Abstract: Systems, devices, and methods of protecting electronic or Internet-connected devices against fraudulent and malicious activities. A Data Collector and Mediator Unit monitors network traffic, and generates datasets of network traffic; each dataset includes network traffic monitored within a time-slot having a particular fixed time-length. A Predictor Unit includes a Features Extractor, to extract features from the datasets; and a Machine Learning (ML) unit, to run the extracted features through a ML model and to classify a particular traffic-portion as being either (I) an anomalous traffic-portion that is associated with fraudulent or malicious activity, or (II) a non-anomalous traffic-portion that is not-associated with fraudulent or malicious activity. The ML unit operates on both (i) anomalies in traffic patterns, and (ii) anomalies of user behavior and/or device behavior.

Abstract: Systems, devices, and methods of classifying encrypted network communications. A Traffic Monitoring Unit operates to monitor network traffic, and to capture HTTPS-encrypted packets that are exchanged over an HTTPS connection between an end-user device and a web server. An HTTPS Traffic Classification Unit operates to detect discrete HTTPS-encrypted objects within that HTTPS connection, and to classify those discrete HTTPS-encrypted objects based on at least one of: a first Analysis Model that classifies HTTPS-encrypted objects based on a type of content that is represented in the HTTPS-encrypted object; a second Analysis Model that classifies HTTPS-encrypted objects based on a type of server-side application that is associated with the HTTPS-encrypted object. Each Analysis Model utilizes Machine Learning (ML), Deep Learning (DL), Artificial Intelligence (AI), or Statistical and Mathematical Analysis (SMA).

Abstract: Systems, devices, and methods of classifying encrypted network communications. A Traffic Monitoring Unit operates to monitor network traffic, and to capture HTTPS-encrypted packets that are exchanged over an HTTPS connection between an end-user device and a web server. An HTTPS Traffic Classification Unit operates to detect discrete HTTPS-encrypted objects within that HTTPS connection, and to classify those discrete HTTPS-encrypted objects based on at least one of: a first Analysis Model that classifies HTTPS-encrypted objects based on a type of content that is represented in the HTTPS-encrypted object; a second Analysis Model that classifies HTTPS-encrypted objects based on a type of server-side application that is associated with the HTTPS-encrypted object. Each Analysis Model utilizes Machine Learning (ML), Deep Learning (DL), Artificial Intelligence (AI), or Statistical and Mathematical Analysis (SMA).

Abstract: Systems, devices, and methods of protecting electronic or Internet-connected devices against fraudulent and malicious activities. A Data Collector and Mediator Unit monitors network traffic, and generates datasets of network traffic; each dataset includes network traffic monitored within a time-slot having a particular fixed time-length. A Predictor Unit includes a Features Extractor, to extract features from the datasets; and a Machine Learning (ML) unit, to run the extracted features through a ML model and to classify a particular traffic-portion as being either (I) an anomalous traffic-portion that is associated with fraudulent or malicious activity, or (II) a non-anomalous traffic-portion that is not-associated with fraudulent or malicious activity. The ML unit operates on both (i) anomalies in traffic patterns, and (ii) anomalies of user behavior and/or device behavior.

Abstract: System, device, and method of detecting and mitigating Domain Name Server (DNS) tunneling attacks in a communication network. A system includes a Data Collector Unit, to monitor outbound Domain Name System (DNS) queries that are outgoing from a communication network or from an end-user device, towards an entry node of the Internet or towards a firewall unit or towards a trusted DNS server. The Data Collector Unit generates datasets of outbound DNS queries, each dataset corresponding to outbound DNS queries that are associated with a particular time-slot. A DNS Tunneling Attack Detector Unit includes a feature extractor, to extract Machine Learning (ML) features from each dataset of outbound DNS queries; and also a ML unit, to run the extracted features through a ML model, and to classify a particular outbound DNS query as belonging to a DNS tunneling attack based on ML-based analysis and classification of the extracted features.

Abstract: The disclosure relates to photovoltaic modules comprising one or more photovoltaic cells embedded in a fiber-reinforced composite thermosetting material, wherein at a front side of the photovoltaic cells, the fiber-reinforced composite material comprises a substantially transparent resin, and substantially transparent fibers, and wherein the refractive indices of the resin and the glass fibers are substantially the same. In particular, the fibers can be glass fibers treated with aminosilane coupling agents and the resin can be an epoxy resin. Further disclosed are methods of manufacture of photovoltaic modules comprising one or more crystalline silicon photovoltaic cells comprising: providing a mold, one or more photovoltaic cells in the mold, and reinforcement fibers in the mold and positioning a bag surrounding the mold cavity. Then a vacuum is created in the bag substantially gradually, and the resin is infused with the mold due to the created vacuum.