The paper introduces a comprehensive model and accompanying methodology for constructing adaptive security profiles aimed at protecting wireless networks under dynamic cyber-threat conditions. The significance of the study stems from the ever-escalating complexity of attacks targeting wireless infrastructures, the widespread emergence of hybrid threat scenarios, the continual expansion of clientdevice diversity (including IoT endpoints), and the increasingly rapidly evolving risk landscape. The investigation was conducted with explicit reference to contemporary international cybersecurity standards-IEEE 802.11ax/802.11be, ISO/IEC 27033, ISO/IEC 15408, and NIST SP 800-53-and was guided by the principles of Zero-Trust architecture, which advocates a context-sensitive approach to designing access-control, authentication, encryption, and monitoring policies. The proposed model facilitates the creation of a family of security profiles that can be dynamically updated in accordance with the prevailing threat level, interaction modality, trust degree assigned to participating nodes, and detected behavioral anomalies within network traffic. The developed methodology incorporates a multifactorial risk assessment that considers the technological characteristics of the transmission medium, observed attack activity, interference levels, and the specific access context. Consequently, the model can underpin automated solutions that enhance cyber-resilience, certify the security posture of wireless networks, and enable dynamic, real-time audit mechanisms.
In the current era of digital transformation and the rapid deployment of wireless data-transmission technologies, the demand for reliable, adaptive, and resilient protection of network infrastructure is steadily increasing. Wireless networks that adhere to the IEEE 802.11ax and 802.11be standards are now pervasive across corporate, industrial, governmental, and public domains, yet they remain among the most vulnerable components of the contemporary information-and-communication ecosystem. The open radio spectrum, highly dynamic connection topology, and elevated mobility of client devices—including numerous IoT endpoints—create favorable conditions for sophisticated, multi-stage attack campaigns, thereby necessitating a fundamental re-evaluation of existing security mechanisms.
Concurrently, regulatory bodies are intensifying their demands for the formal certification of wireless-system security, stipulating that audits must evaluate not only architectural and technical parameters but also each system’s capacity to react rapidly to shifting risk levels, detect anomalous behaviour, and adapt access-control policies to the prevailing threat context. Conventional, rulebased solutions—anchored in static configurations—frequently fail to satisfy the flexibility and scalability imperatives of today’s cyber landscape. The emergence of the Zero-Trust Architecture (ZTA) paradigm, which mandates the verification of every request irrespective of origin or network location, further accentuates the urgency for novel, dynamically adaptive security mechanisms.
The study advances an approach that constructs a formalised model for adaptive security-profile management in wireless networks, thereby enabling not only the automated generation and continual updating of security profiles, but also the dynamic adjustment of individual security parameters with respect to the actual network state, observed behavioural deviations, devicespecific trust levels, traffic intensity, and the activity of prospective threats. Moreover, the integration of the proposed solution with comprehensive security-monitoring platforms—such as security information and event management (SIEM) systems—significantly augments contextaware response capabilities and sustains fine-grained, real-time access-control policies.
The proposed mechanisms are grounded in contemporary international standards and guidelines (NIST SP 800-53, ISO/IEC 27033, ISO/IEC 15408) and exhibit heightened effectiveness in operational contexts that demand not only strict regulatory compliance but also rapid adaptation to environmental fluctuations. Their deployment significantly increases the transparency of audit procedures throughout the entire security-lifecycle, optimises both direct and indirect certification expenditures, and formalises end-to-end security-assessment workflows through a rigorously riskoriented logic. Accordingly, the adaptive security-profile model constitutes a robust foundation for holistic, multilayered protection mechanisms in wireless networks, seamlessly integrating granular automation, context-driven awareness, and continuously updated, dynamic risk evaluation. Its utilisation not only equips organisations of varying scale to address an array of contemporary cybersecurity challenges, but also facilitates the practical realisation of flexible, efficient, and standardised approaches to safeguarding critical information-and-communication infrastructures.
