User-customized deceptions and their deployment in networks

Description

CROSS REFERENCES TO RELATED APPLICATIONS

The contents of the following of applicant's US patent applications are hereby incorporated herein in their entireties.

• • U.S. patent application Ser. No. 15/722,351, entitled SYSTEM AND METHOD FOR CREATION, DEPLOYMENT AND MANAGEMENT OF AUGMENTED ATTACKER MAP, and filed on Oct. 2, 2017 by inventors Shlomo Touboul, Hanan Levin, Stephane Roubach, Assaf Mischari, Itai Ben David, Itay Avraham, Adi Ozer, Chen Kazaz, Ofer Israeli, Olga Vingurt, Liad Gareh, Israel Grimberg, Cobby Cohen, Sharon Sultan and Matan Kubovsky.
  • U.S. patent application Ser. No. 15/403,194, now U.S. Pat. No. 9,787,715, entitled SYSTEM AND METHOD FOR CREATION, DEPLOYMENT AND MANAGEMENT OF AUGMENTED ATTACKER MAP, and filed on Jan. 11, 2017 by inventors Shlomo Touboul, Hanan Levin, Stephane Roubach, Assaf Mischari, Itai Ben David, Itay Avraham, Adi Ozer, Chen Kazaz, Ofer Israeli, Olga Vingurt, Liad Gareh, Israel Grimberg, Cobby Cohen, Sharon Sultan and Matan Kubovsky.
  • U.S. patent application Ser. No. 15/004,904, now U.S. Pat. No. 9,553,885, entitled SYSTEM AND METHOD FOR CREATION, DEPLOYMENT AND MANAGEMENT OF AUGMENTED ATTACKER MAP, and filed on Jan. 23, 2016 by inventors Shlomo Touboul, Hanan Levin, Stephane Roubach, Assaf Mischari, Itai Ben David, Itay Avraham, Adi Ozer, Chen Kazaz, Ofer Israeli, Olga Vingurt, Liad Gareh, Israel Grimberg, Cobby Cohen, Sharon Sultan and Matan Kubovsky.
  • U.S. Provisional Application No. 62/172,251, entitled SYSTEM AND METHOD FOR CREATION, DEPLOYMENT AND MANAGEMENT OF AUGMENTED ATTACKER MAP, and filed on Jun. 8, 2015 by inventors Shlomo Touboul, Hanan Levin, Stephane Roubach, Assaf Mischari, Itai Ben David, Itay Avraham, Adi Ozer, Chen Kazaz, Ofer Israeli, Olga Vingurt, Liad Gareh, Israel Grimberg, Cobby Cohen, Sharon Sultan and Matan Kubovsky.
  • U.S. Provisional Application No. 62/172,253, entitled SYSTEM AND METHOD FOR MULTI-LEVEL DECEPTION MANAGEMENT AND DECEPTION SYSTEM FOR MALICIOUS ACTIONS IN A COMPUTER NETWORK, and filed on Jun. 8, 2015 by inventors Shlomo Touboul, Hanan Levin, Stephane Roubach, Assaf Mischari, Itai Ben David, Itay Avraham, Adi Ozer, Chen Kazaz, Ofer Israeli, Olga Vingurt, Liad Gareh, Israel Grimberg, Cobby Cohen, Sharon Sultan and Matan Kubovsky.
  • U.S. Provisional Application No. 62/172,255, entitled METHODS AND SYSTEMS TO DETECT, PREDICT AND/OR PREVENT AN ATTACKER'S NEXT ACTION IN A COMPROMISED NETWORK, and filed on Jun. 8, 2015 by inventors Shlomo Touboul, Hanan Levin, Stephane Roubach, Assaf Mischari, Itai Ben David, Itay Avraham, Adi Ozer, Chen Kazaz, Ofer Israeli, Olga Vingurt, Liad Gareh, Israel Grimberg, Cobby Cohen, Sharon Sultan and Matan Kubovsky.
  • U.S. Provisional Application No. 62/172,259, entitled MANAGING DYNAMIC DECEPTIVE ENVIRONMENTS, and filed on Jun. 8, 2015 by inventors Shlomo Touboul, Hanan Levin, Stephane Roubach, Assaf Mischari, Itai Ben David, Itay Avraham, Adi Ozer, Chen Kazaz, Ofer Israeli, Olga Vingurt, Liad Gareh, Israel Grimberg, Cobby Cohen, Sharon Sultan and Matan Kubovsky.
  • U.S. Provisional Application No. 62/172,261, entitled SYSTEMS AND METHODS FOR AUTOMATICALLY GENERATING NETWORK ENTITY GROUPS BASED ON ATTACK PARAMETERS AND/OR ASSIGNMENT OF AUTOMATICALLY GENERATED SECURITY POLICIES, and filed on Jun. 8, 2015 by inventors Shlomo Touboul, Hanan Levin, Stephane Roubach, Assaf Mischari, Itai Ben David, Itay Avraham, Adi Ozer, Chen Kazaz, Ofer Israeli, Olga Vingurt, Liad Gareh, Israel Grimberg, Cobby Cohen, Sharon Sultan and Matan Kubovsky.

