DNNBoT: Deep Neural Network-Based Botnet Detection and Classification

The evolution and expansion of IoT devices reduced human efforts,increased resource utilization, and saved time;however, IoT devices createsignificant challenges such as lack of security and privacy, making them morevulnerable to IoT-based botnet attacks. There is a need to develop efficientand faster models which can work in real-time with efficiency and stability. The present investigation developed two novels, Deep Neural Network(DNN) models, DNNBoT1 and DNNBoT2, to detect and classify well-knownIoT botnet attacks such as Mirai and BASHLITE from nine compromisedindustrial-grade IoT devices. The utilization of PCA was made to featureextraction and improve effectual and accurate Botnet classification in IoTenvironments. The models were designed based on rigorous hyperparameterstuning with GridsearchCV. Early stopping was utilized to avoid the effects ofoverfitting and underfitting for both DNN models. The in-depth assessmentand evaluation of the developed models demonstrated that accuracy andefficiency are some of the best-performed models. The novelty of the presentinvestigation, with developed models, bridge the gaps by using a real datasetwith high accuracy and a significantly lower false alarm rate. The results wereevaluated based on earlier studies and deemed efficient at detecting botnetattacks using the real dataset.

Development of PCCNN-Based Network Intrusion Detection System for EDGE Computing

Intrusion Detection System(IDS)plays a crucial role in detecting and identifying the DoS and DDoS type of attacks on IoT devices.However,anomaly-based techniques do not provide acceptable accuracy for efficacious intrusion detection.Also,we found many difficulty levels when applying IDS to IoT devices for identifying attempted attacks.Given this background,we designed a solution to detect intrusions using the Convolutional Neural Network(CNN)for Enhanced Data rates for GSM Evolution(EDGE)Computing.We created two separate categories to handle the attack and non-attack events in the system.The findings of this study indicate that this approach was significantly effective.We attempted both multiclass and binary classification.In the case of binary,we clustered all malicious traffic data in a single class.Also,we developed 13 layers of Sequential 1-D CNN for IDS detection and assessed them on the public dataset NSL-KDD.Principal Component Analysis(PCA)was implemented to decrease the size of the feature vector based on feature extraction and engineering.The approach proposed in the current investigation obtained accuracies of 99.34%and 99.13%for binary and multiclass classification,respectively,for the NSL-KDD dataset.The experimental outcomes showed that the proposed Principal Component-based Convolution Neural Network(PCCNN)approach achieved greater precision based on deep learning and has potential use in modern intrusion detection for IoT systems.

Fusion-Based Deep Learning Model for Hyperspectral Images Classification

A crucial task in hyperspectral image(HSI)taxonomy is exploring effective methodologies to effusively practice the 3-D and spectral data delivered by the statistics cube.For classification of images,3-D data is adjudged in the phases of pre-cataloging,an assortment of a sample,classifiers,post-cataloging,and accurateness estimation.Lastly,a viewpoint on imminent examination directions for proceeding 3-D and spectral approaches is untaken.In topical years,sparse representation is acknowledged as a dominant classification tool to effectually labels deviating difficulties and extensively exploited in several imagery dispensation errands.Encouraged by those efficacious solicitations,sparse representation(SR)has likewise been presented to categorize HSI’s and validated virtuous enactment.This research paper offers an overview of the literature on the classification of HSI technology and its applications.This assessment is centered on a methodical review of SR and support vector machine(SVM)grounded HSI taxonomy works and equates numerous approaches for this matter.We form an outline that splits the equivalent mechanisms into spectral aspects of systems,and spectral–spatial feature networks to methodically analyze the contemporary accomplishments in HSI taxonomy.Furthermore,cogitating the datum that accessible training illustrations in the remote distinguishing arena are generally appropriate restricted besides training neural networks(NNs)to necessitate an enormous integer of illustrations,we comprise certain approaches to increase taxonomy enactment,which can deliver certain strategies for imminent learnings on this issue.Lastly,numerous illustrative neural learning-centered taxonomy approaches are piloted on physical HSI’s in our experimentations.