DISTRIBUTED AGENT BASED MODEL FOR SECURITY MONITORING AND RESPONSE

• US 20140237599A1
• Filed: 10/01/2013
• Published: 08/21/2014
• Est. Priority Date: 12/24/2002
• Status: Active Grant

• Current Assignee: Inventship LLC
• Original Assignee: Walter Paul Labys
• Inventors: Gertner, Yael, Herz, Frederick S.M., Labys, Walter Paul
• Granted Patent: US 9,503,470 B2
• Time in Patent Office: Days
• US Class Current: 726/24
• CPC Class Codes: H04L 63/1416 Event detection, e.g. attack signature detection
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 63/1441 Countermeasures against malicious traffic countermeasures against attacks on cryptographic mechanisms H04L9/002
• CPC Class Codes: H04L 63/145 the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms

Abstract

An architecture is provided for a widely distributed security system (SDI-SCAM) that protects computers at individual client locations, but which constantly pools and analyzes information gathered from machines across a network in order to quickly detect patterns consistent with intrusion or attack, singular or coordinated. When a novel method of attack has been detected, the system distributes warnings and potential countermeasures to each individual machine on the network. Such a warning may potentially include a probability distribution of the likelihood of an intrusion or attack as well as the relative probabilistic likelihood that such potential intrusion possesses certain characteristics or typologies or even strategic objectives in order to best recommend and/or distribute to each machine the most befitting countermeasure(s) given all presently known particular data and associated predicted probabilistic information regarding the prospective intrusion or attack. If any systems are adversely affected, methods for repairing the damage are shared and redistributed throughout the network.

Claims

1. A system that detects the state of a computer network, comprising:

a plurality of distributed agents disposed in said computer network, each said distributed agent comprising;

data collection means for passively collecting, monitoring, and aggregating data representative of activities of respective nodes within said computer network;

means responsive to the data from the data collection means for analyzing said data to develop activity models representative of activities of said computer network in a normal state and activities of said computer network in an abnormal state as a result of intrusions, infections, scams and/or other suspicious activities in said computer network; and

means for generating counter-offensive measures where unauthorized access to a program or file containing executable code results in the program or file disabling an operating system with all associated applications of a computer in the computer network until/unless the presumed attacker is able to prove to the machine owner/victim that the presumed attacker had been authorized to access the target data or machine provoking the said counter offensive measure.

2. A system as in claim 1, further comprising a distributed sensor network that aggregates and analyzes data to develop a probabilistic likelihood of a threat to safe code, machines, servers, or individuals, wherein said counter-offensive generating means generates counter-offensive measures that are targeted to a given threat or attack based upon historical feedback from successes and failures of previous counter measures used in response to similar attacks and threats.

3. A system as in claim 2, wherein said historical feedback updates an adaptive learning or adaptive rule base.

4. A system as in claim 2, wherein said distributed sensor network aggregates and analyzes data pertaining to software within the network in order to enable detection and characterization of vulnerabilities and/or provide recommendations for remedial repair or revision.

5. A system as in claim 1, wherein said counter-offensive generating means creates a bogus target for invoking attack on said bogus target for purposes of achieving early detection of a system infection.

6. A method of detecting the state of a computer network, comprising:

providing a plurality of distributed agents disposed in said computer network to passively collect, monitor, and aggregate data representative of activities of respective nodes within said computer network;

analyzing said data to develop activity models based on collected data and representative of activities of said network in a normal state and activities of said computer network in an abnormal state as a result of intrusions, infections, scams and/or other suspicious activities in said computer network, said data analysis including performing a pattern analysis on the collected data to identify patterns in the collected data representative of suspicious activities; and

generating counter-offensive measures where unauthorized access to a program or file containing executable code results in the program or file disabling an operating system with all associated applications of a computer in the computer network until/unless the presumed attacker is able to prove to the machine owner/victim that the presumed attacker had been authorized to access the target data or machine provoking the said counter offensive measure.

7. A method as in claim 6, further comprising aggregating and analyzing data to develop a probabilistic likelihood of a threat to safe code, machines, servers, or individuals, and generating counter-offensive measures that are targeted to a given threat or attack based upon historical feedback from successes and failures of previous counter measures used in response to similar attacks and threats.

8. A method as in claim 7, further comprising updating an adaptive learning or adaptive rule base with said historical feedback.

9. A method as in claim 7, further comprising aggregating and analyzing data pertaining to software within the network in order to enable detection and characterization of vulnerabilities and/or provide recommendations for remedial repair or revision.

10. A method as in claim 6, further comprising creating a bogus target for invoking attack on said bogus target for purposes of achieving early detection of a system infection.