Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Hamza, Manar Ahmed; Hassine, Siwar Ben Haj; Abunadi, Ibrahim; Al‐Wesabi, Fahd N.; Alsolai, Hadeel; Hilal, Anwer Mustafa; Yaseen, Ishfaq; Motwakel, Abdelwahed

doi:10.32604/cmc.2022.024764

Cited by 16 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This revolt has automated and simplified several tasks in a field plethora like industry, marketing, or medicine. Certainly, the latter provides various advantages such as the capability to process huge volumes of complicated data and manage repetitive and tedious tasks in record time [8]. The conventional methods employed in cybersecurity at the time of threat detection were majorly dependent upon manual statistical rules and data analytics that needs substantial duration [9].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

Alrayes¹,

Alotaibi²,

Alazwari³

et al. 2023

Computer Systems Science and Engineering

Self Cite

View full text Add to dashboard Cite

Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection (EAGTODL-CTD) in IoT Cloud Networks. The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment. The proposed EAGTODL-CTD model mainly focuses on the conversion of input binary files to color images, where the malware can be detected using an image classification problem. The EAG-TODL-CTD model pre-processes the input data to transform to a compatible format. For threat detection and classification, cascaded gated recurrent unit (CGRU) model is exploited to determine class labels. Finally, EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters, showing the novelty of our work. The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign. The experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

Alrayes¹,

Alotaibi²,

Alazwari³

et al. 2023

Computer Systems Science and Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…At present, Machine Learning (ML) systems are utilized for the management of the network by numerous authors. Cybersecurity is a primary concern in advancing networking technologies and computers [3,4]. The criminals are being smart and launching new menaces; a substantial investigation was placed to expand the counteractions to rescue organizations and individuals from these damages.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Alalayah¹,

Alrayes²,

Nour³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Malware is a 'malicious software program that performs multiple cyberattacks on the Internet, involving fraud, scams, nation-state cyberwar, and cybercrime. Such malicious software programs come under different classifications, namely Trojans, viruses, spyware, worms, ransomware, Rootkit, botnet malware, etc. Ransomware is a kind of malware that holds the victim's data hostage by encrypting the information on the user's computer to make it inaccessible to users and only decrypting it; then, the user pays a ransom procedure of a sum of money. To prevent detection, various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning (ML) algorithm. This study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection (LBAAA-OMLMD) approach in Computer Networks. The presented LBAAA-OMLMD model mainly aims to detect and classify the existence of ransomware and goodware in the network. To accomplish this, the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection (LBAAA-FS) model to reduce the curse of dimensionality problems. Besides, the Flower Pollination Algorithm (FPA) with Echo State Network (ESN) Classification model is applied. The FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier results. The experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset, and the outcomes are inspected in distinct measures. The comprehensive comparative examination demonstrated the betterment of the LBAAA-OMLMD model over recent algorithms.

show abstract

“…Next, the study employed supervised ML techniques such as Artificial Neural Networks (ANNs), Random Forest (RF), and Support Vector Machine (SVM) on the cluster. Hawawreh et al [17][18][19][20][21] presented advanced anomaly detection methodologies that could validate and learn through data gathered from TCP/IP packets. It embraces a successive training procedure, implemented by Deep-FeedForward Neural Network (DNN-FFNN) and Autoencoder (AE) structure, estimated by two popular network datasets.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model

S. Almasoud

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

The Internet of Things (IoT) is considered the next-gen connection network and is ubiquitous since it is based on the Internet. Intrusion Detection System (IDS) determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified behaviour. In this background, the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification (EMML-CADC) model in an IoT environment. The aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced efficiency. To attain this, the EMML-CADC model primarily employs a data preprocessing stage to normalize the data into a uniform format. In addition, Enhanced Cat Swarm Optimization based Feature Selection (ECSO-FS) approach is followed to choose the optimal feature subsets. Besides, Mayfly Optimization (MFO) with Twin Support Vector Machine (TSVM), called the MFO-TSVM model, is utilized for the detection and classification of cyberattacks. Here, the MFO model has been exploited to fine-tune the TSVM variables for enhanced results. The performance of the proposed EMML-CADC model was validated using a benchmark dataset, and the results were inspected under several measures. The comparative study concluded that the EMML-CADC model is superior to other models under different measures.

show abstract

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Cited by 16 publications

References 23 publications

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model

Contact Info

Product

Resources

About