Francophoned – A Sophisticated Social Engineering Attack. In April 2013, the administrative assistant to a vice president at a French-based multinational company received an email referencing an invoice hosted on a popular file sharing service. A few minutes later, the same administrative assistant received a phone call from another vice president within the company, instructing her to examine and process the invoice. The vice president spoke with authority and used perfect French. However, the invoice was a fake and the vice president who called her was an attacker.
The supposed invoice was actually a remote access Trojan (RAT) that was configured to contact a command-and-control (C&C) server located in Ukraine. Using the RAT, the attacker immediately took control of the administrative assistant’s infected computer. These tactics, using an email followed up by a phone call using perfect French, are highly unusual and are a sign of aggressive social engineering.
Aggressive tactics Victims Figure 1. Attacking on the move Figure 2. Texas Uses Data Visualization to Combat Medicaid Fraud. The Texas Office of Inspector General used the LYNXeon visualization tool to track connections among government payments, health care providers and Medicaid recipients. Image above is an illustration, courtesy of 21CT. Pinning down how much taxpayer money is lost to Medicaid fraud is difficult simply because the successful frauds go undetected. But the U.S. Government Accountability Office estimated that $32.7 billion (or 10 percent) of state Medicaid payments made in 2007 were improper.
Other estimates are much higher. It’s no wonder why. Consequently, there are a number of schemes used by providers and patients to defraud Medicaid. Billing for services not renderedDouble billingBilling for more hours than there are in a daySubstituting generic drugsBilling for more expensive procedures than performedKickbacks to nursing homesPersonal expenses in nursing home Medicaid claims “People who are committing fraud spend all day, every day thinking about it. Related Stories. Investigative Analytics and Pattern Detection - 21CT. User feedbacks. First Impression of LYNXeon 2.29 Let's say that you go to the same restaurant at least once a week for an entire year. The staff is always friendly, the menu always has something that sounds appealing, and the food is always good enough to keep you coming back for more.
The only real drawback is that it usually takes a solid half-hour to get your food, but you've learned to find something else to do while you're waiting because it's always been worth the wait. Today you go into the same restaurant, but now the staff goes out of their way to service you, the menu has twice as much selection as before, the food is literally the best thing you've ever tasted, and it was on your table just the way you like it within 30 seconds of placing your order.
This is my initial impression of the newly released version of 21CT's LYNXeon software (version 2.29). I'll be honest. Enhanced performance wasn't the only feature that found it's way into the 2.29 release. Now comes the fun part. What is pql ? SYSTEM AND METHOD FOR OPTIMIZING PATTERN QUERY SEARCHES ON A GRAPH DATABASE - Sargeant, Daniel. This application claims the priority of U.S.
Provisional Application Ser. No. 61/262,917, entitled “Pattern Query Optimizer and Method of Using Same” and filed Nov. 19, 2009, which is hereby incorporated by reference in its entirety. Appendix A contains source code for an exemplary implementation of an embodiment of a pattern query language (PQL) parser and lexer. In a database management system, a graph has one or more nodes (or vertices) that are connected by one or more edges (or links). Each node may have a type or class and at least one value associated with it.
A graph database refers to a collection of data that is stored in a graph data structure implemented in a database management system. Analysts often have the need to look for patterns in data that can be represented as subgraphs. To reduce the search time needed for a particular pattern query, it may be desirable to produce an optimal deconstruction of the pattern query. FIG. 1 illustrates an exemplary pattern query. 1. 2. 3. FireEye.