A review of contemporary scientific literature affirms the increasing scholarly interest in
developing adaptive security profiles for wireless networks, particularly under conditions of
dynamic cyber threats. Khan et al. [
Abie and Pirbhulal [
Ahmadi’s investigation [
Within the domain of anomaly- and mixed-threat detection, Abdulkareem et al. [
Techniques drawn from discrete mathematics, graph-analytic modelling, set theory, and fuzzy logic were employed to implement the adaptive security-profile model. Behavioural analysis combined with fuzzy-membership functions underpinned the construction of the trust model. Principles of mathematical induction were applied to formally prove the correctness of the profile-update mechanisms. Machine-learning algorithms, complemented by statistical analyses of risk dynamics, facilitated robust anomaly detection. The overarching system architecture and operative workflows were visualised by means of data-flow diagrams (DFDs) and associated graph structures.
During the development of an adaptive wireless-network protection model, the evolution of the
IEEE 802.11 family of standards (802.11a/b/g/n/ac/ax/be)—which collectively define the
architectural foundations and information-security mechanisms of the wireless medium—was
thoroughly analysed. As the physical and data-link layers advanced, increasing emphasis was
placed on authentication controls, a consideration that is particularly critical given the
contemporary dynamics of cyber-threat landscapes. Early schemes (open authentication and
shared-key authentication) proved susceptible to numerous attacks; consequently, modern
implementations rely on Extensible Authentication Protocol (EAP) methods—specifically EAP-TLS,
PEAP, EAP-TTLS, EAP-FAST, and EAP-PWD [
Threats can be categorised by their level of impact into signal-level attacks—such as jamming,
radio-frequency interference, and rogue access-point spoofing—and information-level attacks,
including traffic interception, man-in-the-middle insertion, and unauthorised resource access.
Hybrid assaults that blend physical-layer and logical-layer vectors demand highly flexible
mitigation capabilities. Consequently, the construction of adaptive security profiles enables the
dynamic selection of cryptographic primitives, granular access-control policies, and authentication
modalities on the basis of continuous risk evaluation, environmental context, and observed user
behaviour.
The proposed approach enhances the overall cybersecurity posture of the system and facilitates
seamless integration with dynamic auditing, continuous-monitoring, and formal certification
instruments that align with leading international standards—namely NIST SP 800-53, ISO/IEC
27033, and ISO/IEC 15408 [
Figure 1 presents a spatial architectural model for establishing adaptive wireless-network protection, organised around a three-tier “input—processing—output” paradigm. The input tier integrates the IEEE 802.11 standards suite, user context (trust levels and behavioural attributes), and the principal threat vectors—namely, signal-level and information-level attacks. The processing tier incorporates authentication modules (EAP-TLS, PEAP), cryptographic mechanisms (AES, SAE), risk-assessment engines, and an adaptive-profile manager that synthesises policies commensurate with the prevailing threat landscape. The output tier displays the resultant artefacts: generated access-control policies, cryptographic requirements, audit logs, and compliance reports that align with NIST and ISO/IEC specifications. Dashed connectors illustrate the logical interactions among subsystems without obscuring the diagram’s elements. Collectively, the model highlights the consolidation of standards, contextual data, and risk-driven management into a unified security architecture.
The study proposes a comprehensive methodology for assembling an adaptive family of security
profiles intended to safeguard wireless networks, explicitly accounting for the taxonomy of critical
information-security components that operate within an open radio environment. The
methodological foundation rests on rigorous mathematical modelling of profile structures
consistent with the requirements of the IEEE 802.11ax and 802.11be standards [
The construction of an adaptive security profile is performed with careful consideration of network
architecture, connection dynamics, traffic composition, and specific threat vectors. The peculiarities
of an open radio environment demand rigorous oversight of authentication, encryption, access
governance, and strict adherence to security policies. Essential profile components include:
zoneisolation mechanisms (VLANs, DMZs); state-of-the-art cryptographic protocols (AES-GCM, WPA3,
SAE) [
Given the constant mobility of users and the prevalence of hybrid-access scenarios, network
nodes are treated as constituents of a remote infrastructure that remains susceptible to a spectrum
of attacks, including traffic interception, man-in-the-middle intrusions, authentication spoofing,
and forcible de-authentication. Accordingly, adaptive security profiles must enable the dynamic,
real-time adjustment of policies in direct response to the prevailing threat context. The proposed
model establishes a holistic security architecture that continuously aligns with risk fluctuations,
automates the selection of counter-measures, guarantees conformity with international standards
(ISO/IEC 27033, ISO/IEC 15408, NIST SP 800-53 [
The analytical model for constructing an adaptive family of security profiles under dynamic cyber-threat conditions relies on a graph-analytic framework that formalises the structure and interrelationships among profiles of distinct tiers. A multi-level hierarchy is prescribed, wherein each profile functions as a discrete structural entity governed by harmonised requirements for access control, cryptographic provisions, and compliance monitoring. This arrangement enables adaptive governance of protection levels in accordance with prevailing threats, user roles, network topology, and resource categories.
