Integrated Intrusion Detection Scheme using Agents

Rai, Kajal; Guleria, Ajay; Devi, M. Syamala

doi:10.17781/p002646

Cited by 3 publications

(4 citation statements)

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“…It is able to detect new types of attacks. Rai et al (2016) develop decision tree algorithm using the C4.5 tree approach and the algorithm is designed to address two major issues, feature selection and split value. Salient features are determined using information gain and each attribute is selected to make the classifier unbiased to most recurring values.…”

Section: Related Workmentioning

confidence: 99%

Improved minority attack detection in Intrusion Detection System using efficient feature selection algorithms

Rejimol Robinson,

Anagha Madhav,

Thomas

2024

Expert Systems

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Machine Learning and Data Mining algorithms are used extensively to enhance the performance of Intrusion Detection Systems. The number of training instances and the dimensionality of data are crucial factors affecting the performance of the model built during the training of any supervised learning algorithms. A sufficient proportion of instances having relevant features from all classes of attacks and normal traffic are considered most desirable while building the classification model that classifies the network traffic into attack and normal. This paper proposes a methodology to improve the accuracy of the model by giving importance to the relevant features that can contribute to model building. The feature selection using correlation‐based and information gain‐based techniques during training and testing contributes much to the detection of stealthier attacks and minority attacks. Then the features of the less detected attacks are identified as the second phase of the filter that is used to improve the performance. The relevant features of stealthy attacks are identified based on the correlation of corresponding features of the attack and normal data as the attacks are made stealthy mostly by making it resemble the normal traffic. Finally, the attacks that are rarely found in the training data are oversampled to improve their detection. CICIDS 2017 data set is employed as it comprises stealthier attacks generated using modern tools. NSL KDD data set is also used for evaluation to compare the proposed work with existing literature as it is used in most of the available literature. The results show superior performance with an accuracy of 99.8%, false positive rate of 0.2%, and a detection rate and 99.8%.

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Section: Related Workmentioning

confidence: 99%

Improved minority attack detection in Intrusion Detection System using efficient feature selection algorithms

Rejimol Robinson,

Anagha Madhav,

Thomas

2024

Expert Systems

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“…They showed that the main goal of an intrusion detection system is to find as many attacks as possible with as few false alarms as possible. (i) Estimating critical network parameter thresholds (ii) PCA, chi-square distribution, and Gaussian mixture distribution (iii) Least square support vector machine (LS-SVM) (iv) Genetic algorithm (GA) (v) Hybrid classifier (DTNB), which is a combination of decision table (DT) and NB algorithms (i) [26] (ii) [27] (iii) [28] (iv) [29] (v) [30] AI and machine learning methods AI and ML methods have a high predictive capacity and need little human intervention (i) Naïve Bayesian classifier and a decision tree (ii) Naïve Bayesian classifier (iii) SVM (iv) Hybrid model that incorporates data mining approaches such as the K-means clustering algorithm and the RBF kernel function of the support vector machine (v) Decision tree (J48) algorithm (vi) RF (vii) C4.5 decision tree (viii) k-means and k-nearest neighbors (ix) GA and best feature set selection (BFSS) (x) RF and SVM (i) [31] (ii) [32] (iii) [33] (iv) [34] (v) [35] (vi) [36] (vii) [37] (viii) [38] (ix) [39] (x) [40] Swarm intelligence and evolutionary computation methods It uses GAs, particle swarm optimization (PSO), and differential evolution computational techniques (i) GAs, PSO, and differential evolution (DE) (ii) Particle swarm optimization classifiers with genetic-particle swarm optimization (iii) Feature selection (EFS) algorithm and teaching learning-based optimization (TLBO) methodologies are combined (iv) Combines the approaches of PSO, GA, and neural networks (RBF) (i) [41] (ii) [42] (iii) [43] (iv) [44] Deep learning methods Part of AI where the model is a mathematical algorithm that is trained to provide the same conclusion or prediction as a human expert (i) [45] (ii) [46] (iii) [47] (iv) [48] (v) [49] (vi) [50] (vii) [51] (viii) [52] (ix) [53] (x) [54] (xi) [55] (xii) ...…”

