Abstract: The present disclosure relates to sustainable and green polylactic acid-based membranes embedded with self-assembled positively and negatively charged multiwalled carbon nanotube/graphene oxide nanohybrids for the removal of organic and inorganic nutrients from wastewater, and methods of synthesis of the same. A positively charged multi-walled carbon nanotube/graphene oxide (f-MWCNT/GO) nanohybrid-based mixed matrix membrane can comprise a self-assembled multi-walled carbon nanotube and graphene oxide (f-MWCNT/GO) nanohybrid, and a polylactic acid (PLA) membrane matrix. The f-MWCNT/GO nanohybrid is integrated into the PLA membrane matrix to form the positively charged mixed matrix membrane. A negatively charged multi-walled carbon nanotubes (f-GO/MWCNTs-COOH) nanohybrid-based mixed matrix membrane can comprise a positively charged Graphene Oxide and negatively charged multi-walled carbon nanotube-COOH (f-GO/MWCNTs-COOH) nanohybrid, and a polylactic acid (PLA) membrane matrix.

Abstract: The present disclosure provides new and innovative systems and methods for filtering encrypted messages. In an example, a computer-implemented method includes obtaining a message, determining sender profiling features of the message, determining enterprise graph features of the message, determining header features of the message, determining a message flag based on the sender profiling features, the enterprise graph features, and the header features, and processing the message based on the message flag.

Abstract: The present disclosure provides new and innovative systems and methods for filtering encrypted messages. In an example, a computer-implemented method includes obtaining a message, determining sender profiling features of the message, determining enterprise graph features of the message, determining header features of the message, determining a message flag based on the sender profiling features, the enterprise graph features, and the header features, and processing the message based on the message flag.

Abstract: The present disclosure provides new and innovative systems and methods for filtering encrypted messages. In an example, a computer-implemented method includes obtaining a message, determining sender profiling features of the message, determining enterprise graph features of the message, determining header features of the message, determining a message flag based on the sender profiling features, the enterprise graph features, and the header features, and processing the message based on the message flag.

Abstract: The present disclosure provides new and innovative systems and methods for filtering encrypted messages. In an example, a computer-implemented method includes obtaining a message, determining sender profiling features of the message, determining enterprise graph features of the message, determining header features of the message, determining a message flag based on the sender profiling features, the enterprise graph features, and the header features, and processing the message based on the message flag.

Abstract: Methods and systems for detecting malicious domains. The method comprises storing domain data for a plurality of domains and selecting a relationship parameter which represents a relationship between at least two of the domains. The method further comprises generating a graph for the domains by identifying a plurality of domain nodes, connecting the domain nodes with a plurality of edges and calculating an edge weight for each edge. The method further comprises identifying at least one domain node as a known malicious domain node and the other domain nodes as candidate domain nodes, calculating a malicious score for each candidate domain node based on the edge weights and identifying a domain in the plurality of domains as malicious if the malicious score is within a predetermined range.

Abstract: Methods and systems for detecting malicious domains. The method comprises storing domain data for a plurality of domains and selecting a relationship parameter which represents a relationship between at least two of the domains. The method further comprises generating a graph for the domains by identifying a plurality of domain nodes, connecting the domain nodes with a plurality of edges and calculating an edge weight for each edge. The method further comprises identifying at least one domain node as a known malicious domain node and the other domain nodes as candidate domain nodes, calculating a malicious score for each candidate domain node based on the edge weights and identifying a domain in the plurality of domains as malicious if the malicious score is within a predetermined range.