System and method for graphical reticulated attack vectors for internet of things aggregate security (gravitas)
According to various embodiments, a system for detecting security vulnerabilities in at least one of cyber-physical systems (CPSs) and Internet of Things (IoT) devices is disclosed. The system includes one or more processors configured to construct an attack directed acyclic graph (DAG) unique to each CPS or IoT device of the devices. The processors are further configured to generate an aggregate attack DAG from a classification of each device and a location of each device in network topology specified by a system administrator. The processors are also configured to calculate a vulnerability score and exploit risk score for each node in the aggregate attack DAG. The processors are further configured to optimize placement of defenses to reduce an adversary score of the aggregate attack DAG.
1 . A system for detecting security vulnerabilities in at least one of cyber-physical systems (CPSs) and Internet of Things (IoT) devices, the system comprising one or more processors configured to:
construct an attack directed acyclic graph (DAG) unique to each CPS or IoT device of the devices, each attack DAG comprising a first plurality of nodes, each node of the first plurality representing a system-level operation of the device, a plurality of paths, each path representing an attack vector of the device, and a second plurality of nodes, each node of the second plurality representing an exploit goal of the device;
generate an aggregate attack DAG from a classification of each device and a location of each device in network topology specified by a system administrator, where classification comprises at least one of a purpose and physical limitation of the device;
calculate a vulnerability score and exploit risk score for each node in the aggregate attack DAG; and
optimize placement of defenses to reduce an adversary score of the aggregate attack DAG.
2 . The system of claim 1 , wherein the first plurality of nodes further comprises root nodes and the second plurality of nodes further comprises leaf nodes.
3 . The system of claim 1 , wherein a location of each device in network topology comprises whether each device has a wired or wireless connection to other devices in a local network.
4 . The system of claim 1 , wherein a location of each device in network topology comprises whether each device can connect to an external network.
5 . The system of claim 1 , wherein the processors are further configured to generate a set of potential defenses to be applied during optimization.
6 . The system of claim 1 , wherein the processors are further configured to generate an aggregate attack DAG from permissions for each device specified by the system administrator.
7 . The system of claim 6 , wherein the permissions comprise at least one of login permissions and execute command permissions.
8 . The system of claim 1 , wherein the processors are further configured to calculate each vulnerability score based on exploitability factors of the device and adjust the vulnerability score based on any defenses applied.
9 . The system of claim 1 , wherein each vulnerability score is calculated once.
10 . The system of claim 1 , wherein the processors are further configured to calculate each exploit risk score with a probabilistic union function based on corresponding vulnerability scores.
11 . The system of claim 1 , wherein each exploit risk score is recalculated when a new defense is added.
12 . The system of claim 1 , wherein the processors are further configured to optimize placement of defenses to minimize objection functions specified by the system administrator.
13 . The system of claim 1 , wherein the adversary score is based on an adversary model specified by the system administrator and a function of the exploit risk scores for each node in the aggregate attack DAG.
14 . A method for detecting security vulnerabilities in at least one of cyber-physical systems (CPSs) and Internet of Things (IoT) devices, the method comprising:
constructing an attack directed acyclic graph (DAG) unique to each CPS or IoT device of the devices, each attack DAG comprising a first plurality of nodes, each node of the first plurality representing a system-level operation of the device, a plurality of paths, each path representing an attack vector of the device, and a second plurality of nodes, each node of the second plurality representing an exploit goal of the device;
generating an aggregate attack DAG from a classification of each device and a location of each device in network topology specified by a system administrator, where classification comprises at least one of a purpose and physical limitation of the device;
calculating a vulnerability score and exploit risk score for each node in the aggregate attack DAG; and
optimizing placement of defenses to reduce an adversary score of the aggregate attack DAG.
15 . The method of claim 14 , wherein the first plurality of nodes further comprises root nodes and the second plurality of nodes further comprises leaf nodes.
