Intrusion Detection System using Naive Bayes algorithm

Sharmila, B.; Nagapadma, Rohini

doi:10.1109/wiecon-ece48653.2019.9019921

Cited by 23 publications

(10 citation statements)

References 8 publications

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“…The Naïve Bayes method is another instance of supervised machine learning that is based on the Bayes theorem, which asserts that classes of values for all attributes are independent of one another. 19 The Gaussian Naïve Bayes are more fitting for categorical data sets with more than two kinds of attacks. The NB algorithm provides an advantage when dealing with novel attack types, and it also trains more quickly.…”

Section: Naïve Bayesmentioning

confidence: 99%

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A feed forward deep neural network model using feature selection for cloud intrusion detection system

Sharma,

Singh

2023

Concurrency and Computation

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SummaryThe rapid advancement and growth of technology have rendered cloud computing services indispensable to our activities. Threats and intrusions have since multiplied exponentially across a range of industries. In such a scenario, the intrusion detection system, or simply the IDS, is deployed on the network to monitor and detect any attacks. The paper proposes a feed‐forward deep neural network (FFDNN) method based on deep learning methodology using a filter‐based feature selection model. The feature selection strategy aims to determine and select the most highly relevant subset of attributes from the feature importance score for training the deep learning model. Three benchmark data sets were used to assess the experiment: CIC‐IDS 2017, UNSW‐NB15, and NSL‐KDD. In order to justify the proposed technique, a comparison was done using other learning algorithms ranging from classical machine learning to ensemble learning methods that can detect various attacks. The experiments showed that the FFDNN model with reduced feature subsets gave the highest accuracy of 99.53% and 94.45% in the NSL‐KDD and UNSW‐NB15 data sets, while the ensemble‐based XGBoost model performed better in the CIC‐IDS 2017 data set. In addition, the results show that the overall accuracy, recall, and F1 score of the deep learning algorithm are generally better for all the data sets.

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Section: Naïve Bayesmentioning

confidence: 99%

“…The NB algorithm provides an advantage when dealing with novel attack types, and it also trains more quickly. 19…”

Section: Naïve Bayesmentioning

confidence: 99%

A feed forward deep neural network model using feature selection for cloud intrusion detection system

Sharma,

Singh

2023

Concurrency and Computation

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“…Gambar 2. Konsep Kerja SIDS [1] (Sumber: Khraisat et al, 2019) Peneliti sebelumnya terkait alert IDS dengan klasifikasi Naïve Bayes antara lain dilakukan dengan: KDD99 dataset dan teknik PCA [7], AIDS dan teknik Correlation-Based Feature Selection [8], NSL-KDD dataset [9], zero probability [10], NSL-KDD dataset dan teknik PCA ditambah SVM [11], KNN sebagai perbandingan [12], diskritisasi variabel [13], Gaussian Naïve Bayes dan Kyoto dataset [14], HND dan KDD99 dataset [15], MNBIDS dan KDD99 dataset [16].…”

Section: Pendahuluanunclassified

Naïve Bayes dan Confusion Matrix untuk Efisiensi Analisa Intrusion Detection System Alert

Suryadewiansyah

Tju

2022

TEKNOSI

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Banyaknya malware menyebabkan IDS (Intrusion Detection System) dituntut menyesuaikan diri semakin kompleks sehingga mahal dan membebani perusahaan yang menggunakannya. Sistem yang berbasis teknologi Host-based IDS dan Signatured-based IDS sudah banyak digunakan namun hanya mampu mendeteksi serangan yang sudah diketahui sebelumnya, untuk memperbaiki kinerjanya perlu dilakukan analisa pada data log berdasarkan alert yang diberikan. Teknik klasifikasi Naïve Bayes digunakan untuk membantu meningkatkan efisisensi dan efektifitas analisa tersebut. Penelitian ini dilakukan dengan mengambil empat langkah bagian dari metodologi SKKNI (Standar Kompetensi Kerja Nasional Indonesia) No.299 tahun 2020, Artificial Intelligence, sub bidang Data Science, yaitu data understanding, data preparation, modeling, dan model evaluation. Dataset dari penyedia layanan IDS sebanyak 575 data yang dibagi menjadi 515 data latih dan 60 data uji. Hasil evaluasi data uji dengan confusion matrix diperoleh pengukuran metrik accuracy 0,87, recall 0,89, precision 0,83, dan F-Measure 0,86. Adanya FP (False Positive) dan FN (False Negatif), keduanya sangat penting bagi penguna IDS untuk meningkatkan kualitas layanan kepada pelanggan dan mengurangi resiko akibat adanya intrusi. FP dan FN menjadi fokus dalam melakukan analisa log alert dari IDS sehingga tidak perlu menganalisa keseluruhan data, berdampak memberikan hasil 85% lebih efektif dan berkontribusi pada efisiensi tenaga dan waktu bagi tim keamanan suatu peruasahaan pengguna IDS. Selain itu didapat bahwa sekitar 50% data IDS adalah intrusi atau pengganggu lainnya.

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“…Host-based (HIDS) are software items mounted on the host machine to analyze and track all traffic events in device application files and operating systems [51]. Network-Based IDS are located at strategic points on the network to catch and analyze the stream of packets passing through network links, in contrast to the HIDS, which analyzes each host individually [52] [53]. The difference between HIDS and NIDS is shown in Table 1.…”

Section: Host-based Ids (Hids) Vs Network-based Ids (Nids)mentioning

confidence: 99%

Application of Machine Learning Approaches in Intrusion Detection System

Aziz

Abdulazeez

2021

JSCDM

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The rapid development of technology reveals several safety concerns for making life more straightforward. The advance of the Internet over the years has increased the number of attacks on the Internet. The IDS is one supporting layer for data protection. Intrusion Detection Systems (IDS) offer a healthy market climate and prevent misgivings in the network. Recently, IDS has been used to recognize and distinguish safety risks using Machine Learning (ML). This paper proposed a comparative analysis of the different ML algorithms used in IDS and aimed to identify intrusions with SVM, J48, and Naive Bayes. Intrusion is also classified. Work with the KDD-CUP data set, and their performance has been checked with the WEKA software. A comparison of techniques such as J48, SVM, and Naïve Bayes showed that the accuracy of j48 is the higher one which was (99.96%).

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Intrusion Detection System using Naive Bayes algorithm

Cited by 23 publications

References 8 publications

A feed forward deep neural network model using feature selection for cloud intrusion detection system

A feed forward deep neural network model using feature selection for cloud intrusion detection system

Naïve Bayes dan Confusion Matrix untuk Efisiensi Analisa Intrusion Detection System Alert

Application of Machine Learning Approaches in Intrusion Detection System

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