Performance of Machine Learning Techniques in Anomaly Detection with Basic Feature Selection Strategy - A Network Intrusion Detection System

Pranto, Md. Badiuzzaman; Ratul, Md. Hasibul Alam; Rahman, Md. Mahidur; Diya, Ishrat Jahan; Zahir, Zunayeed-Bin

doi:10.12720/jait.13.1.36-44

Cited by 33 publications

(11 citation statements)

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“…Furthermore, neural networks, particularly those endowed with weighted fuzzy membership functions, are instrumental in addressing classification challenges. A pioneering approach involving neural networks in the processing of sole pressure sensor data yielded a commendable accuracy rate of 75.90% by Pranto et al 8 . Recently, Mathew 9 achieved a highly accurate score of 99.27% through the use of an extreme gradient boosting model, which was carefully combined with F-Score feature selection.…”

Section: Machine Learningmentioning

confidence: 99%

A word structure similarity framework for Wordle game

Huang,

Rao,

Lao

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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In 2022, lots of tweets consisting of emoji squares and a few words describing moods emerge on Twitter. In fact, it is the result of a word guessing game called Wordle. In order to figure out the relationship among words in Wordle, this paper tries to study the feedback data of its players and extract useful information to further improve the design of Wordle as well as other games of its kind. Specifically, from the perspective of word structure, strict and fuzzy matching algorithms were designed to simulate the data distribution of target word, while some common structure features of words were extracted and weighted by word frequency. On one hand, the data distribution of the target word is simulated with reference to the words with the most similar structures. On the other hand, the difficulty of a word in the Wordle game was quantified by analyzing the number of player's tries, and then the difficulty coefficient of each word was calculated. Combined with the above numerical algorithms, the difficulty of the target word was evaluated objectively by the difficulty of its similar word sequences. Experiments showed that the proposed framework achieved good performance on the Wordle dataset. Our work was awarded Honorable Mention in 2023 Mathematical Contest in Modeling (MCM).

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Section: Machine Learningmentioning

confidence: 99%

A word structure similarity framework for Wordle game

Huang,

Rao,

Lao

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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“…Pranto, M.B et al [40] tested many classification methods using the KDD-99 dataset. Regarding the pre-processing steps, they emphasize feature selection using famous techniques (selecting K-Best) to achieve better accuracy in the classification task.…”

Section: Machine Learning Intrusion Detection Systemsmentioning

confidence: 99%

Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data

Psychogyios,

Papadakis,

Bourou

et al. 2024

Future Internet

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The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individuals exploiting vulnerabilities to gain access to confidential data, obstruct activity, etc. To this end, intrusion detection systems (IDSs) are needed to filter malicious traffic and prevent common attacks. In the past, these systems relied on a fixed set of rules or comparisons with previous attacks. However, with the increased availability of computational power and data, machine learning has emerged as a promising solution for this task. While many systems now use this methodology in real-time for a reactive approach to mitigation, we explore the potential of configuring it as a proactive time series prediction. In this work, we delve into this possibility further. More specifically, we convert a classic IDS dataset to a time series format and use predictive models to forecast forthcoming malign packets. We propose a new architecture combining convolutional neural networks, long short-term memory networks, and attention. The findings indicate that our model performs strongly, exhibiting an F1 score and AUC that are within margins of 1% and 3%, respectively, when compared to conventional real-time detection. Also, our architecture achieves an ∼8% F1 score improvement compared to an LSTM (long short-term memory) model.

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“…The performance of ML techniques for anomaly detection, with and without FS, is examined in the study [32]. The authors utilize the KDD99 dataset, encompassing many network traffic records.…”

Section: Related Workmentioning

confidence: 99%

An efficient intrusion detection systems in fog computing using forward selection and BiLSTM

Abu Zwayed,

Anbar,

Manickam

et al. 2024

Bulletin EEI

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Intrusion detection systems (IDS) play a pivotal role in network security and anomaly detection and are significantly impacted by the feature selection (FS) process. As a significant task in machine learning and data analysis, FS is directed toward pinpointing a subset of pertinent features that primarily influence the target variable. This paper proposes an innovative approach to FS, leveraging the forward selection search algorithm with hybrid objective/fitness functions such as correlation, entropy, and variance. The approach is evaluated using the BoT-IoT and TON_IoT datasets. By employing the proposed methodology, our bidirectional long-short term memory (BiLSTM) model achieved an accuracy of 98.42% on the TON_IoT dataset and 98.7% on the BoT-IoT dataset. This superior classification accuracy underscores the efficacy of the synergized BiLSTM deep learning model and the innovative FS approach. The study accentuates the potency of the proposed hybrid approach in FS for IDS and highlights its substantial contribution to achieving high classification performance in internet of things (IoT) network traffic analysis.

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Performance of Machine Learning Techniques in Anomaly Detection with Basic Feature Selection Strategy - A Network Intrusion Detection System

Cited by 33 publications

References 0 publications

A word structure similarity framework for Wordle game

A word structure similarity framework for Wordle game

Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data

An efficient intrusion detection systems in fog computing using forward selection and BiLSTM

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