The hierarchical profile system is predicated on the principle of inheritance, wherein each
successive tier extends the functionality of its predecessor. The foundational—or base—profile is
devised with an enterprise-wide perspective, encompassing access points, routers, gateways, and
the associated authentication and monitoring subsystems. Its functional requirements span
authentication (EAP, TLS, the public-key infrastructure, and multifactor authentication) [
To ensure robust protection of wireless networks under conditions of dynamic cyber threats, it is essential to create flexible, adaptive security profiles grounded in a comprehensive analysis of network characteristics, device-trust levels, and the prevailing threat landscape. To illustrate the overarching architecture for constructing such an adaptive security profile, a corresponding structural-and-functional model has been devised. The accompanying diagram (Figure 2) depicts the architecture for forming an adaptive security profile for wireless networks confronted with dynamic cyber threats. The construction proceeds in sequential stages: first, the network topology and device-trust levels are examined; next, threats are assessed at both the physical and information layers, followed by risk modelling. Using these data, functional security requirement — encompassing authentication, encryption, and access control—are derived. Subsequent steps involve aggregating these requirements into a foundational functional package, establishing a hierarchy of profiles by access tier (guest, standard, critical), and enabling real-time policy adaptation. The final output is a formalised security profile that responds dynamically to shifts in the threat environment while maintaining strict conformity with contemporary informationsecurity standards.
The generalised model that defines the adaptive family of security profiles 2[
The proposed model facilitates the creation of adaptive security profiles that are calibrated to
the network’s prevailing risk posture, device typology, client-trust level, data-sensitivity category,
and applicable regulatory obligations. It thus furnishes a structured framework for the automated
generation, continual updating, and systematic auditing of profiles within wireless environments—
an essential capability given the dynamic, targeted cyber-attacks that typify contemporary wireless
infrastructures [
To systematise and facilitate comprehension of the proposed mathematical model for an
adaptive security profile, all formalised dependencies (Formulae 1–30) are grouped into four
functional layers, each aligned with its role within the overall protection structure. Layer 1 (Basic)
embraces the fundamental components that establish the system’s initial security-and-trust
framework: W0(baseline functional package), P Z (adaptive security profile), Trust (device-trust
model). Layer 2 (Risk Adaptation) incorporates dynamic mechanisms that react to changes in threat
level and interaction context—captured by Formula R (t ), Impact ( t ), ∆ P Z enabling the model to
adjust security policies in real time [
W 0= { ( ui , c j ) ∨ ( ui ∈ U 0 , c j ∈ C 0 , c j⊨ ui },
where U 0 denotes the set of baseline threats (e.g., interception, spoofing, de-authentication), C 0
represents the set of baseline counter-measures (e.g., AES-GCM, WPA3, PMF) [
Adaptive security profile for a specific class:
P Z i =W 0⋃ W d = { ( uk , c l ) ∨ ( uk ∈ U , c l ∈ C , p ( uk , c l ) ≥ δ },
(2)
where Wd= {( u , c ) }denotes the set of additional security-functional requirements generated in
response to the external operating environment; p ( uk , c l )—is the concordance function that maps
a counter-measure to a threat (i.e., its effectiveness coefficient) and δ represents the concordance
threshold—the minimally acceptable protection level. An adaptive security profile for a given class
is thus formalised as the union of baseline and additional security-functional requirements, each
selected to match the prevailing threat landscape. Equation (2) describes the set of
“threat–countermeasure” pairs for which the concordance function p ( uk , c l ) exceeds the threshold δ , thereby
defining the minimally admissible level of defensive efficacy. Consequently, the profile adapts to its
environment by incorporating only those mechanisms that provide a sufficient degree of resistance
to current threats [
Graph-structural model of the profile: (1) (3)
M = { G x =( S x , E x , w x ) ∨ x ∈ { A , C , I . D } },
where S x denotes the set of graph vertices (security agents grouped by category), E x the set of
edges (interaction channels or dependencies among agents), and wx : E x →[
t1
∑ ∫ ( α x ∙ φ x ( S x ( t ) , R ( t ) )− β x ∙ C x ( t ) ) dt ,
x ∈ { A ,C , I . D } t0
where φx (S x (t ) , R (t ))is the response function of security service x to the risk profile R (t ) ,
C x (t ) denotes the dynamic cost of maintaining that service at time t and α x , β x are weighting
coefficients that express the relative importance of the service’s effectiveness and cost. The
functional target model for security-profile evaluation quantitatively determines the effectiveness
