Adaptive Security Research Articles

Adaptive secure malware efficient machine learning algorithm for healthcare data

AbstractMalware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices. On the other hand, IoT‐based heartbeat digital healthcare applications have been steadily increasing in popularity. These applications make a lot of data, which they send to the fog cloud to be processed further. In healthcare networks, it is critical to examine healthcare data for malicious intent. The malware is a peace code with polymorphic and metamorphic attack forms. Existing malware analysis techniques did not find malware in the content‐aware heartbeat data. The Adaptive Malware Analysis Dynamic Machine Learning (AMDML) algorithm for content‐aware heartbeat data in fog cloud computing is described in this article. Based on heartbeat data from health records, an adaptive method can train both pre‐ and post‐train malware models. AMDML is based on a rule called ‘federated learning,’ which says that malware analysis models are made at both the local fog node and the remote cloud to meet the performance workload safely. The simulation results show that AMDML outperforms machine learning malware analysis models in terms of accuracy by 60%, delay by 50%, and detection of original heartbeat data by 66% compared to existing malware analysis schemes.

An Adaptive Secure and Efficient Bio-Inspired Routing Protocol for Effective Cooperation in FANETs

An Adaptive Secure and Efficient Bio-Inspired Routing Protocol for Effective Cooperation in FANETs

Accountable and Fine-Grained Controllable Rewriting in Blockchains

Most blockchains are designed to be immutable such that an object, e.g., a block or a transaction, is persisted once it has been registered. However, blockchain immutability hinders blockchain development due to the increasing abuse of blockchain storage and legal obligations. To break immutability in a controlled way, Derler et al. (NDSS’19) proposed a redactable blockchain with fine-grained controllable rewriting by introducing the notion of policy-based chameleon hash (PCH). Given a PCH-based object associated with an access policy, a trapdoor holder whose rewriting privileges satisfy the access policy can alter the object. Although this work offers an elegant approach to blockchain rewriting, it lacks accountability. In practice, the trapdoor holders may abuse their rewriting privileges, and even use their chameleon trapdoor to build a device in a blackbox manner to gain illegal profits while avoiding being caught. In this paper, we introduce a new design of PCH with blackbox accountability (PCHA). Blackbox accountability offers not only linkability between any modified object and its modifier, but also traceability that enables a central authority to identify responsible trapdoor holders whose secret keys have contributed to the blackbox device. Besides modeling PCHAs, we present a generic construction of PCHAs with rigorous security proofs. We instantiate a concrete construction of PCHA by introducing a practical attribute-based traitor tracing (ABTT) with adaptive security on prime-order pairing groups. The experimental analysis demonstrates that our PCHA and ABTT schemes have modest overheads and superior functionality to the state-of-the-art solutions. In particular, the price of accountability in key generation, hash, and adaption is almost negligible compared to the state-of-the-art solution.

Security analysis of public key encryption with filtered equality test

<p style='text-indent:20px;'>Public key encryption with equality test can provide a very simple add-on in which any one can directly perform testing over a pair of ciphertexts to check whether the underlying messages are identical or not without decryption. To restrict the such test power for different scenarios, that of delegated equality test is introduced to allow only the authenticated party to perform the test. In this paper, we focus on the security of public key encryption with filtered equality test (PKE-FET). The delegation to the party is only of a message set for designated testers in PKE-FET, which implies it cannot test any underlying message out of the set. We aim for investigating distinct security notions (static and adaptive security) with specific properties of the potential adversaries. Finally, we show the relationship between the security and complexity, and show the scheme of Huang et al. can reach adaptive security.</p>

Event-Triggered Adaptive Fixed-Time Secure Control for Nonlinear Cyber-Physical System With False Data-Injection Attacks

This brief investigates the event-triggered adaptive fixed-time secure control problem for nonlinear cyber-physical systems (CPSs) under false data-injection attacks. By employing adding a power integrator technique, an adaptive controller along with the triggering mechanism is designed by using the compromised state variables. To compensate the effect of the unknown attacks, a dynamic switching function is introduced into the controller and a corresponding switching mechanism is developed based on the fixed-time stability theorem. Under the adaptive secure control scheme, the dynamic controller parameter can be regulated online to compensate the cyber attacks such that the fixed-time stability can be ensured for the considered nonlinear CPSs and Zeno behavior can be avoided. A simulation example is given to demonstrate the effectiveness of the proposed control strategy.

