The Internet, web consumers and computing systems have become more vulnerable to cyber-attacks. Malicious uniform resource locator (URL) is a prominent cyber-attack broadly used with the intention of data, money or personal information stealing. Malicious URLs comprise phishing URLs, spamming URLs, and malware URLs. Detection of malicious URL and identification of their attack type are important to thwart such attacks and to adopt required countermeasures. The proposed methodology for detection and categorization of malicious URLs uses stacked restricted Boltzmann machine for feature selection with deep neural network for binary classification. For multiple classes, IBK-kNN, Binary Relevance, and Label Powerset with SVM are used for classification. The approach is tested with 27700 URL samples and the results demonstrate that the deep learning-based feature selection and classification techniques are able to quickly train the network and detect with reduced false positives.

Over the last 20 years, Wi-Fi technology has advanced to the point where most modern devices are small and rely on Wi-Fi to access the internet. Wi-Fi network security is severely questioned since there is no physical barrier separating a wireless network from a wired network, and the security procedures in place are defenseless against a wide range of threats. This study set out to assess federated learning, a new technique, as a possible remedy for privacy issues and the high expense of data collecting in network attack detection. To detect and identify cyber threats, especially in Wi-Fi networks, the research presents FEDDBN-IDS, a revolutionary intrusion detection system (IDS) that makes use of deep belief networks (DBNs) inside a federated deep learning (FDL) framework. Every device has a pre-trained DBN with stacking restricted Boltzmann machines (RBM) to learn low-dimensional characteristics from unlabelled local and private data. Later, these models are combined by a central server using federated learning (FL) to create a global model. The whole model is then enhanced by the central server with fully linked SoftMax layers to form a supervised neural network, which is then trained using publicly accessible labeled AWID datasets. Our federated technique produces a high degree of classification accuracy, ranging from 88% to 98%, according to the results of our studies.

Smart objects associated with the Internet, constitutes the concept of Internet of Things (IoT), are reforming people's collaboration with the world. As innovation comes to all around, anybody can abuse it, and it is constantly fundamental to secure it. Various intrusion detection approaches have been established with their individual merits and demerits. The greater part of study in the zone of IoT intrusion detection is quiet built on either benchmarked KDD cup dataset or simulated datasets due to non-accessibility of real datasets. For this reason, there is a necessity to create test data set, which depends on real network data. The proposed work builds a novel data set from the wireless network packet traffic flow captured through Wireshark that holds different attack profiles. The captured traffic is filtered and preprocessed to generate a featured dataset. The proposed Intrusion Detection System (IDS) uses hybrid machine learning technique by combining K-means clustering and Support Vector Machine (SVM) classification to achieve low false positive rate with improved detection rate.