Apache Spark and Deep Learning Models for High-Performance Network Intrusion Detection Using CSE-CIC-IDS2018.

Keeping computers secure is becoming challenging as networks grow and new network-based technologies emerge. Cybercriminals' attack surface expands with the release of new internet-enabled products. As many cyberattacks affect businesses' confidentiality, availability, and integrity, network intrusion detection systems (NIDS) show an essential role. Network-based intrusion detection uses datasets like CSE-CIC-IDS2018 to train prediction models. With fourteen types of attacks included, the latest big data set for intrusion detection is available to the public. This work proposes three models, two deep learning convolutional neural networks (CNN), long short-term memory (LSTM), and Apache Spark, to improve the detection of all types of attacks. To reduce the dimensionality, random forests (RF) was employed to select the important features; it gave 19 from 84 features. The dataset is imbalanced; thus, oversampling and undersampling techniques reduce the imbalance ratio. The Apache Spark model produced the best results across all 15 classes, with accuracy as high as 100% for all classes, as seen by the experiments' findings. For the F1-score, Apache Spark showed the highest results with 1.00 for most classes. The findings of the three models showed outstanding results for multiclassification network intrusion detection.

Classification of land use/land cover using artificial intelligence (ANN-RF).

Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive and impetus to invent and adopt the notion of developing machine learning because it is simple. This study intended to increase the accuracy of machine-learning approaches for land use/land cover classification using Sentinel-2A, and Landsat-8 satellites. This study aimed to implement a proposed method, neural-based with object-based, to produce a model addressed by artificial neural networks (limited parameters) with random forest (hyperparameter) called ANN_RF. This study used multispectral satellite images (Sentinel-2A and Landsat-8) and a normalized digital elevation model as input datasets for the Sana'a city map of 2016. The results showed that the accuracy of the proposed model (ANN_RF) is better than the ANN classifier with the Sentinel-2A and Landsat-8 satellites individually, which may contribute to the development of machine learning through newer researchers and specialists; it also conventionally developed traditional artificial neural networks with seven to ten layers but with access to 1,000's and millions of simulated neurons without resorting to deep learning techniques (ANN_RF).