Detection of intruder using the improved dragonfly optimization algorithm

Devarakonda, Nagaraju; Anandarao, Sarvani; Kamarajugadda, Raviteja

doi:10.1088/1757-899x/1074/1/012011

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Te F-measure also shows promising results in comparison to other techniques for R2L attacks. (9) If Maximum > 1 (10) For J in range (Selected Data, Row) (11) If Selected Data (J) = = 0 (12) Normalized Data (J, I) � 0 (13) Else (14) Normalized Data (J, I)�(Selected Data (J)-Minimum)/(Maximum-Minimum) (15) End-If (16) End-For (17) Else (18) For J in range (Selected Data, Row) (19) Normalized Data (J, I) � Selected Data (J) (20)…”

Section: Resultsmentioning

confidence: 99%

“…But, the drawback of the study is the requirement of the module to use the integrated system that detects the attacks for updated data logs. Devarakonda et al integrated the HIDS and NIDS and implemented the improved dragonfy optimization technique [10]. Te detection rate was 96% but limited to determining the attacks for desired results in terms of precision.…”

Section: Introductionmentioning

confidence: 99%

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Intrusion Detection System Using the G-ABC with Deep Neural Network in Cloud Environment

Gulia

Solanki

Dalal

et al. 2023

Scientific Programming

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Cloud computing plays a pivotal role in sharing resources and information. It is challenging to secure cloud services from different intruders. Intrusion detection system (IDS) plays a vital role in detecting intruder attacks, and it is also used to monitor the traffic in the network. The paper is aimed to control the attacks using the machine learning (ML) technique integrated with the artificial bee colony (ABC) named Group-ABC (G-ABC). The IDS detector has been implemented and further simulation results have been determined using the G-ABC. The evaluation has been carried out using the measures such as precision, recall, accuracy, and F-measure. Different attacks such as user to root (U2R), probe, root to local (R2L), backdoors, worms, and denial-of-service (DoS) attacks have been detected. The simulation analysis is performed using two datasets, namely, the NSL-KDD dataset and UNSW-NB15 dataset, and comparative analysis is performed against the existing work to prove the effectiveness of the proposed IDS. The objective of the work is to determine the intruder attacker system using the deep learning technique.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System Using the G-ABC with Deep Neural Network in Cloud Environment

Gulia

Solanki

Dalal

et al. 2023

Scientific Programming

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“…Fitness function is introduced to check the accuracy of the function. Devarakonda et al [15] showed the usage of improvised dragonfly optimization algorithm. The convergence and fitness function are added to the traditional dragonfly optimization algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

Escape the Traffic Congestion Using Brainstorming Optimization Algorithm and Density Peak Clustering

Devarakonda¹,

Dasari²,

Kamarajugadda³

2021

ISI

Self Cite

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In recent days many people are working on twitter data as the tweets are easily available and also provide reliable data. Collecting and processing these tweets produces promising and accurate results in solving many real world problems. Common problem faced by most of the people is traffic congestion. Traffic congestion results in traffic jams, mental and physical health disturbance. So to avoid this, our paper tried to show the methodology which can bring out promising results. In this paper for processing the tweet data we have used the common approach of Term Frequency-Inverse Document Frequency (TF-IDF) and discussed the application of brainstorming optimization algorithm (BSO) to avoid traffic congestion. We have also introduced the density peak clustering (DPC) to train the brain storming optimization technique. This paper has shown the modified BSO and DPC on the tweets to bring out the results which show traffic conditions at various places. We have justified our work by conducting the experiment.

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“…Recently, researchers from various domains applied meta-heuristic algorithms to FS. Some of the recent works that used metaheuristic algorithms for FS in IDS are: Genetic Algorithm (GA) [7], Bat Optimization algorithm (BOA) [10], Particle Swarm Optimization (PSO) [8], Dragonfly Algorithm (DA) [11], Grey Wolf Optimization (GWO) [9], Firefly optimization (FFO) [12], whale optimization algorithm (WOA) [13] and Pigeon Inspired Optimizer (PIO) [14]. Among them, Alazzam et al [14] proposed a PIO to select the prominent features by eradicating the redundant features.…”

Section: Introductionmentioning

confidence: 99%

Improved Crow Search-Based Feature Selection and Ensemble Learning for IoT Intrusion Detection

Jayalatchumy,

Ramalingam,

Balakrishnan

et al. 2024

IEEE Access

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Network intrusion detection in the Internet of Things (IoT) framework has presented considerable challenges in recent decades. A wide variety of machine-learning approaches are introduced in network intrusion detection. The existing methodologies commonly lack consistency in achieving optimal performance across various multi-class categorization tasks. The present study elucidates implementing a unique intrusion system with the primary objective of enriching the efficacy of network intrusion detection. In the initial phase, it is imperative to employ data-denoising methodologies to effectively tackle the issue of data imbalance. In the next step, the enhanced Crow search algorithm is used to determine the most significant features that aid in better classifying intrusion attacks. In the final phase, the ensemble classifier takes the selected features as input to categorize the standard and invader labels. The present work introduces an ensemble mechanism that comprises four distinct classifiers. The assessment of the proposed approach is validated on two denoised datasets, specifically NSL-KDD and UNSW-NB15. The experimental outcomes demonstrate that the formulated approach achieves exceptional accuracy of 99.4% and 99.2% for the NSL-KDD and UNSW-NB15 datasets, respectively.

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Detection of intruder using the improved dragonfly optimization algorithm

Cited by 6 publications

References 4 publications

Intrusion Detection System Using the G-ABC with Deep Neural Network in Cloud Environment

Intrusion Detection System Using the G-ABC with Deep Neural Network in Cloud Environment

Escape the Traffic Congestion Using Brainstorming Optimization Algorithm and Density Peak Clustering

Improved Crow Search-Based Feature Selection and Ensemble Learning for IoT Intrusion Detection

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