Artificial Intelligence-Driven Composition and Security Validation of an Internet of Things Ecosystem
- Extension with metrics of the system composition and management procedures. This enables the calculation of the SPD for the currently composed system. Metrics are evolving as an integral characteristic of systems’ development as they are utilized for the comparison of the various possible system configurations, as well as, the impact quantification of the ongoing changes in the main setting.
- Verification that the various components can be composed into the examined system. This becomes imperative for heterogeneous environments (settings with different device types) and system-of-systems (a system comprised of many subsystems).
- Validation of properties that hold after the composition of two systems. This capability is useful in order to assure that the finally composed system fulfills the designed specifications and works correctly.
- Automated reactive strategies and moving-target defenses (MTDs). The IoT ecosystem is dominant by machine-to-machine (M2M) communications. Therefore, we need means to make the overall system self-adaptable to changes, failures, and/or other ongoing events. MTD is also such an approach in the cyber-security field . MTDs include proactive altering of the system configuration, networking, and/or topology to increase the malicious effort for analyzing the system in normal operation, as well as, reactive operations to respond to specific types of ongoing attacks.
2. Related Work
2.1. System Composition
2.1.1. UML-Based Proposals
2.1.2. Logic-Based Proposals
2.2. Security Validation
2.3. Quantifying Security
3. Materials and Methods
3.1. SPD Multi-Metric
3.1.1. Attack Surface
3.1.2. Protection Level
- Required time: The period that it takes (e.g., days or weeks) to detect and exploit a limitation.
- Expertise: The technical knowledge and skills required (e.g., copy-cat, advanced, or expert).
- Knowledge of the target: Familiarity with the victim’s system and operation (e.g., public, sensitive, or critical knowledge regarding some subsystems, etc.).
- Window of opportunity: Hackers may need appreciable access to the system to successfully presume upon a threat while avoiding detection.
- Resources: The required software, hardware, or other equipment (e.g., common or specialized resources).
3.2. Medieval Castle Approach
- AND-operation: 0 ≤ d ≤ dmin
- OR-operation: dmax ≤ d ≤ 1
- MEAN-operation: dmin ≤ d ≤ dmax
3.3. Composed SPD Multi-Metric
4.3.1. Process Operation
|Algorithm 1: OperationEvaluation|
|Input: The operation identifier op.|
|Output: The operation’s <SPD>.|
|foreach timepoint ti in opdo|
|foreach attribute attri in SequentialAttrsdo|
|operationSPD = SequentialAttrs.getFinalAttr().EvaluateSPD();|
4.3.2. Composition Operation
|Algorithm 2: OperationApplicabilityCheck|
|Input: The component C, the set of operations setop.|
|Output: TRUE/FALSE: checks if the operation can be performed or not.|
|foreach operation opi in opdo|
|opiAttrs = GetOperationAttrs(opi);|
|if (!ContainsAllAttrs(C, opiAttrs)) then|
4.4. System Components
- possess sources–processed data;
- deploy attributes–technologies and protocols;
- execute operations–series of attributes;
- contain sub-components–components of lower layers;
- offer specific SPD levels–based on the assessed metrics and their underlying sub-components.
|Input: The component C.|
|Output: The component’s <SPD>.|
|structuralSPD = MedievalCastleSPD(C);|
|constraintSPD = MinRelativeComponentSPD(C);|
|componentSPD = structuralSPD;|
|if (structuralSPD > constraintSPD) then|
|componentSPD = constraintSPD;|
4.5. Composition and Decomposition
|Algorithm 4: Composition|
|Input: The component C, a subcomponent subC.|
|Output:TRUE/FALSE: checks if the composition is performed or not.|
|currentSPD = C.SPD;|
|if(!C.ComplyWithFunctionalPre(subC) or !C.ComplyWithNonFunctionalPre(subC))then|
|Return(FALSE); //Composition is blocked|
|C.compose(subC); //Composition is performed|
|if (ComponentSPD(C) != currentSPD) then|
|Return(FALSE); //Composition is blocked|
|for each subcomponent of C do|
5. Compositions of SPD Metrics
5.1. Composition Verification Definitions
- Assume QCompOp as the set of composition operations that all qcomp’s sub-components have to be capable to execute.
- Assume QCompCons as a set of constraints along with the minimum SPD values that all qcomp’s sub-components have to satisfy.
- A componentat the adjacent lower layers of qcomp, could be incorporated to qcomp if it can perform an operationand grantify all the constraints in QCompCons. The composition event is modelled as. After a successful composition at S’, it holds that. A component qcomp2 could be disintegrate from qcomp at any time-point. The decomposition event is modelled as. After a successful decomposition at S′, it holds that.
5.2. SPD Validation Definitions
- Assume cons_comp_pre as the pre-conditions of this constraint at qcomp’s layer.
- For a sub-layer i, assume cons_subcomp_pre_i as pre-conditions of this the constraint at sub-layer i.
- the constraint cons holds iff qcomp fullfils cons_comp_pre and each sub-componentof sub-layer i fulfils the related cons_subcomp_pre_i.
