Intrusion-Detection System Based on Fast Learning Network in Cloud Computing

Hasan, Ali Mohammed; Fadli, Zolkipli Mohamad

doi:10.1166/asl.2018.12942

Cited by 12 publications

(6 citation statements)

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“…The ESOML-IDS algorithm mainly developed an innovative ESO-based FS technique for optimally selecting feature subsets to detect the existence of intrusions in the FC and EC platforms. Ali and Zolkipli's study [24] comprised a brief description of the IDS and presented to the reviewer some basic principles of the IDS task in CC, further developing a novel Fast Learning Network method for functions dependent upon intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

Modeling of Improved Sine Cosine Algorithm with Optimal Deep Learning-Enabled Security Solution

Almuqren,

Maray,

Aljameel

et al. 2023

Electronics

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Artificial intelligence (AI) acts as a vital part of enhancing network security using intrusion detection and anomaly detection. These AI-driven approaches have become essential components of modern cybersecurity strategies. Conventional IDS is based on predefined signatures of known attacks. AI improves signature-based detection by automating the signature generation and reducing false positives through pattern recognition. It can automate threat detection and response, allowing for faster reaction times and reducing the burden on human analysts. With this motivation, this study introduces an Improved Sine Cosine Algorithm with a Deep Learning-Enabled Security Solution (ISCA-DLESS) technique. The presented ISCA-DLESS technique relies on metaheuristic-based feature selection (FS) and a hyperparameter tuning process. In the presented ISCA-DLESS technique, the FS technique using ISCA is applied. For the detection of anomalous activities or intrusions, the multiplicative long short-term memory (MLSTM) approach is used. For improving the anomaly detection rate of the MLSTM approach, the fruitfly optimization (FFO) algorithm can be utilized for the hyperparameter tuning process. The simulation value of the ISCA-DLESS approach was tested on a benchmark NSL-KDD database. The extensive comparative outcomes demonstrate the enhanced solution of the ISCA-DLESS system with other recent systems with a maximum accuracy of 99.69%.

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Section: Related Workmentioning

confidence: 99%

Modeling of Improved Sine Cosine Algorithm with Optimal Deep Learning-Enabled Security Solution

Almuqren,

Maray,

Aljameel

et al. 2023

Electronics

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“…The authors of this article introduced a time-based sliding window algorithm as a data preprocessing technique and an ensemble approach on Random Forest algorithm for classification. The experiment is tested on CIDDS-01 dataset that has given an Accuracy of 97% [8] [9] This paper adopts a Fast k Nearest Neighbor Classification algorithm for NIDS. The authors applied a partial distance search method as a distance metric, Variance based feature indexing methods.…”

Section: Related Workmentioning

confidence: 99%

“…Statistical and Machine Learning (ML) techniques through systematic, simulation and integration methods are implemented by several researchers as key solutions to mitigate these attacks [1ande proposed frame work sing FkNN over a large featured dataset in terms of computational time with out compromising the accuracy] [4][8] [9].…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers from the last two decades implemented ML algorithms for NIDS, out of these studies kNN classifier is also widely used. To overcome the lazy learning nature of the kNN classifier, a fast kNN classification approach based on Partial Distance Search (PDS) is implemented [8] [9] and the experiments were conducted on Kyoto and NSLKDD datasets were…”

Section: Introductionmentioning

confidence: 99%

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A Novel Framework for NIDS Throuh Fast Knn Classifier on CICIDS 2017 Dataset

Krishna¹,

Swathi²,

Rao³

2020

IJRTE

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This paper investigates the performance of a Fast kNearest Neighbor Classifier (FkNN) for Network Intrusion Detection System (NIDS) on Cloud Environment. For this study Variance Index based Partial Distance Search (VIPDS) kNN [7] is adopted as an FkNN classifier. A benchmark dataset CICIDS2017[16] is considered for the evaluation process because it is a 78 featured dataset with most updated cloud related attacks. To achieve this objective a frame work is proposed for implementing FkNN and compared with kNN classifier by considering two performance measures Accuracy and computational time. This study explores the gain in the computational time without compromising the Accuracy while using FkNN instead of kNN over a large featured dataset. The conclusions are drawn as per the results obtained from the experiments conducted on CICIDS2017 dataset.

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“…Their significance in making it simple for users to engage, share, and produce content, such as blogging, social networks, online communities, newsgroups, and virtual reality, increases social media for all consumers [2]. It is backed up by data from smartinsights.com, which shows that the number of available social media has surpassed 1.69 billion, Twitter has exceeded 340 million, Snapchat has exceeded 450 million, and so on [3]. According to these figures, Facebook is the most popular social networking platform with the most registered users.…”

Section: Introduction To Social Mediamentioning

confidence: 99%

Social Media Data-Based Business Intelligence Analysis Using Deep Learning

2024

IJIES

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The digitization of goods and services is predicated on the production of value by contemporary organizations. What customers say about a firm on social media has become a significant part of the big data revolution. Furthermore, social media data analytics is a challenging topic to learn because of the partiality of text assessment and the extra variables. Social media data analytics using the deep learning (SMDA-DL) framework is proposed in this paper. A two-phase framework is suggested in the SMDA-DL framework: Pre-processing stage and selecting an efficient deep learning technique for this information is the emphasis of phase one; the second phase relies on existing components of large data structures and the model developed in phase one. First, small and large databases in non-big data sets will be analyzed in the first step of the suggested approach. While the first step of the deep learning model may be used to analyze massive data databases, the second stage is recommended. For the first layer of the architecture, there is a case study for assessing social media assessments. The proposed framework yields good simulation results in an accuracy ratio of 98.7%, precision ratio of 99.7%, recall ratio of 98.4% and F score of 98%.

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Intrusion-Detection System Based on Fast Learning Network in Cloud Computing

Cited by 12 publications

References 0 publications

Modeling of Improved Sine Cosine Algorithm with Optimal Deep Learning-Enabled Security Solution

Modeling of Improved Sine Cosine Algorithm with Optimal Deep Learning-Enabled Security Solution

A Novel Framework for NIDS Throuh Fast Knn Classifier on CICIDS 2017 Dataset

Social Media Data-Based Business Intelligence Analysis Using Deep Learning

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