Encoding Approach for Intrusion Detection Using PCA and KNN Classifier

Sameera, Nerella; Shashi, M.

doi:10.1007/978-981-15-1480-7_15

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…The authors have used the NSL-KDD data set for experimenting with the proposed IDS framework. Specifically, this research uses the numeric version of NSL-KDD [9] which is a slightly simplified class of the original dataset consisting of all 33 numeric fields formed by applying an encoding approach that converts categorical fields into numeric fields [9]. To begin with, the dataset is pre-processed initially to prepare the data for experimentation.…”

Section: Resultsmentioning

confidence: 99%

Clustering based Intrusion Detection System for effective Detection of known and Zero-day Attacks

Nerella Sameera,

M.Siva Jyothi,

K.Lakshmaji

et al. 2023

J. Adv. Zool.

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Developing effective security measures is the most challenging task now a days and hence calls for the development of intelligent intrusion detection systems. Most of the existing intrusion detection systems perform best at detecting known attacks but fail to detect zero-day attacks due to the lack of labeled examples. Authors in this paper, comes with a clustering-based IDS framework that can effectively detect both known and zero-day attacks by following unsupervised machine learning techniques. This research uses NSL-KDD dataset for the motive of experimentation and the experimental results exhibit best performance with an accuracy of 78%.

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Section: Resultsmentioning

confidence: 99%

Clustering based Intrusion Detection System for effective Detection of known and Zero-day Attacks

Nerella Sameera,

M.Siva Jyothi,

K.Lakshmaji

et al. 2023

J. Adv. Zool.

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“…There are several machine learning methods, so it's not easy to test them all, we tried to test only the most known and used of them, which are support vector machine (SVM) [19], K-nearest neighbors (KNN) [20], and decision tree [21].  Support vector machine (SVM): it is a machine learning method, which is intended to solve binary and multiple classification problems, it is based on margins, it takes few samples and it achieves good results [22].…”

Section: Analysis Methodsmentioning

confidence: 99%

Parallel processing using big data and machine learning techniques for intrusion detection

Boukhalfa

Hmina

Chaoni

2020

IJ-AI

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Currently, information technology is used in all the life domains, multiple devices produce data and transfer them across the network, these transfers are not always secured, they can contain new menaces invisible by the current security devices. Moreover, the large amount and variety of the exchanged data cause difficulties related to the detection time. To solve these issues, we suggest in this paper, a new approach based on storing the large amount and variety of network traffic data employing Big Data techniques, and analyzing these data with Machine Learning algorithms, in a distributed and parallel way, in order to detect new hidden intrusions with less processing time. According to the results of the experiments, the detection accuracy of the Machine Learning methods reaches 99.9 %, and their processing time has been reduced considerably by applying them in a parallel and distributed way, which proves that our proposed model is effective for the detection of new intrusions.

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“…In order to better verify the classification effect of the model, a simple CNN model and some traditional machine learning methods such as ExtraTreeClassifier [48], KNN [49], and naïve_bayes [50] are selected for comparison. e experiment uses a dichotomy method from the training dataset to divide the data into two, and both the training and test sets contain (87670, 43) feature data.…”

Section: Binary-classification Testmentioning

confidence: 99%

Classification of Abnormal Traffic in Smart Grids Based on GACNN and Data Statistical Analysis

Zhang

et al. 2021

Security and Communication Networks

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With the continuous development of smart grids, communication networks carry more and more power services, and at the same time, they are also facing more and more security issues. For example, some malicious software usually uses encryption technology or tunnel technology to bypass firewalls, intrusion detection systems, etc., thereby posing a serious threat to the information security of smart grids. At present, the classification of network traffic mainly depends on the correct extraction of network protocol characteristics. However, the process of extracting network features by some traditional methods is time-consuming and overly dependent on experience. In order to solve the problem of accurate classification of power network traffic, this paper proposes a method of convolutional neural network based on genetic algorithm optimization (GACNN) and data statistical analysis. This method can simultaneously extract the time characteristics between different packet groups and the spatial characteristics in the same packet group. Therefore, it greatly saves manpower and gets rid of the dependence on experience value. The proposed method has been tested and verified on the UNSW-NB15 dataset and the real dataset collected by the power company. The results show that the proposed method can correctly classify abnormal network flows and is much better than traditional machine learning methods. In large-scale real network flow scenarios, the detection rate of the proposed method exceeds 97%, while the traditional method is generally less than 90%.

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Encoding Approach for Intrusion Detection Using PCA and KNN Classifier

Cited by 11 publications

References 7 publications

Clustering based Intrusion Detection System for effective Detection of known and Zero-day Attacks

Clustering based Intrusion Detection System for effective Detection of known and Zero-day Attacks

Parallel processing using big data and machine learning techniques for intrusion detection

Classification of Abnormal Traffic in Smart Grids Based on GACNN and Data Statistical Analysis

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