Efficient Privacy-Preserving Spatial Range Query Over Outsourced Encrypted Data

With the rapid development of Location-Based Services (LBS), a large number of LBS providers outsource spatial data to cloud servers to reduce their high computational and storage burdens, but meanwhile incur some security issues such as location privacy leakage. Thus, extensive privacy-preserving LBS schemes have been proposed. However, the existing solutions using Bloom filter do not take into account the redundant bits that do not map information in Bloom filter, resulting in high computational overheads, and reveal the inclusion relationship in Bloom filter. To solve these issues, we propose an efficient Privacy-preserving Spatial Range Query (PSRQ) scheme by skillfully combining Geohash algorithm with Circular Shift and Coalesce Bloom Filter (CSC-BF) framework and Symmetric-key Hidden Vector Encryption (SHVE), which not only greatly reduces the computational cost of generating token but also speeds up the query efficiency on large-scale datasets. In addition, we design a Confused Bloom Filter (CBF) to confuse the inclusion relationship by confusing the values of 0 and 1 in the Bloom filter. Base on this, we further propose a more secure and practical enhanced scheme PSRQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> by using CBF and Geohash algorithm, which can support more query ranges and achieve adaptive security. Finally, formal security analysis proves that our schemes are secure against Indistinguishability under Chosen-Plaintext Attacks (IND-CPA) and PSRQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> achieves adaptive IND-CPA, and extensive experimental tests demonstrate that our schemes using million-level dataset improve the query efficiency by 100x compared with previous state-of-the-art solutions.

Adapting to Evolving Cyber Threat Landscapes with Dynamic Security Protocol Management in Large-Scale IoT Sensor Networks

The Adaptive Security Protocol Framework (ASPF) is introduced as a sophisticated algorithm designed for dynamic security protocol adaptation in large-scale IoT sensor networks. Comprising five integral algorithms, namely ASPF, MLTD, DKMS, BAP, and CTIS, the framework ensures a comprehensive and adaptive defense mechanism against evolving cyber threats. ASPF initiates with data collection, preprocessing, and feature extraction, employing supervised learning for model training. Anomaly detection triggers alerts and responses, guiding continuous learning and security protocol adaptation. MLTD enhances real-time threat detection through dynamic model training and threat intelligence integration. DKMS focuses on secure key management for data transmissions, calculating device thresholds and ensuring adaptive key exchanges. BAP leverages historical data for behavioral profiling, enabling real-time anomaly detection and adaptive profile updates. CTIS assesses and aggregates threat levels, fostering continuous collaboration and collective defense. The ablation study emphasizes the indispensable role of each algorithm, showcasing their synergistic contributions to the overall system's adaptability and robustness. Evaluation through comprehensive tables and visual representations highlights the proposed method's superiority over existing security protocols. The ablation study underscores the holistic nature of ASPF, solidifying its efficacy in addressing the dynamic challenges of cybersecurity in large-scale IoT sensor networks.