of adaptive protection over a given time interval. Formula (4) incorporates the integral
performance assessment of each security service—authentication, confidentiality, integrity, and
access control—during the period from t0 to t1. The function φx (S x (t ) , R (t )) characterises how
service x reacts to the risk profileR (t ), whereas C x( t ) captures the variable expenditures required
to sustain that service. The coefficients α x , β x enable a weighted consideration of both efficacy and
cost for each category [
Dynamic client-trust model:
Trust ( a j , t )= 1 ∑N θ ij ∙ e−λ ∙|bi (t )−b j|,
Z j i=1
where bi (t ) denotes the behavioural profile of a device / user at time t , b j represents the
reference (benchmark) behavioural model; θij are the weighting coefficients that quantify the
influence of individual behavioural factors on the overall trust level and λ is the
anomalysensitivity coefficient. The dynamic client-trust model provides a formal mechanism for evaluating,
in real time, the reliability of a device or user on the basis of its behavioural characteristics. The
quantity Trust (a j , t ) specifies the trust level assigned to agenta j at time t і and is computed as a
weighted mean deviation of its current behaviour bi (t ) from the reference model b j. The coefficient
θij captures the contribution of each behavioural indicator to the aggregate evaluation, while the
exponential function governed by λ models sensitivity to deviations by reducing the trust value in
the presence of significant anomalies. The normalisation factor Z j rescales the resulting metric to
the interval 0 to 1 [
The spatial model of the adaptive security profile, P Z3 D is conceived as a three-tier structure that formalises the dynamic interaction among the contextual, computational, and control components of the security system. The model is defined as a tuple comprising three layers:
P Z 3 D =( LContext , L Processing , LControl ), where each tier constitutes a set of operators acting upon the profile:
LContext= { f k : I oT , Trust , Risk } ,
L Processing= { gk : EAP , AES , SIEM } ,
LControl= { hk : ACL , PolicyUpdate , Comp lianceC h eck } ,
The Context layer (LContext) comprises the set of operators f k, that ingest external-context
sources—such as IoT-device states, client-trust levels, and risk assessments. Serving as the input
tier, this layer generates the initial evaluation of the security profile in accordance with the
(4)
(5)
(6)
(7)
(8)
(9)
surrounding environment. The Processing layer (L Processing) contains the operators gk that execute
authentication mechanisms (EAP), cryptographic safeguards (AES), and event-telemetry collection
for SIEM platforms [
Any modification to the parameters within the set of functional requirements triggers an update
of the security profile, whereas its foundation—the baseline functional package—remains
immutable. This design adheres to the principle of modularity, whereby core mechanisms are fixed
and only the supplementary components evolve in accordance with the network’s current state.
The protective profile is therefore represented as the union of security-requirement sets that
encompass both baseline and dynamic elements across four key functions—authentication ( A),
confidentiality (C), integrity (I), and access control (D) is each modelled as a corresponding
subgraph of agents [
PZ =
where G xbase denotes the baseline agents of service x , while G xbase represents the dynamic
(adaptive) agents that are activated when the threat level rises. This approach preserves the
stability of the protective environment through the immutable core components and, at the same
time, provides flexibility and adaptability by dynamically expanding the profile in response to
current threats [
By integrating temporal variations in protection parameters, the adaptive security-profile model
can be expressed as a time-dependent function P Z ( t ) that reflects the dynamic activity of each
security service and its current significance [
Given that the baseline set of security-functional requirements W0 and the additional set Wd
are generated independently, they share no intersection. Consequently, no element of one set is
duplicated in the other, and the total number of all pertinent security-profile requirements can be
expressed as the simple sum of their cardinalities [
Each adaptive security profile can be formalised as a set of functional requirements that are
mapped onto the corresponding security services [
PZ =⋃x ∈ { A ,C , I , D } ( ⋃g ∈ Gx FT R g ), where F T R g designates the set of Functional Technical Requirements associated with agent g and G x represents the set of security agents assigned to functional service x. This projection renders the adaptive profile in a structured form that explicitly reflects the roles and functional purposes of every component within the protection system. Such formalisation significantly enhances the transparency of security-management processes and facilitates seamless integration with auditing, certification, and regulatory-compliance systems.