FIELD OF THE INVENTION

The present invention relates to computer security, and in particular to preventing attackers from breaching computer networks.

BACKGROUND OF THE INVENTION

Reference is made to FIG. 1, which is a simplified diagram of a prior art organization network 100 connected to an external internet 10. Network 100 is shown generally with resources including endpoint computers 110, databases 120, switches and routers 130, and mobile devices 140 such as smart phones and tablets, for ease of presentation, although it will be appreciated by those skilled in the art that organization networks today are generally much more complex and include other devices such as printers, other types of network elements such as relays, and any Internet of Things objects. The various connections shown in FIG. 1 may be direct or indirect, wired or wireless communications, or a combination of wired and wireless connections. Endpoint computers 110 and databases 120 may be physical elements or logical elements, or a mix of physical and logical elements. Endpoint computers 110 and databases 120 may be virtual machines. Endpoint computer 110 and databases 120 may be local, remote or cloud-based elements, or a mix of local, remote and cloud-based elements. Endpoint computers 110 may be client workstation computers, or server computers including inter alia file transfer protocol (FTP) servers, email servers, structured query language (SQL) servers, secure shell (SSH) servers and other application servers, or a mix of client and server computers. An organization's information technology (IT) department manages and controls network 100 in order to serve the organization's requirements and meet the organization's needs.

Access to endpoint computers 110 and databases 120 in network 100 may optionally be governed by an access governor 150, such as a directory service, that authorizes users to access endpoint computers 110 and databases 120 based on “credentials”. Access governor 150 may be a name directory, such as ACTIVE DIRECTORY® developed by Microsoft Corporation of Redmond, Wash., for WINDOWS® environments. Background information about ACTIVE DIRECTORY is available at Wikipedia. Other access governors for WINDOWS and non-WINDOWS environments, include inter alia Lightweight Directory Access Protocol (LDAP), Remote Authentication Dial-In User Service (RADIUS), and Apple Filing Protocol (AFP), formerly APPLETALK®, developed by Apple Inc. of Cupertino, Calif. Background information about LDAP, RADIUS and AFP is available at Wikipedia.

Access governor 150 may be one or more local machine access controllers. Access governor 150 may be one or more authorization servers, such as a database server or an application server.

In lieu of access governor 150, the endpoints and/or servers of network 100 determine their local access rights.

Credentials for accessing endpoint computers 110 and databases 120 include inter alia server account credentials such as <address> <username> <password> for an FTP server, an SQL server, or an SSH server. Credentials for accessing endpoint computers 110 and databases 120 also include user login credentials <username> <password>, or <username> <ticket>, where “ticket” is an authentication ticket, such as a ticket for the Kerberos authentication protocol or NTLM hash used by Microsoft Corp., or login credentials via certificates or via another implementation used today or in the future. Background information about the Kerberos protocol and the LM hash is available at Wikipedia.