Розвиток парадигми РОП на основі теоретичних досліджень у сфері безпеки

The paper considers the introduction and development of the risk-based approach (RBA) para-digm. It describes the capabilities of RBA methods to categorize threats by their level of dan-ger, which allows the use of information technologies and the implementation of more efficient optimization measures. A gradual transition to the RBA paradigm from the point of view of IT development and as a way to the independent (impartial) audit of the main safety parameters has been traced. The possibilities and limitations of the existing methods are analyzed, and the trend toward the transition to adaptive security management is shown in the paper. The possibil-ities of reducing the costs of building models for determining the numerical values of risk due to the development of probabilistic structural and logical models by types of dangerous objects and productions are demonstrated. The significant potential of domestic developments concern-ing the development and implementation of a probabilistic-physical approach to the assessment of durability and reliability using probabilistic models based on DM- and DN-distribution func-tions in conditions of insufficient information on failures is described in the article. The work demonstrates the effectiveness of the transition to the modern methods of safety analysis based on the RBA paradigm in Ukraine's nuclear energy sector since the early 2000s. In this sector, thanks to the updating of development processes, safety planning measures, and government control, the damages resulting from emergency shutdowns have decreased by more than 10 times. At the same time, it has been noted that our country is lagging behind in implementing the RBA paradigm in other areas of production and life. To accelerate the implementation of RBA in a wide range of industries and spheres of life, the priority problems and tasks related to the development of IT, which require solutions in our country and all over the world, have been formulated.

Adaptive Secure Practical Fault-tolerant Output Regulation of Multiagent Systems With DoS Attacks by Asynchronous Communications

The secure practical fault-tolerant output regulation of multiagent systems (MASs) with denial-of-service (DoS) attacks is considered in this paper. Different from most existing output regulation problems: 1) The leader matrix is only known to a part of followers; 2) The communication networks are unreliable and there exist DoS attacks; 3) Actuator faults exist in the MASs. Firstly, to overcome the impact of DoS attacks, the adaptive edge-event-triggered observer is designed to estimate the leader's matrix and state. The novel edge-event-triggered mechanism enables asynchronous intermittent communication between agents, and the adaptive learning rate makes the observer design independent of the global information for the communication topology. Secondly, in order to compensate for the impact of actuator faults on system stability, a novel resilient adaptive event-triggered fault-tolerant controller is designed, and it has zero-order holder (ZOH) characteristics and avoids chattering phenomenon. Finally, two simulation examples well show the effectiveness of our developed methods.

Adaptive Secure Transmission Based on Collaborative Communication in Smart Grids

In this paper, a collaborative power line communication (PLC) and generalized spatial modulation architecture (CPGA) is proposed to resolve the adaptive secure transmission problem in smart grid. In the proposed CPGA, the adaptive transmission is driven by the level of data in smart grid, which is achieved through a proposed antenna selection scheme based on interval quantification. And the anti-eavesdropping ability is improved through PLC-assisted transmission and the artificially designed jammer. Additionally, a better management ability of energy efficiency is guaranteed based on the data-driven adaptive transmission scheme. The closed-form expressions of the security capacity, the management ability of energy efficiency and average bit error rate (ABER) are derived for the performance of adaptive secure transmission. Finally, we analyze the security capacity, management capability of energy efficiency and ABER of the CPGA. Numerical results show that better ABER performance can be achieved in the CPAG compared with the traditional GenSM. Moreover, for the traditional multi-input multi-output, the better security capacity and management of energy efficiency can be achieved in the CPGA. And the adaptive balance between secure level and energy management are realized through the CPGA in smart grid.

A Trusted and Adaptive Security Mechanism for Wearable E-Healthcare Systems

A Trusted and Adaptive Security Mechanism for Wearable E-Healthcare Systems

Fine-grained flexible access control: ciphertext policy attribute based encryption for arithmetic circuits

Applying access structure to encrypted sensitive data is one of the challenges in communication networks and cloud computing. Various methods have been proposed to achieve this goal, one of which is attribute-based encryption (ABE). In ABE schemes, the access structure, a.k.a policy, can be applied to the key or ciphertext. Thus, if the policy is applied to the key, the ABE scheme is called the key policy attribute-based encryption (KP-ABE), and if it is applied to the ciphertext, the scheme is called the ciphertext policy attribute-based encryption (CP-ABE). Since in the KP-ABE, the policy is selected once by a trusted entity and is fixed then, they are not suitable for applications where the policy needs to change repeatedly. This problem is solved in CP-ABE, where the policy is selected by the sender and can be changed for each message encryption. Furthermore, it is desired in the ABE schemes that a strong fine-grained access control can be realized. While most of the existing access structures are of Boolean type, an arithmetic access structure can support a stronger fine-grained access structure. We present the first CP-ABE scheme with an arithmetic circuit access policy based on the multilinear maps. First, we outline a basic design and then two improved versions of this scheme, with or without the property of hidden attributes, are introduced. We also define the concept of hidden result attribute based encryption (HR-ABE) which means that the result of the arithmetic function will not be revealed to the users. We define a new hardness assumption, called the \((k-1)\)-distance decisional Diffie–Hellman assumption, which is at least as hard as the k-multilinear decisional Diffie–Hellman assumption. Under this assumption, we prove the adaptive security of the proposed scheme.