To evaluate the protection level of a wireless network and to analyse the subsequent
effectiveness of its security profile, a multilevel system of criteria has been proposed that
encompasses the principal facets of information security. One of the key layers focuses on
cryptographic criteria, which characterise the strength and resilience of the employed encryption
algorithms [
K sec=α 1 ∙ l k +α 2 ∙ Rk +α 3 ∙ f IV,
where lk denotes the key length (e.g., 128, 192, or 256 bits); R k specifies the key-rotation
algorithm type (the presence of periodic renewal markedly enhances protection); f IV expresses the
effectiveness of initialization-vector utilisation (for example, preventing IV reuse in AES-GCM)
[
The authentication criteria in the adaptive security-profile model play a pivotal role in
establishing trust in connected devices and users [
Ascore=δ 1 ∙ M EAP+δ 2 ∙ Cert use+δ 3 ∙ MFA,
(15)
where M EAP denotes the degree of support for contemporary EAP-based authentication
methods (e.g., EAP-TLS, PEAP, EAP-TTLS) [
Assessing device trust is a critical element in forming an adaptive security profile, because it
considers the behavioural characteristics of the node and the current risk level associated with its
activity [
T T ( u )= ∫ μ beh ( u , t ) ∙ θ risk ( t ) dt ,
O
where T ( u ) denotes the integral trust score for device uuu over the interval [0,T], μ beh ( u , t ) is
the behavioural function that gauges the conformity of the device’s actions to its reference profile
at moment t, θ risk ( t ) is the time-varying risk coefficient that reflects the prevailing threat
conditions in the network environment [
The system-level risk quantifies the aggregate danger facing the wireless network at a specific
instant, accounting for both the probability of individual threats materialising and the potential
impact of each [
i=1
where R ( t ) represents the overall system-risk level at time t, pi ( t ) is the probability of
occurrence of threat i, S i denotes the magnitude of damage or impact that the system would incur
should threat imaterialise and n is the total number of identified threats. This model makes it
possible to assess risk dynamics in real time and serves as a basis for adapting security profiles to
the prevailing threat climate [
The profile-adaptation index reflects the sensitivity of the security profile to variations in the system’s risk level, indicating how rapidly and flexibly protection parameters are modified in response to changes in the threat environment. Formally, it is defined as:
Aindex= ∂∂PRZ((tt)) = ∂∂PRZ
,
(18)
where Aindex denotes the profile-adaptation index; P Z ( t ) is the adaptive security profile at
moment t, R ( t ) represents the risk level at the same moment; and ∂ P Z is the derivative of the
∂ R
security profile with respect to the risk level, capturing the system’s instantaneous response to
changing threats. The index is a critical indicator of the effectiveness of the adaptive-security
management system because it quantifies the capability of the system to reconfigure itself
promptly—for example, to tighten authentication policies, modify cryptographic parameters, or
activate additional counter-measures—when risk increases [
The integral effectiveness of the profile is a generalised metric that expresses the extent to
which the functional capabilities of the adaptive security profile are realised over the time interval
[0, T] relative to the maximum attainable level of effectiveness. Formally, this metric is defined by
the following equation [
T ∫ S real ( t ) dt E int= TO , (19) ∫ S max ( t ) dt
O
The formula enables a quantitative appraisal of how effectively the security profile performs its
functions throughout the entire operational period. E int values approaching 1 indicate a high
degree of conformity between the implemented measures and the prescribed policies and
requirements. Conversely, values that fall markedly below 1 signal that the desired security level
has not been achieved and that the profile—or the mechanisms through which it is enforced—must
be re-evaluated [
The standards-compliance assessment is a pivotal indicator in the certification and audit of
security solutions, particularly in the context of adaptive protection profiles for wireless networks.