Access governor 150 may maintain a directory of endpoint computers 110, databases 120 and their users. Access governor 150 authorizes users and computers, assigns and enforces security policies, and installs and updates software. When a user logs into an endpoint computer 110, access governor 150 checks the submitted password, and determines if the user is an administrator (admin), a normal user (user) or other user type.

Endpoint computers 110 may run a local or remote security service, which is an operating system process that verifies users logging in to computers and other single sign-on systems and other credential storage systems.

Network 100 may include a security information and event management (SIEM) server 160, which provides real-time analysis of security alerts generated by network hardware and applications. Background information about SIEM is available at Wikipedia.

Network 100 may include a domain name system (DNS) server 170, or such other name service system, for translating domain names to IP addresses. Background information about DNS is available at Wikipedia.

Network 100 may include a firewall 180 located within a demilitarized zone (DMZ), which is a gateway between organization network 100 and external internet 10. Firewall 180 controls incoming and outgoing traffic for network 100. Background information about firewalls and DMZ is available at Wikipedia.

One of the most prominent threats that organizations face is a targeted attack; i.e., an individual or group of individuals that attacks the organization for a specific purpose, such as leaking data from the organization, modifying data and systems, and sabotaging data and systems.

Targeted attacks are carried out in multiple stages, typically including inter alia reconnaissance, penetration, lateral movement and payload. Lateral movement involves establishing a foothold within the organization and expanding that foothold to additional systems within the organization.

In order to carry out the lateral movement stage, an attacker, whether a human being who is operating tools within the organization's network, or a tool with “learning” capabilities, learns information about the environment it is operating in, such as network topology, organization structure, and implemented security solutions, and then operates in accordance with that data. One method to defend against such attacks is to plant misleading information/decoys/bait with the aim that the attacker learns of their existence and consumes those bait resources, which are monitored so as to notify an administrator of malicious activity. In order to monitor usage of deceptive information, decoy servers, referred to as “honeypots”, are deployed in the organization. Background information about honeypots is available at Wikipedia.

Decoy servers try to mimic attractive real servers. However, a challenge in deploying decoy servers is to make then appear authentic. Specifically, an effective honeypot needs to appear reliable to an attacker, in particular matching attributes of real hosts such as operating system types, and local installed products.

Planting deceptions that appear reliable is a challenging issue. Conventionally, a lot of research must be carried out in order to generate reliable deceptions to plant in network resources, and the administrator, or such other user, does of have the capability to customize deceptions. I.e., the selection of deceptions is limited to those that the conventional security system provides.

SUMMARY

Embodiments of the present invention enable an administrator or other such user to generate custom deceptions. The administrator or such other user can conduct their own research and plant their own generated deceptions, irrespective of what conventional security systems offer.

Embodiments of the present invention provide a formal language for defining customize deceptions, and a formal language translation unit to generate deceptions that are installable in endpoints of the network.

There is thus provided in accordance with an embodiment of the present invention a system for generating and deploying custom deceptions for a network, including an administrator computer for generating custom deception entities (CDEs), each CDE including parameters including inter alia (i) a type of entity, (ii) conditions for deployment of the CDE, and (iii) a deception type, and a management server, comprising an application programming interface for use by the administrator computer to generate CDEs through the medium of a formal language for specifying deceptions, and a translator for translating formal language CDEs to deceptions that are installable in network endpoint computers, wherein the management computer receives a request from a network endpoint computer to retrieve CDEs, selects CDEs that are relevant to the requesting network endpoint computer based on the parameters of the CDE, translates the requested CDEs to installable deceptions, and transmits the installable deceptions to the network endpoint computer for installation thereon.

