The Blueprint breaks down cybersecurity into two areas of action:

• Protecting our Critical Information Infrastructure Today
• and Building a Stronger Cyber Ecosystem for Tomorrow.

Israeli Minister of the Home Front Defense Matan Vilnai: Preparedness is Everything

“I have just returned from trip to Japan. The country is recovering from the triple blow of tsunami, earthquake and the following nuclear disaster. The only way to cope with such unfortunate events is to be prepared. Preparedness is everything.”

IEEE HST 2011, to be held 15-17 November 2011 in Boston, MA, will bring together global science and technology thought leaders to foster homeland security technology innovation. The conference is produced by IEEE with technical support from DHS S&T, IEEE Biometrics Council, IEEE Engineering in Medicine and Biology Society, and IEEE-USA, as well as with organizational support from MIT Lincoln Laboratory, Raytheon, and MITRE. The focus of the Call for Papers, Posters and Tutorials is on emerging technologies in the areas of: Cyber Security, Attack and Disaster Preparation, Recovery, and Response; Land and Maritime Border Security, and Biometrics, Forensics and Physical Security. Sponsors include Raytheon, Massport, Elbit Systems, and Teledyne Benthos. To learn more, visit www.ieee-hst.org or contact Robert Alongi, IEEE at information@ieee-hst.org at +1 781-245-5405.

A few months ago, in April 2011, the Workshop on Semantic Graph Database Search Patterns has taken place in Seattle, Washington, USA. One of the case studies presented at the workshop was Application of Semantic Graph Database Search Patterns in Law Enforcement (excerpt from the workshop’s report):

This data has ≈ 100K elements/records about people, places, and events, and on the order of millions of relationships between records. It is in relational form,
but with a rudimentary triple-based ontology.

Major features of the dataset include:

• Gang activity, sex offenders, burglary, etc.
• Roles: witness, victim, suspect, perpetrator– Events are temporal. Associations between people and addresses/phone number are temporal. Addresses are spatial, linked to people and organizations.
• Example of events: car prowl at a certain time, time of report of shots fired, not so much booking or jail-related, more focused on officers. However, history and records are important, like criminal records and people who were in jail together or shared a cell together.

Characteristics of useful queries include:

1. Shortest path queries (“Show how two people are connected”) are very useful, but tend to blow up for time or memory.
2. Who is associated with crimes over a specific geographic region? The spacial component becomes very difficult for boundary cases such as a car accident
   in the middle of a street that serves as a boundary for the law enforcement zones.
3. When a crime occurs, queries are requested that perform the task of landscaping, or trying to find anything related to the new crime, such as similar victims, locations, etc.

Finally, it would be useful to be able to search for probable crime spree patterns. Here a crime spree is loosely defined as a pattern of similar crimes following a feasible spatio-temporal sequence. For example, give all crimes in the “neighborhood” (defined) of a given space+time, and do so iteratively for all crimes in the neighborhood, until the neighborhood can no longer be expanded. The sequence can no longer be expanded when the time between two events are such that there was not sufficient time to move from the previous location to the next. Since the “time” of the crime is a fuzzy term—when did a break-in happen given when it was first noticed?—this constraint may come down to a confidence factor dipping below some threshold.