This metric determines the extent to which the implemented security profile fulfils the
requirements of international standards such as ISO/IEC 27033 or NIST SP 800-53 [
, ∆ P Z = Ψ ∂ R ( t ) ∂ T ( u )
, ∂ t ∂ t , where S is the set of security requirements specified by the relevant standard (e.g., ISO/IEC 27033, NIST SP 800-53); P Z is the set of functional security requirements implemented in the profile; # ( P Z ∩ S )denotes the number of requirements that are both implemented in the profile and mandated by the standard; and # S is the total number of requirements contained in that standard. The resulting metric, which ranges from 0 to 1, quantifies the degree to which the security system conforms to regulatory and technical specifications. A high Conf ISOvalue signifies that the system is prepared for formal certification and that its security policies are transparent.
The reactive-profile-update function is a critical mechanism that enables the system to adapt to
changes in the threat landscape and in user behaviour in real time [
Resilience mix=( ij ),
S
pij
where Ψ denotes the decision function that governs policy updates;
is the rate of
∂ T ( u )
change of the system-level risk over time; and is the rate of change of trust assigned to a
∂ t
user or device. This function enables the system to modify the security-profile configuration
automatically in line with risk dynamics and behavioural anomalies—an especially critical
capability for safeguarding wireless networks amid highly dynamic cyber-threat conditions [
The composite-attack resilience assessment formalises the adaptive-protection system’s
capacity to withstand simultaneous, interacting, or overlapping threat vectors—for example, a
combination of a man-in-the-middle attack with forced de-authentication or radio-frequency
interference [
Formalising the profile-to-enterprise-policy alignment enables a quantitative appraisal of how
well the functional security requirements coincide with the organisation’s incumbent
informationprotection policy [
X conf = i=1
k
∑ δ i ∙ match ( FTRi , Policy i )
k
,
where S ij is the counter-measure strength, Policy i represents the corresponding security policy
defined within the enterprise ICS, match ( FTRi , Policy i ) is a Boolean or fuzzy compliance
function for aligning a functional technical requirement with the policy (e.g., 1 is the full
compliance, 0.5 is the partial compliance, 0 is the non-compliance), δ i is the weighting coefficient
that captures the importance of the i-the requirement and k is the total number of functional
requirements evaluated [
The global adaptive meta-function specifies the overall utility of the constructed adaptive security profile in a dynamic environment, explicitly balancing the effectiveness of each protective service against the costs of sustaining that service:
U ( PZ )=
( w +x ∙ E x −w -x ∙ C x ),
where x ∈ { A , C , I , D } denotes the service categories—authentication (A), confidentiality (C),
integrity (I), and access control (D); E x is the effectiveness score for service x, C x represents its
operational cost; w+x is the weighting coefficient that captures the importance of the service’s
effectiveness; and w-x is the weighting coefficient that reflects the significance of its cost. This
utility function underpins the multi-criteria optimisation of the profile: maximising protective
effectiveness while minimising expenditure makes it possible to select the most balanced
adaptiveprofile configuration for the prevailing conditions and requirements 1[
The Markov-based transition probability to a new profile formalises the mechanism for dynamically updating the adaptive security profile on the basis of changing risk levels, trust assessments, and the cost of switching between profiles. This construct ensures flexible responsiveness of the security system within a volatile cyber environment. The corresponding expression is given by: (23) (24) (25)
P ( P Z i → P Z j )= f ( ∆ R , ∆ T , cos t switch ),
The model rests on the principles of Markov processes, whereby the system’s next state depends
solely on its current state and the observed changes in its environment [
The fuzzy-logic threat-assessment model accommodates uncertainty and incomplete
information about the security landscape [
The cumulative-threat impact model for an adaptive profile describes the aggregate effect exerted by multiple threats on the protection system over a specified time interval. Such a model is essential for evaluating the accumulated risk that influences profile adaptation under conditions of the continual presence of dynamic cyber threats. The formal expression is: ‹formula to be inserted›.