There is additionally provided in accordance with an embodiment of the present invention a method performed by a management server of a network for generating and deploying custom deceptions for the network, including generating custom deception entities (CDEs) through the medium of a formal language for specifying deceptions, each CDE including parameters including inter alia (i) a type of entity, (ii) conditions for deployment of the CDE, and (iii) a deception type, receiving a request from a network endpoint computer to retrieve CDEs, selecting CDEs that are relevant to the requesting network endpoint computer based on the parameters of the CDE, translating the selected CDEs from their formal language description to installable deceptions, and transmitting the installable deceptions to the network endpoint computer for installation thereon.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be more fully understood and appreciated from the following detailed description, taken in conjunction with the drawings in which:

FIG. 1 is a simplified diagram of a prior art enterprise network connected to an external internet;

FIG. 2 is a simplified diagram of a system that generates and deploys custom deceptions, in accordance with an embodiment of the present invention;

FIG. 3 is a simplified diagram showing how custom deceptions are deployed, in accordance with an embodiment of the present invention; and

FIG. 4 is a simplified flowchart of a method for generating and deploying custom deceptions, in accordance with an embodiment of the present invention.

For reference to the figures, the following index of elements and their numerals is provided. Similarly numbered elements represent elements of the same type, but they need not be identical elements.

Elements numbered in the 1000's are operations of flow charts.

DETAILED DESCRIPTION

In accordance with embodiments of the present invention, systems and methods are provided for generating, deploying and managing custom deceptions. These systems and methods employ a special formal language for defining deceptions of various types.

Reference is made to FIG. 2, which is a simplified diagram of a system that generates and deploys custom deceptions, in accordance with an embodiment of the present invention. In addition to the conventional components of FIG. 1, FIG. 2 shows a network 200 that includes a management server 210 and a policy database 230. FIG. 2 also shows several endpoint computers 110 that have custom deceptions, designated D, planted therewithin. FIG. 2 also shows an endpoint computer used by an administrator of network 200, designated A.

Management server 210 includes an application programming interface (API) 211 for generating, editing and deleting custom deceptions, a translator 212 for translating from the native formal language for defining deceptions to actual deceptions that may be installed in endpoints of network 200, a deployer 213 that changes the deployment flow so as to include installation or uninstallation of custom deceptions. Generally, the administrator of network 200 generates, edits and deletes the custom deceptions using API 211.

Management server 210 also includes a forensic application 214, which is a real-time application that is transmitted to an endpoint computer 110 in the network, when a deception is accessed by that computer 110, this indicating that that computer has been breached by an attacker. When forensic application 214 is launched on the endpoint computer, it identifies a process running within that endpoint computer 110 that accessed the deception, logs the activities performed by the thus-identified process in a forensic report, and transmits the forensic report to management server 210.

Management server 210 also includes a policy manager 215. Policy manager 215 defines a decoy and response policy. The response policy defines different decoy types, different decoy combinations, response procedures, notification services, and assignments of policies to specific network nodes, network users, groups of nodes or users or both. Once policies are defined, they are stored in policy database 230 with the defined assignments.

Each decoy server 240 includes a forensic alert module 242, which alerts management system 210 that an attacker is accessing the decoy server via an endpoint computer 110 of the network, and causes management server 210 to send forensic application 214 to the endpoint computer 110 that is accessing the decoy server. In an alternative embodiment of the present invention, decoy server 240 may store forensic application 214, in which case decoy server 240 may transmit forensic application 214 directly to the endpoint computer 110 that is accessing decoy server 240. In another alternative embodiment of the present invention, management server 210 or decoy server 240 may transmit forensic application 214 to a destination computer other than the endpoint computer 110 that is accessing decoy server 240.

