Optimal Security Research Articles

ECCPWS: An ECC-based protocol for WBAN systems

The development of Wireless Body Area Network (WBAN) and Wearable Health Monitoring Systems (WHMS) play a key role in healthcare monitoring. WBAN includes medical sensors that monitor vital signs of patients, collect data and usually transmit them to medical servers via wireless channels. Therefore, the patient’s sensitive information sent over the channel can be vulnerable to various attacks. Hence, designing lightweight authentication security protocols for these systems with the lowest computational and communication costs has become a major challenge. Recently, Sowjanya et al. (2020) presented a lightweight authentication scheme for WHMS based on Elliptic Curve Cryptography (ECC), which provides optimal security features and low storage, communication and computational costs. In this paper, the security of their scheme is evaluated and passive insider secret disclosure and replay attacks against their scheme are presented. It is obvious that other attacks such as desynchronization attack and impersonation attack can be applied to this protocol by obtaining the secret value of network manager. The complexity of our proposed attacks is just one run of the protocol and their success probability equals to one. Finally, by remedying the Sowjanya et al. (2020) ’s protocol, a lightweight ECC-based authentication scheme called ECCPWS is proposed. Moreover, the proposed protocol’s security proof is performed via informal methods and also formally through Real-Or-Random (ROR) model, BAN logic, Scyther and AVISPA tools. The security verification results of ECCPWS show that the ECCPWS has complete security against various security vulnerabilities and attacks.

Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

In recent scenario of Wireless Sensor Networks (WSNs), there are many application developed for handling sensitive and private data such as military information, surveillance data, tracking, etc. Hence, the sensor nodes of WSNs are distributed in an intimidating region, which is non-rigid to attacks. The recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and threats. In traditional models, the solution has been made for defending the networks, only to specific attacks. However, in real-time applications, the kind of attack that is launched by the adversary is not known. Additionally, on developing a security mechanism for WSN, the resource constraints of sensor nodes are also to be considered. With that note, this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism (IDRM) for defending the sensor network from various attacks. Moreover, for efficient model design, the work includes the part of feature evaluation of some general attacks of WSNs. The IDRM also includes determination of optimal secure paths and Node security for secure routing operations. The performance of the proposed model is evaluated with respect to several factors; it is found that the model has achieved better security levels and is efficient than other existing models in WSN communications. It is proven that the proposed IDRM produces 74% of PDR in average and a minimized packet drop of 38% when comparing with the existing works.

Mathematical Model of Protection of Critical Infrastructure Object from Trojan Programs

The article proposes a conceptual mathematical model for protecting critical infrastructure objects from Trojan programs. The model is based on a game-theoretic approach where the defender and the attacker have conflicting goals. When applying the model, a vulnerability analysis of critical infrastructure objects is performed to identify potential weak points that can be exploited by Trojan programs. Then a game model is formulated to capture the strategic interaction between the defender and the attacker. An optimal security investment for the defender is introduced, which balances the cost of security against the expected loss from a successful attack. Also, the effectiveness of the proposed protection strategies is evaluated using testing and evaluation methods. The research results show that the model is effective in protecting critical infrastructure objects from Trojans and can help allocate security resources to maximize protection while minimizing costs.

Privacy Preservation Modelling for Securing Image Data using Novel Ethereum-based Ecosystem

The broad usage of images in real-time applications demands a cloud infrastructure due to its advantages. Many use cases are built where the image data is shared, sharing becomes the core function, and the medical domain takes its broad advantage. The cloud is a centralized infrastructure for its all-operation usages; it depends mainly on the trusted third party to handle security concerns. Therefore, the privacy preservation of the image data or any data becomes an issue of concern. The distrusted system advantages are achieved using blockchain technology for image data security and privacy concerns. The traditional approaches of the security and privacy models raise many apprehensions as these are designed on the centralized systems of the data sharing mechanisms. It is also observed that large data files are not wisely handled, which demands building a framework model that takes image data and any other data of any size to ensure a dependable optimal security system. This paper presents a framework model to achieve optimal time complexity for securing the privacy aspects of the image data or any other data that uses space optimal file system using distributed security mechanism for both the storage and sharing of the data. The proposed framework model for optimal time complexity and security uses a duplication algorithm using stakeholder agreement to ensure efficient access control to the resources using the cryptographic approach to the Ethereum ecosystem. The performance metric used in the model evaluation includes the degree of availability and efficiency. On benchmarks, it performs well compared to the traditional cloud-built distributed systems. The quantified outcome of the proposed scheme exhibits a 42.5% of reduction in time for data repositioning, a 41.1% of reduction in time for data retrieval, a 34.8% of reduction in operational cost, a 73.9% of reduction in delay, and a 61% faster algorithm execution time in contrast to conventional blockchain method.

