Modular Platform for Detecting and Classifying Phishing Websites Using Cyber Threat Intelligence

Phishing attacks are deceptive types of social engineering techniques that attackers use to imitate genuine websites in order to steal the login credentials and private data of the end-users. The continued success of these attacks is heavily attributed to the prolific adoption of online services and the lack of proper training to foster a security awareness mindset of online users. In addition to the financial and reputational damages caused by data breaches of individual users and businesses, cyber adversaries can further use the leaked data for various malicious purposes. In this work, a modular platform was introduced that facilitates accurate detection and automatic evaluation of websites visited by employees of a company or organization. The basis for this approach is a preceding website analysis, which is essential when hunting for potential threats from proxy logs. The platform contains three modules. Characterization of suspicious websites relies on a set of pre-defined features and a multi-stage threat intelligence technique, the functionality of which has been ascertained in initial tests on real data set

Cognitive privacy middleware for deep learning mashup in environmental IoT

Data mashup is a Web technology that combines information from multiple sources into a single Web application. Mashup applications support new services, such as environmental monitoring. The different organizations utilize data mashup services to merge data sets from the different Internet of Multimedia Things (IoMT) context-based services in order to leverage the performance of their data analytics. However, mashup, different data sets from multiple sources, is a privacy hazard as it might reveal citizens specific behaviors in different regions. In this paper, we present our efforts to build a cognitive-based middleware for private data mashup (CMPM) to serve a centralized environmental monitoring service. The proposed middleware is equipped with concealment mechanisms to preserve the privacy of the merged data sets from multiple IoMT networks involved in the mashup application. In addition, we presented an IoT-enabled data mashup service, where the multimedia data are collected from the various IoMT platforms, and then fed into an environmental deep learning service in order to detect interesting patterns in hazardous areas. The viable features within each region were extracted using a multiresolution wavelet transform, and then fed into a discriminative classifier to extract various patterns. We also provide a scenario for IoMT-enabled data mashup service and experimentation results