With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%.
Skin cancer is undoubtedly one of the deadliest diseases, and early detection of this disease can save lives. The usefulness and capabilities of deep learning in detecting and categorizing skin cancer based on images have been investigated in many studies. However, due to the variety of skin cancer tumour shapes and colours, deep learning algorithms misclassify whether a tumour is cancerous or benign. In this paper, we employed three different pre-trained state-of-the-art deep learning models: DenseNet121, VGG19 and an improved ResNet152, in classifying a skin image dataset. The dataset has a total of 3297 dermatoscopy images and two diagnostic categories: benign and malignant. The three models are supported by transfer learning and have been tested and evaluated based on the criteria of accuracy, loss, precision, recall, f1 score and ROC. Subsequently, the results show that the improved ResNet152 model significantly outperformed the other models and achieved an accuracy score of 92% and an ROC score of 91%. The DenseNet121 and VGG19 models achieve accuracy scores of 90% and 79% and ROC scores of 88% and 75%, respectively. Subsequently, a deep residual learning skin cancer recognition (ResNetScr) system has been implemented based on the ResNet152 model, and it has the capacity to help dermatologists in diagnosing skin cancer.
Intrusion Detection Systems (IDSs) are efficient applications that monitor activities of specific network or system to detect any abnormal activity and then send alarms for a defined management station. However, the current IDSs generate a high number of false alarms; False Positives (FP) and False Negatives (FN), which decreases the accuracy of distinguishing attacks from normal activities. Therefore, this thesis introduces the implementation of enhanced IDS using two classifiers: PCA-SVM and PCA-KNN. The performance of the system with using these classifiers is compared using the NSL-KDD dataset to determine the optimal classifier in terms of detection rate and the number of generated false alarms. This is performed based on dividing the dataset into training and testing sets, where the Control Chart is then applied on the training set to improve the results, where it filters the data to remove the out-bound data and keep the data in the range from Mean-3sigma to Mean+3sigma. Six evaluation metrics; FP, FN, True Positive (TP), True Negative (TN), Detection Rate (DR) and Classification Rate (CR) are computed for both classifiers for three sets of features; with and without applying a control chart. The obtained results demonstrate that the PCA-KNN based IDS with control chart offers the best detection rate with minimum number of generated false alarms for sets F2 and F3, while the PCA-SVM based IDS with control chart offers the best detection rate with minimum number of generated false alarms for F1. The average achieved detection rate for the PCA-KNN based IDS is 98.17% with control chart and 88.7738% without control chart. On the other hand, the average achieved detection rate for the PCA-SVM based IDS is 97.62% with control chart and 96.63587% without control chart. Based on these outcomes, the application of control chart enhances the detection rate and decreases the number of false alarms for both classifiers. In addition, the PCA-KNN is the best classifier to be applied on the IDS with minimum number of false alarms and highest security and detection rate. Our proposed IDSs are implemented and tested in MATLAB 2014.
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