixed data traffics between attacks and normal activities. Our results show significant gain in terms of efficiency compared to other alternative mechanisms.

The role of intrusion detection systems in electronic information security

PurposeThe purpose of this paper is to investigate the effectiveness of intrusion detection systems as an access control supplement in protecting electronic information resources and networks in information‐centric organisations. The study focuses on the strengths and vulnerabilities of intrusion detection systems (IDSs).Design/methodology/approachA qualitative case study is conducted with a retail organisation, and an educational institution in Cape Town, South Africa. Using purposive sampling, interviews are held with network administrators of sample institutions to unpack security priorities and the functionalities of IDSs, the significance of the system in concept, whether it is understood within network departments, the cost factor, and its value in securing networks against all possible security challenges. The activity theory is applied as a lens to understanding the security process, and to inform a future security frameworks and research initiatives.FindingsThe findings are clear. Although IDSs have vulnerabilities, they offer an added cushion to conventional network access control efforts. Access control for example, guards the gate but IDSs are the watchdogs in your yard, and IDS closes a gap in a network security that only IDSs can. It alerts you of a potential attacker, enabling you to respond promptly, in whichever way you like. It does however, require deliberate reaction against a detected intrusion to be effective, but remains a useful security tool that should become standard to all network security initiatives. A framework presenting network security as a work activity – with actors who are guided by goals – is offered to guide planning, implementations of network security and further research in future.Originality/valueSecurity awareness is crucial to effective e‐citizenry, but complacency could be a threat. As a unique contribution, the paper presents an activity‐theory work‐activity framework of analysing network security. Further, the paper presents original, industry‐specific interview findings, raising awareness that existing security measures need to be viewed as a continuous work‐activity whose planning and implementations are embedded on goals and processes towards pursued outcomes. Access controls themselves should be monitored. They should be supplemented by effective intrusion detection systems if unauthorised access is to be effectively minimised.