Equilibrium Allocation Approaches of Quantum Key Resources With Security Levels in QKD-Enabled Optical Data Center Networks

In this article, the network performance of equilibrium allocation of quantum key resources was investigated in quantum key distribution (QKD)-enabled optical data center networks. To effectively use quantum key resources, we propose three novel efficient load balancing routing, wavelength, and time-slot assignment (LB-RWTA) approaches, including LB-RWTA with flexible security level (LB-RWTA-FSL), LB-RWTA with specific security level (LB-RWTA-SSL), and LB-RWTA without security level (LB-RWTA-NSL). Particularly, the proposed LB-RWTA-FSL approach is uniquely featured by adaptive security level classification (ASLC) and load balancing (LB), aiming to demarcate the security level (SL) and reduce the overall network congestion. We introduce an existing routing, wavelength, and time-slot assignment (E-RWTA) approach without SL, called E-RWTA, for comparison. Simulation results show the effectiveness of our proposed approaches in terms of much higher quantum key resource efficiency and thus much higher network security performance than the state-of-the-art quantum key resource allocation approaches compared with the E-RWTA approach in QKD-enabled optical data center networks.

Adaptive secure transmission for two‐way energy harvesting relay networks with two‐sided eavesdropping

AbstractIn this paper, for energy harvesting (EH) two‐way relay (TWR) networks with two‐sided eavesdroppers, the problem of secure information transmission is investigated. Based on this system model, a novel full‐duplex (FD) relaying jamming scheme is proposed to achieve simultaneously information reception, EH and jamming signal transmission at the cooperative relay. Particularly, the entire transmission phase and received signal at relay node are divided into two different time durations and three different parts by time allocation (TA) and power splitting (PS), respectively. For the aim of maximizing the sum secrecy rate, adaptive TA factor and PS factor are jointly optimized. We derive the non‐convex optimization function. To solve this highly non‐convex optimization problem, a heuristic approach based on particle swarm optimization (PSO) is proposed. Numerical results verify the superior sum secrecy rate of the proposed scheme. Besides, the impact of system parameters on the output performance is analyzed.

An Adaptive QoS and Trust-Based Lightweight Secure Routing Algorithm for WSNs

The limited resources and low computational power of wireless sensor networks (WSNs) make them vulnerable to various security attacks. Conventional security mechanisms require too many resources to allow the reliable operation of WSNs due to their resource-constrained nature. In addition, multihop communication in WSNs creates a requirement for guaranteed Quality of Service (QoS). Therefore, providing security while maintaining QoS and energy efficiency in WSNs are important design considerations. To further increase the performance of WSNs, there is a need to overcome the energy-hole problem, which leads to poor coverage of the field of interest. An energy-hole problem is created because of using poor deployment strategies. In this article, we define a multiobjective WSN optimization problem and present a novel algorithm known as lightweight secure routing (LSR) to manage WSNs that directly addresses the multiobjective WSN optimization problem. Our LSR algorithm uses ant colony optimization (ACO), an adaptive security model based on direct and indirect trust calculations, an adaptive QoS model, a hybrid deployment model based on 2-D Gaussian and uniform distributions, and an adaptive connectivity model that uses an appropriate communicational radius to ensure high connectivity between sensor nodes to solve the multiobjective WSN optimization problem. We divide our simulation results into three analyses, namely, trust model analysis, network scalability analysis, and security risk analysis to show that LSR outperforms the existing techniques in terms of energy consumed to calculate trust values, trust values convergence, network lifetime, average routing delay, and packet delivery ratio.

