Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

Cybersecurity threats are increasing rapidly as hackers use advanced techniques.As a result,cybersecurity has now a significant factor in protecting organizational limits.Intrusion detection systems(IDSs)are used in networks to flag serious issues during network management,including identifying malicious traffic,which is a challenge.It remains an open contest over how to learn features in IDS since current approaches use deep learning methods.Hybrid learning,which combines swarm intelligence and evolution,is gaining attention for further improvement against cyber threats.In this study,we employed a PSO-GA(fusion of particle swarm optimization(PSO)and genetic algorithm(GA))for feature selection on the CICIDS-2017 dataset.To achieve better accuracy,we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU(gated recurrent unit)and LSTM(long short-term memory).The results show considerable improvement,detecting several network attacks with 98.86%accuracy.A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.

A Novel Framework for Multi-Classification of Guava Disease

Guava is one of the most important fruits in Pakistan,and is gradually boosting the economy of Pakistan.Guava production can be interrupted due to different diseases,such as anthracnose,algal spot,fruit fly,styler end rot and canker.These diseases are usually detected and identified by visual observation,thus automatic detection is required to assist formers.In this research,a new technique was created to detect guava plant diseases using image processing techniques and computer vision.An automated system is developed to support farmers to identify major diseases in guava.We collected healthy and unhealthy images of different guava diseases from the field.Then image labeling was done with the help of an expert to differentiate between healthy and unhealthy fruit.The local binary pattern(LBP)was used for the extraction of features,and principal component analysis(PCA)was used for dimensionality reduction.Disease classification was carried out using multiple classifiers,including cubic support vector machine,Fine K-nearest neighbor(F-KNN),Bagged Tree and RUSBoosted Tree algorithms and achieved 100%accuracy for the diagnosis of fruit flies disease using Bagged Tree.However,the findings indicated that cubic support vector machines(C-SVM)was the best classifier for all guava disease mentioned in the dataset.

Classification of hyperspectral images using fusion of CNN and MiniGCN with SVM

Convolutional neural networks(CNNs)have gained popularity for categorizing hyperspectral(HS)images due to their ability to capture representations of spatial-spectral features.However,their ability to model relationships between data is limited.Graph convolutional networks(GCNs)have been introduced as an alternative,as they are effective in representing and analyzing irregular data beyond grid samplingconstraints.WhileGCNs have traditionally.been computationally intensive,minibatch GCNs(miniGCNs)enable minibatch training of large-scale GCNs.We have improved the classification performance by using miniGCNs to infer out-of-sample data without retraining the network.In addition,fuzing the capabilities of CNNs and GCNs,through concatenative fusion has been shown to improve performance compared to using CNNs or GCNs individually.Finally,support vector machine(SvM)is employed instead of softmax in the classification stage.These techniques were tested on two HS datasets and achieved an average accuracy of 92.80 using Indian Pines dataset,demonstrating the effectiveness of miniGCNs and fusion strategies.