Based on a current policy, deployer 213 receives a request from an endpoint computer 110 for deceptions to install, and deployer 213 decides what should be installed and what should be uninstalled, based on inter alia the assignment of endpoint computer 110 and the deception settings. CDGs are assigned to policies. If a CDG is assigned to a current policy that includes endpoint computer 110, then deployer 213 sends the CDG to endpoint computer 110 for installation. Otherwise, if the current policy does not include endpoint computer 110 and the CDG was previously installed on endpoint computer 110, then deployer 213 sends an instruction to endpoint computer 110 to uninstall the CDG.

Reference is made to FIG. 3, which is a simplified diagram showing how custom deceptions are deployed by deployer 213, in accordance with an embodiment of the present invention. FIG. 3 shows three phases, as follows.

• • 1. Endpoint computer 110 sends a “get deception to install” request to management server 210;
  • 2. Management server 210 determines the relevant deceptions for the specific endpoint computer 110, based on the endpoint computer context, such as logged in users and running processes, and translates the relevant deceptions from their native formal language description to an actual deception that is installable on endpoint computer 110; and
  • 3. Management server 210 transmits to endpoint computer 110 relevant deceptions to install/uninstall, based on a snapshot of endpoint computer 110.

In order to add a CDG to the flow, when management server 210 determines the relevant deceptions to install, management server 210 chooses from the regular deceptions and the CDGs that are configured on the system. Translator 212 then validates the conditions of the CDG, and translates the CDG from their formal language description into an actual deception to install.

When endpoint computer 110 finishes the installation process, it sends the status of each deception to management server 210. Management server 210 saves the status in a snapshot of endpoint computer 110. As such, when the administrator or such other user deletes the CDG, or unassigns endpoint computer 110 from the policy, management server 210 knows the exact file or registry to look for, in order to uninstall the CDG.

Formal Deception Language

A basic construct is a custom deception entity (CDE), which has parameters inter alia as shown in TABLE II below.

The type parameter specifies whether the deception is a file-based custom deception, a registry-based custom deception, or an executable deception. TABLES III-V below indicate inter alia the parameters for each of these types of deceptions.

The following keywords inter alia are used for the formal deception language. Each keyword is to be replaced with actual deceptive content.

• • <SERVER_NAME1>, <SERVER_NAME2>, . . .
  • <USER_NAME1>, <USER_NAME2>, . . .
  • <PASSWORD1>, <PASSWORD2>, . . .
  • <PORT1>, <PORT2>, . . .
  • <HKEY_USERS>
  • <HOME_FOLDER>
  • <PERMISSION>
  • <HIDDEN>

A custom deception group (CDG) is a set of CDEs. Custom deceptions API 211 exposes inter alia the following methods.

• • GET returns the CDGs as configured in the system
  • POST CREATE CDG receives a CDG as input, and generates the CDG in the system
  • DELETE CDG receives a CDG as input, and deletes the CDG from the system

The input to translator 212 is combined with custom deception parameters and the contextual parameters vis-à-vis the specific endpoint; e.g., contextual users, running processes and installed applications. Translator 212 replaces keywords with the given input. E.g., if the deception type is SSH, management server 210 chooses an SSH trap server and replaces <SERVER_NAME> with the name of the trap server. If there are multiple <SERVER_NAME> keywords, then management server 210 finds a number of trap servers to replace the keywords.

It is noted that each CDG has its own specific conditions; e.g., processes that run on the system, and a file that exists on the file system. When translator 212 translates a CDG to deceptions, translator 212 must validate that the conditions are met in order to transmit the deceptions.

It will be appreciated by those skilled in the art that the above formal language for defining deceptions may be extended. E.g., plant deception file_deception(name, location, file_name, permission, visibility, content) with values(“deception_name”, “C:\Users”, “file.txt”, “READ-ONLY”, “hidden”, “content with <SERVER_NAME> and <USER_NAME>) where “process1” is in running_processes and operating_system=“Windows”

In the foregoing specification, the invention has been described with reference to specific exemplary embodiments thereof. It will, however, be evident that various modifications and changes may be made to the specific exemplary embodiments without departing from the broader spirit and scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.