A Hierarchical Searchable Encryption Scheme Using Blockchain-Based Indexing

Focusing on the fine-grained access control challenge of multi-user searchable encryption, we propose a hierarchical searchable encryption scheme using blockchain-based indexing (HSE-BI). First, we propose a hierarchical search index structure based on a DAG-type access policy and a stepwise hierarchical key derivation mechanism; which we outsourced to the blockchain network to achieve reliable hierarchical search. We design a dynamic append-only update protocol for the blockchain-based index to deal with adding and deleting files. Secondly, we propose a hierarchical authorization mechanism based on broadcast encryption to achieve fine-grained search permission granting and revoking, which can prevent a malicious server from colluding with corrupted users. The security and complexity analysis shows that HSE-BI achieves optimal search time while satisfying adaptive secure and revocation secure. Our experimental results are encouraging, e.g., compared with the traditional multi-user searchable encryption schemes, HSE-BI’s hierarchical search policy does not impact the search performance visually. The growth rate of the search latency decreases with the increasing number of hierarchical users, which can act as an efficient crypto tool to open up venues for other applications. We demonstrate that HSE-BI is more suitable for actual applications with fine-grained access requirements and can act as an efficient crypto tool to open up venues for other applications.

Dynamic event‐triggered model‐free adaptive security tracking control for constrained subway train system

AbstractIn this article, the dynamic event‐triggered model‐free adaptive security tracking control problem of the subway system with speed and traction/braking force constraints under aperiodic denial‐of‐service (DoS) attacks is studied. The complex subway train system is transformed into a linearization data model by using the dynamic linearization technique. For the subway system, both speed and traction/braking forces need to be restrained to ensure the safety of the subway equipment. In addition, the dynamic event‐triggered mechanism is designed to reduce communication frequency and prolong the hardware life cycle. At the same time, considering aperiodic DoS attacks in the network channel, the security tracking control problem is guaranteed. In the simulation, the algorithm is applied to the subway train system, and the effectiveness of this algorithm is verified with a comparison.

Administrative principles of adaptive security policy of public–private support of innovative business in the conditions of globalization

Administrative principles of adaptive security policy of public–private support of innovative business in the conditions of globalization

Adaptive security control of time-varying constraints nonlinear cyber-physical systems with false data injection attacks

In this article, an adaptive security control scheme is presented for cyber-physical systems (CPSs) suffering from false data injection (FDI) attacks and time-varying state constraints. Firstly, an adaptive bound estimation mechanism is introduced in the backstepping control design to mitigate the effect of FDI attacks. Secondly, to solve the unknown sign time-varying state-feedback gains aroused by the FDI attacks, a type of Nussbaum function is employed in the adaptive security control. Then, by constructing a barrier Lyapunov function, it can be ensured that all signals of controlled system are bounded and the time-varying state constraints are not transgressed. Finally, the provided simulation examples demonstrate the effectiveness of the proposed controller.

Application of the Learning Automaton Model for Ensuring Cyber Resiliency

This work addresses the functional approach to ensuring cyber resiliency as a kind of adaptive security management. For this purpose, we propose a learning automaton model capable of self-learning and adapting to changes while interacting with the external environment. Each node in the under-controlled system has a set of probable actions with respect to neighboring nodes. The same actions are represented in the graph of the learning automaton, but the probabilities of actions in the graph model are permanently updated based on the received reinforcement signals. Due to the adaptive reconfiguration of the nodes, the system is able to counteract the cyberattacks, preserving resiliency. The experimental study results for the emulated wireless sensor network (WSN) are presented and discussed. The packets loss rate stays below 20% when the number of malicious nodes is 20% of the total number of nodes, while the common system loses more than 70% of packets. The network uptime with the proposed solution is 30% longer; the legitimate nodes detect malicious nodes and rebuild their interaction with them, thereby saving their energy. The proposed mechanism allows ensuring the security and functional sustainability of the protected system regardless of its complexity and mission.