Section: Intrusion Detection Techniques In Social Mediamentioning

confidence: 99%

“…In terms of using a naïve Bayesian classifier and a decision tree, Rai et al [32] proposed a new approach for adapting network intrusion detection. Mainly, the study addresses various data mining challenges, such as dealing with continuous attributes, handling missing values in the dataset, and reducing the noise in the training set.…”

Section: Ai and Machinementioning

confidence: 99%

Intrusion Detection Techniques in Social Media Cloud: Review and Future Directions

Abuali

Nissirat

Al-Samawi

2023

Wireless Communications and Mobile Computing

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As social media use increases, the number of users has risen also. This has increased the volume of data carried over the network, making it more important to secure users’ data and privacy from threats. As users are unaware of hackers, social media’s security flaws and new forms of attack will persist. Intrusion detection systems, therefore, are vital to identifying intrusion risks. This paper examines a variety of intrusion detection techniques used to detect cyberattacks on social media networks. The paper provides a summary of the prevalent attacks on social media networks, such as phishing, fake profiles, account compromise, and cyberbullying. Then, the most prevalent techniques for classifying network traffic, including statistical and artificial intelligence (AI) techniques, are addressed. The literature also demonstrates that because AI can manage vast, scalable networks, AI-based IDSs are more effective at classifying network traffic and detecting intrusions in complex social media networks. However, AI-based IDSs exhibit high computational and space complexities; therefore, despite their remarkable performance, they are more suitable for high computing power systems. Hybrid IDSs, utilizing statistical feature selection and shallow neural networks, may provide a compromise between computational requirements and efficiency. This investigation shows that accuracies of statistical techniques range from 90% to 97.5%. In contrast, AI and ML technique detection accuracy ranges from 78% to 99.95%. Similarly, swarm and evolutionary techniques achieved from 84% to 99.95% and deep learning-based detection techniques achieved from 45% to more than 99% detection rates. Convolutional neural network deep learning systems outperformed other methods due to their ability to automatically craft the features that would classify the network traffic with high accuracy.

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“…ML algorithms can be roughly categorized into traditional ML and deep learning (DL) algorithms. Regarding traditional ML algorithms, decision tree-based anomaly detection is widely used in various fields, e.g., in smart grid [7] and computer network traffic [8]. Tian et.al adopted the Gradient Boosting Decision Tree (GBDT) algorithm for CAN bus intrusion detection and their method delivered a high True Positive (TP) rate and a low False Positive (FP) rate [9].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network

Dong

Chen

Peng

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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As the central nerve of the intelligent vehicle control system, the in-vehicle network bus is crucial to the security of vehicle driving. One of the best standards for the in-vehicle network is the Controller Area Network (CAN bus) protocol. However, the CAN bus is designed to be vulnerable to various attacks due to its lack of security mechanisms. To enhance the security of in-vehicle networks and promote the research in this area, based upon a large scale of CAN network traffic data with the extracted valuable features, this study comprehensively compared fully-supervised machine learning with semi-supervised machine learning methods for CAN message anomaly detection. Both traditional machine learning models (including single classifier and ensemble models) and neural network based deep learning models are evaluated. Furthermore, this study proposed a deep autoencoder based semi-supervised learning method applied for CAN message anomaly detection and verified its superiority over other semi-supervised methods. Extensive experiments show that the fully-supervised methods generally outperform semi-supervised ones as they are using more information as inputs. Typically the developed XGBoost based model obtained state-of-the-art performance with the best accuracy (98.65%), precision (0.9853), and ROC AUC (0.9585) beating other methods reported in the literature.

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Integrated Intrusion Detection Scheme using Agents

Cited by 3 publications

References 0 publications

Improved minority attack detection in Intrusion Detection System using efficient feature selection algorithms

Improved minority attack detection in Intrusion Detection System using efficient feature selection algorithms

Intrusion Detection Techniques in Social Media Cloud: Review and Future Directions

Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network

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