Impact ( t )= ∫ ( ∑ pi ( s ) ∙ ρi ( s ))ds ,
t n
0 i=1
where Impact ( t ) is a cumulative metric that assesses the total impact of threats on the security
system over timet, pi ( s ) is probability of threat, ρi ( s ) is its impact at a given time s. The indicator
allows an adaptive system to assess when the accumulated risk exceeds thresholds and requires a
change in the current security profile or an increase in the level of protection, providing proactive
security management in the face of prolonged or repeated attack exposure. Impact ( t ) determines
the critical indicator of accumulated risk, which allows assessing the threat impact for the entire
time period [0, t] [
The Wireless Infrastructure Segment Criticality Model allows you to determine how important
a particular network segment is in terms of security, taking into account the value of assets and
associated risks [
where γk is weighting factor of segment importance k, Val ( a j ) is asset value a j, and Risk j is the risk associated with this asset.
The profile resilience equation in the risk management system assesses the ability of the security profile to counter threats, taking into account efficiency, risks and degradation factors [14, 29]:
Crit segmentk =γ k ∙ ∑ ( Val ( a j ) ∙ Risk j ),
j μ risk = ∑ E x x ∑ R x + D x x (27) (28) (29) where E x is the efficiency of the security service x, R x is the risk of threats to the service x, and D x is the degradation factor (for example, due to outdated algorithms or vulnerability exploitation).
The Zero-Day Threat Readiness Index reflects the speed of the adaptive security profile’s
response to the rapid increase in risk characteristic of unknown attacks [
This metric provides a formal gauge of the system’s capacity to adjust its security profile to unforeseen threats in real time. The accompanying formulae enable a comprehensive accounting not only of structural and contextual facets of adaptive protection, but also of behavioural dynamics, risk levels, transition logic between profiles, and the influence of pertinent standards. Collectively, they constitute a robust mathematical foundation for an intelligently managed, adaptive protection system safeguarding wireless infrastructure amid an evolving and hostile cyber-threat landscape.
To capture the logic underlying adaptive-profile formation in wireless networks, a general structural-and-functional process model was constructed using the Data-Flow Diagram (DFD) methodology. The model incorporates interactions among external data sources, the system’s principal processing modules, and the knowledge bases that guide security-profiling decisions in the face of dynamic cyber threats. This structured approach delineates every stage of adaptive protection—from the collection of primary data to profile deployment and the continual updating of policies in response to shifting risk conditions.
Figure 4 presents a comprehensive Data-Flow Diagram (DFD) that depicts the end-to-end process for forming adaptive security profiles to protect wireless networks under dynamic cyberthreat conditions. The diagram encompasses every functional tier of the system—from the acquisition of raw data through to profile deployment and the continual updating of trust metrics. The external entities comprise the Client Device, Network Administrator, SIEM System, and Threat-Intelligence Provider, which collectively establish the contextual boundary of incoming information streams.
The first stage (P1) conducts an environmental and behavioural analysis of devices, capturing telemetry, identification artefacts, and activity characteristics. These data are forwarded to the Risk-and-Trust Assessment module (P2), where the current risk level and device-specific trust score are computed using records housed in the Threat and Trust databases (DB_Threats and DB_Trust).
Subsequently, the Security-Profile Generation stage (P3) constructs a baseline set of requirements (W₀) and appends adaptive components (W_d) in accordance with the prevailing risk context. This procedure references the Standards Repository (DB_Standards) to guarantee compliance with ISO/IEC 27033, NIST SP 800-53, and related frameworks. Profiling modules P3–P4 also ingest the Impact(t) indicator, which aggregates cumulative risk effects and serves as a criterion for adaptively updating security policies in light of long-term threat exposure.
The Decision-Making module (P4) evaluates the generated profile against administrator-defined access policies and selects the most pertinent protection set. The chosen profile is delivered to the Implementation module (P5), which enforces encryption, authentication, access control, and traffic isolation in strict conformity with the profile parameters.
All activities are recorded in the security-event log and processed by the Audit module (P6); the resulting log data are written back to DB_Trust and DB_Profiles to refine behavioural baselines and enhance subsequent adaptive responses.