- if a sub-componentof sub-layer i violates the related cons_subcomp_pre_i, then disintegrate the sub-component:.
- Assume mParam as the parameters of the system which influence m.
- If the state of mParam changes, m’s SPD level is re-calculated:.
- If the state of qcompsub changes, qcomp’s SPD level is re-calculated:.
- If the state of qcomp changes, qcompsub’s SPD level is re-calculated:.
6.1. System Layers
6.2. Motivating Example
6.3. Technical Details
6.3.1. Reasoning System and Agent Technologies
6.3.2. Middleware and Service Platform
6.3.3. Platform Security Features
- Policy Enforcement Point (PEP): lays in the node layer imposing the access control at the device-end
- Policy administration Point (PAP): runs in middleware layer maintaining the access policies
- Policy Decision Point (PDP): also runs in the middleware, assessing the applicable policies which make the authorization decisions
- Policy Information Point (PIP): deployed in the middleware as the framework’s repository for the offered features
6.5. Levels of SPD
6.6. Composition and SPD Assessment
6.7. Reactive Strategies & MTDs
6.8. Performance Assessment
7.1. Validation of the Derived SPD Values
7.2. Future Work
Conflicts of Interest
|Authentication||Challenge of credentials based on authorization and identification|
|Resilience||Preserve protection in cases of failure or corruption|
|Subjugation||Assure that interactions happen based on a defined plan, removing liability and freedom of choice of the interacting entity in case of disclosure|
|Continuity||Preserve activity when failure or corruption occurs|
|Indemnification||Formal agreement between the asset owner and other interacting entities. May involve warnings as a precursor of public legislative protection and legal action|
|Consent||Freely given, informed, and specific agreement of the personal identifiable information (PII) principal for processing the PII. PII should not be shared or disclosed to third parties without consent from the PII principal|
|Opt-in||Policy or procedures under which the PII principal consents for the explicit processing actions of the PII, prior to relevant consent|
|Indentifiability||The ability to infer the identity of the PII principal, directly or indirectly, by a given set of PII. It may subsume complete indentifiability, pseudonymization or anonymity.|
|Notification||Inform the PII principals whose data is being gathered regarding such collection|
|Survivability||Available degraded operations that are useful and acceptable to users when a failure occurs for a specified period|
|Performability||Operations that assures how well the system will perform in the presence of faults for a specified period|
|Removal during use||Mechanisms that record and remove faults via the maintenance cycle after the production stage|
|Confidentiality||Assurance that a processed asset is not known outside the interacting entities|
|Integrity||Assurance that the interacting entities know when an asset has been modified|
|Non-repudiation||Obstructs the interacting entities for denying their role in an occurred interaction|
|Alarm||Notification that an interaction is happening or has happened|
|Fairness||PII should be collected, used or disclosed for appropriate and limited purposes|
|Challenge compliance (accountability)||PII principals should be able to hold PII processors accountable for complying with all privacy controls|
|Retention||Insurance that PII which is no longer required, is not retained in order to limit unauthorized collection, usage and disclosure|
|Disposal||Mechanisms for the disposal or destruction of PII|
|Report||Report that an interaction regarding PII is occurring or has occurred|
|Break/incident response||Procedure for managing a breach concerning PII|
|Tolerance||Insurance that the needed functionality will be delivered in the presence of faults|
|Forecasting||Prediction of possible faults so that they can be removed or their effects can be circumvented|
|Prevention||Obstructs faults from being integrated into a system. It is achieved by using good implementation techniques and development techniques|
|Removal during development||Verification of a system in order to detect and remove faults before the system is put into production|
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|SPD Property||Damage Potential||Effort|
-Authenticated user: 3
|Access rights |
-Authenticated user: 3
-Anonymous user: 1
-Unauthenticated user: 1
|Privacy||PII (personal identifiable information) type |
-Sensitive personal data: 5
-Personal data: 4
|PII actuator |
-PII principal: 4
-Contracted PII processor: 3
-Third party: 2
-Mission critical: 4
-Business critical: 3
-Business operational: 2
-Business supporting: 1
|Dependency level |
-Outgoing dependency: 3
-Incoming dependency: 2
-Independent operation: 1
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Hatzivasilis, G.; Papadakis, N.; Hatzakis, I.; Ioannidis, S.; Vardakis, G. Artificial Intelligence-Driven Composition and Security Validation of an Internet of Things Ecosystem. Appl. Sci. 2020, 10, 4862. https://doi.org/10.3390/app10144862
Hatzivasilis G, Papadakis N, Hatzakis I, Ioannidis S, Vardakis G. Artificial Intelligence-Driven Composition and Security Validation of an Internet of Things Ecosystem. Applied Sciences. 2020; 10(14):4862. https://doi.org/10.3390/app10144862Chicago/Turabian Style
Hatzivasilis, George, Nikos Papadakis, Ilias Hatzakis, Sotiris Ioannidis, and George Vardakis. 2020. "Artificial Intelligence-Driven Composition and Security Validation of an Internet of Things Ecosystem" Applied Sciences 10, no. 14: 4862. https://doi.org/10.3390/app10144862