cti-ATT-CK-v13.1/mobile-attack/attack-pattern/attack-pattern--2ccc3d39-9598-4d32-9657-42e1c7095d26.json

{
    "type": "bundle",
    "id": "bundle--072baa3c-d82d-4553-b4ce-288cca6f31c7",
    "spec_version": "2.0",
    "objects": [
        {
            "x_mitre_platforms": [
                "Android",
                "iOS"
            ],
            "x_mitre_domains": [
                "mobile-attack"
            ],
            "object_marking_refs": [
                "marking-definition--fa42a846-8d90-4e51-bc29-71d5b4802168"
            ],
            "type": "attack-pattern",
            "id": "attack-pattern--2ccc3d39-9598-4d32-9657-42e1c7095d26",
            "created": "2022-04-05T19:57:15.734Z",
            "x_mitre_version": "1.0",
            "external_references": [
                {
                    "source_name": "mitre-attack",
                    "external_id": "T1637",
                    "url": "https://attack.mitre.org/techniques/T1637"
                },
                {
                    "source_name": "Data Driven Security DGA",
                    "url": "https://datadrivensecurity.info/blog/posts/2014/Oct/dga-part2/",
                    "description": "Jacobs, J. (2014, October 2). Building a DGA Classifier: Part 2, Feature Engineering. Retrieved February 18, 2019."
                }
            ],
            "x_mitre_deprecated": false,
            "revoked": false,
            "description": "Adversaries may dynamically establish connections to command and control infrastructure to evade common detections and remediations. This may be achieved by using malware that shares a common algorithm with the infrastructure the adversary uses to receive the malware's communications. This algorithm can be used to dynamically adjust parameters such as the domain name, IP address, or port number the malware uses for command and control.",
            "modified": "2022-04-05T19:57:15.734Z",
            "name": "Dynamic Resolution",
            "x_mitre_detection": "Detecting dynamically generated domains can be challenging due to the number of different Domain Generation Algorithms (DGAs), constantly evolving malware families, and the increasing complexity of the algorithms. There are a myriad of approaches for detecting a pseudo-randomly generated domain name, including using frequency analysis, Markov chains, entropy, proportion of dictionary words, ratio of vowels to other characters, and more.(Citation: Data Driven Security DGA) Content delivery network (CDN) domains may trigger these detections due to the format of their domain names. In addition to detecting a DGA domain based on the name, a more general approach for detecting a suspicious domain is to check for recently registered names or rarely visited domains.",
            "kill_chain_phases": [
                {
                    "phase_name": "command-and-control",
                    "kill_chain_name": "mitre-mobile-attack"
                }
            ],
            "x_mitre_is_subtechnique": false,
            "x_mitre_tactic_type": [
                "Post-Adversary Device Access"
            ],
            "x_mitre_attack_spec_version": "2.1.0",
            "created_by_ref": "identity--c78cb6e5-0c4b-4611-8297-d1b8b55e40b5",
            "x_mitre_modified_by_ref": "identity--c78cb6e5-0c4b-4611-8297-d1b8b55e40b5"
        }
    ]
}