Collectively, the model realises a holistic, dynamically responsive protection cycle for wireless
infrastructures, embodying Zero-Trust principles, automated profile updates, rigorous adherence to
international standards, and the orchestration of a multilayered security architecture [
The adaptive security-profile model is founded on adherence to ISO/IEC 15408 (Common
Criteria for Information Technology Security Evaluation), enabling the systematic structuring of
functional requirements for the protection of wireless networks [
Cryptographic support corresponds to the FCS (Functional Cryptographic Support) class and
covers algorithm selection (FCS_COP.1.1), key-management procedures (FCS_CKM.1.1–2.1), and
integrity-verification schemes—such as GCM (Galois/Counter Mode) and CCMP (Counter Mode
with CBC-MAC Protocol) [
Trust level (LoT) is categorised into five gradations, ranging from LoT1—minimal safeguards
such as WEP-40—to LoT5, which mandates comprehensive protection incorporating EAP-TLS,
SIEM (Security Information and Event Management), IDS/IPS (Intrusion Detection/Prevention
Systems), and Zero-Day-attack detection [
Figure 5 depicts the hierarchical security-profile model, illustrating a five-tier classification by trust level. The tiers span from TL1 (baseline mechanisms, e.g., WEP-40) through TL5 (fullspectrum defence with EAP-TLS, SIEM, IDS/IPS, and Zero-Day analytics). Each successive tier inherits the attributes of its predecessor while augmenting them with enhanced control, encryption, and monitoring capabilities.
The methodology mandates a comprehensive audit of the target system’s configuration, encompassing a detailed appraisal of its deployed cryptographic and authentication mechanisms. When these mechanisms satisfy the criteria associated with a higher security tier, the corresponding trust level is elevated, enabling the security profile to self-adapt to the prevailing risk context while maintaining conformity with international standards such as ISO/IEC 27033 (network security), ISO/IEC 15408, and NIST SP 800-53 (information-system security controls). Moreover, the audit findings serve as inputs for constructing a granular risk map, optimising access-control policies, and prioritising protective measures in alignment with the current threat model. This process supports the dynamic updating of security profiles, thereby ensuring continuous compliance with evolving regulatory mandates and contemporary cybersecurity best practices.
The model and methodology developed in this work for forming adaptive security profiles for the protection of wireless networks provide a rigorous foundation for building flexible, scalable, and risk-oriented information-security systems in a dynamic cyber environment. The proposed approach constructs a formalised, multi-level hierarchy of security profiles that can be automatically updated on the basis of behavioural assessments, trust levels, network topology, traffic characteristics, and the prevailing threat landscape.
The study demonstrates that traditional static access policies are inadequate for countering contemporary, multi-vector threats—particularly in open-air wireless channels. By contrast, introducing adaptive profiles that integrate cryptographic controls, authentication mechanisms, behavioural analytics, and contextual risk assessment markedly enhances cyber-resilience.
A key emphasis is the seamless integration with international standards—IEEE 802.11ax/be, ISO/IEC 15408, ISO/IEC 27033, and NIST SP 800-53—and with Zero-Trust architecture, thereby providing a coherent framework for constructing and certifying profiles. The accompanying mathematical models formalise the logic of security-parameter adaptation, performance-evaluation mechanisms, adaptation indices, trust metrics, and transition functions between profiles, enabling precise characterisation of the security system’s behaviour across both time and space. Collectively, Models (1)–(30) establish a comprehensive mathematical scaffold for building adaptive, certifiable, and scalable security profiles that operationalise Zero Trust in wireless environments.
The proposed model is deployable within corporate Wi-Fi, IoT, and cloud-service security architectures, greatly enhancing its practical value. Specifically, the algorithms can be applied to the design of secure wireless infrastructures, the creation of IoT-oriented profiles, the protection of cloud services and mobile access, and the segmentation of Wi-Fi zones. The methodology is likewise relevant for automated security-audit platforms and expert evaluations of wirelessnetwork compliance with security standards.
In sum, the results of this study address current challenges in information security by elevating the adaptability and dynamic resilience of wireless networks and by establishing a unified methodology to safeguard the confidentiality, integrity, and availability of information resources within heterogeneous digital environments.
While preparing this work, the authors used the AI programs Grammarly Pro to correct text grammar and Strike Plagiarism to search for possible plagiarism. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the publication’s content.