16 . The method of claim 14 , wherein a location of each device in network topology comprises whether each device has a wired or wireless connection to other devices in a local network.
17 . The method of claim 14 , wherein a location of each device in network topology comprises whether each device can connect to an external network.
18 . The method of claim 14 , further comprising generating a set of potential defenses to be applied during optimization.
19 . The method of claim 14 , further comprising generating an aggregate attack DAG from permissions for each device specified by the system administrator.
20 . The method of claim 19 , wherein the permissions comprise at least one of login permissions and execute command permissions.
21 . The method of claim 14 , further comprising calculating each vulnerability score based on exploitability factors of the device and adjusting the vulnerability score based on any defenses applied.
22 . The method of claim 14 , wherein each vulnerability score is calculated once.
23 . The method of claim 14 , further comprising calculating each exploit risk score with a probabilistic union function based on corresponding vulnerability scores.
24 . The method of claim 14 , wherein each exploit risk score is recalculated when a new defense is added.
25 . The method of claim 14 , further comprising optimizing placement of defenses to minimize objection functions specified by the system administrator.
26 . The method of claim 14 , wherein the adversary score is based on an adversary model specified by the system administrator and a function of the exploit risk scores for each node in the aggregate attack DAG.
27 . A non-transitory computer-readable medium having stored thereon a computer program for execution by a processor configured to perform a method for detecting security vulnerabilities in at least one of cyber-physical systems (CPSs) and Internet of Things (IoT) devices, the method comprising:
constructing an attack directed acyclic graph (DAG) unique to each CPS or IoT device of the devices, each attack DAG comprising a first plurality of nodes, each node of the first plurality representing a system-level operation of the device, a plurality of paths, each path representing an attack vector of the device, and a second plurality of nodes, each node of the second plurality representing an exploit goal of the device;
generating an aggregate attack DAG from a classification of each device and a location of each device in network topology specified by a system administrator, where classification comprises at least one of a purpose and physical limitation of the device;
calculating a vulnerability score and exploit risk score for each node in the aggregate attack DAG; and
optimizing placement of defenses to reduce an adversary score of the aggregate attack DAG.
28 . The non-transitory computer-readable medium of claim 27 , wherein the first plurality of nodes further comprises root nodes and the second plurality of nodes further comprises leaf nodes.
29 . The non-transitory computer-readable medium of claim 27 , wherein a location of each device in network topology comprises whether each device has a wired or wireless connection to other devices in a local network.
30 . The non-transitory computer-readable medium of claim 27 , wherein a location of each device in network topology comprises whether each device can connect to an external network.
31 . The non-transitory computer-readable medium of claim 27 , wherein the method further comprises generating a set of potential defenses to be applied during optimization.
32 . The non-transitory computer-readable medium of claim 27 , wherein the method further comprises generating an aggregate attack DAG from permissions for each device specified by the system administrator.
33 . The non-transitory computer-readable medium of claim 32 , wherein the permissions comprise at least one of login permissions and execute command permissions.
34 . The non-transitory computer-readable medium of claim 27 , wherein the method further comprises calculating each vulnerability score based on exploitability factors of the device and adjusting the vulnerability score based on any defenses applied.
35 . The non-transitory computer-readable medium of claim 27 , wherein each vulnerability score is calculated once.
36 . The non-transitory computer-readable medium of claim 27 , wherein the method further comprises calculating each exploit risk score with a probabilistic union function based on corresponding vulnerability scores.
37 . The non-transitory computer-readable medium of claim 27 , wherein each exploit risk score is recalculated when a new defense is added.
38 . The non-transitory computer-readable medium of claim 27 , wherein the method further comprises optimizing placement of defenses to minimize objection functions specified by the system administrator.
39 . The non-transitory computer-readable medium of claim 29 , wherein the adversary score is based on an adversary model specified by the system administrator and a function of the exploit risk scores for each node in the aggregate attack DAG.