An Optimal Security Framework Based on Driver Authentication for Intelligent Transportation Systems

Abstract Security and safety have been a critical issue in the field of Intelligent Transportation Systems. Security of both people and vehicles is a matter of concern. In this paper, an authentication-based security framework is designed to serve the purpose. Only authorized users are provided access to the vehicle. The drivers are first registered for driving the vehicle from an authorized and valid source. The next step is the authentication check before one can be given access to the vehicle. There are two alternative methods provided for the authentication check in the solution depending upon the situation the vehicle is in. The solution is an optimally viable solution from the security perspective of the real-time Intelligent Transportation System environment. The results have shown that the security solution fits into the domain very well and performs as per expectations. The solution has been tested on a Vehicle Tracking Device connected to a vehicle. The same solution can be extended to a fleet of vehicles.

Secure and Optimal LOADng Routing for IoT with Composite Routing Metric

Security is the one of the major challenges for routing the data between the source and destination in an Internet of Things (IoT) network. To overcome this challenge, a secure Lightweight On-demand Ad hoc Distance-vector—Next Generation (LOADng) Routing Protocol is proposed in this paper. As the LOADng protocol is the second version of Ad Hoc On-Demand Distance Vector (AODV) protocol, it retains most of the basic functionality and characteristics of AODV. During the route discovery process, the cyclic shift transposition algorithm (CSTA) is used to encrypt the control packets of the LOADng protocol to improve its security. CSTA approach only derives transposition and substitution without product cipher with respect to input data. Besides this, for choosing the best probable path between the source and destination, routing metrics such as link quality Indicator (LQI), hop count (HC) and queue length (QL) are included in the control packets. The data is then securely sent using CSTA using the optimal secure path selected. Experimental Results depict that the proposed secure and optimal LOADng (SO-LOADng) using CSTA encryption obtains better throughput, delivery ratio encryption time and decryption time than the existing state-of-art approaches.

Optimal Security Design for Risk-Averse Investors

Optimal Security Design for Risk-Averse Investors

An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization

Routing strategies and security issues are the greatest challenges in Wireless Sensor Network (WSN). Cluster-based routing Low Energy adaptive Clustering Hierarchy (LEACH) decreases power consumption and increases network lifetime considerably. Securing WSN is a challenging issue faced by researchers. Trust systems are very helpful in detecting interfering nodes in WSN. Researchers have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization problem. The metaheuristic Elephant Herding Optimizations (EHO) algorithm is formulated based on elephant herding in their clans. EHO considers two herding behaviors to solve and enhance optimization problem. Based on Elephant Herd Optimization, a trust-based security method is built in this work. The proposed routing selects routes to destination based on the trust values, thus, finding optimal secure routes for transmitting data. Experimental results have demonstrated the effectiveness of the proposed EHO based routing. The Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%, by 1.45%, and by 31.94% than LEACH, Elephant Herd Optimization, and Trust LEACH, respectively at Number of Nodes 3000. As the proposed routing is efficient in selecting secure routes, the average packet loss rate is significantly reduced, improving the network’s performance. It is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization.

A Cross-Layer Design Approach to Strategic Cyber Defense and Robust Switching Control of Cyber-Physical Wind Energy Systems

Due to the increasing adoption of smart sensing and Internet of things (IoT) devices, wind energy system (WES) becomes more vulnerable to cyber and physical attacks. Therefore, designing a secure and resilient WES is critical. This paper first proposes a system-of-systems (SoS) framework for the cyber-physical WES. Specifically, on the one hand, we adopt a game-theoretic model to capture the interactions between the WES system defender and the adversary at the cyber layer. The outcome of this cyber defense game is reflected by control-aware Nash equilibria. On the other hand, we devise a cyber-aware robust and resilient switching controller based on a Markov jump linear system model for the physical WES. The performances of the WES cyber and physical layers are interdependent due to their natural couplings. We further investigate the SoS equilibrium of the integrated WES, which considers the system security, robustness, and resilience holistically. Finally, we use case studies to corroborate the developed cross-layer design principles for the cyber-physical WES. Note to Practitioners—Cybersecurity becomes a critical concern of wind energy system (WES) operators as an increasing amount of IoT devices are adopted for WES’s communication, monitoring, and operation support purposes. This cyber-physical integration in WES creates a much broader attack surface because adversaries can compromise the physical WES by attacking its dependent cyberspace. To mitigate the impact of attacks, the operator should not only design intelligent control strategies for WES but also strategically secure the WES’s cyber layer. These two goals are naturally coupled together. On the one hand, the WES operates under different compromised conditions depending on the attack actions at the cyber layer. Thus, the control design needs to be adversary-aware by taking the real-time cyber state into account. On the other hand, the adversary’s cyberattack strategy is influenced by the induced performance degradation of WES. Hence, the corresponding attack measures and countermeasures, in turn, should be physically control-aware. This paper establishes a holistic mathematical framework to simultaneously address these two challenging objectives. The obtained solution provides guidelines for the WES operator on the optimal security resource investment in defending against cyberattacks and the robust switching control design to mitigate the impacts of attacks further. This methodology creates a defense-in-depth paradigm for the WES operators to maintain the energy system efficiency in the adversarial environment. This cross-layer design approach is also efficient and user-friendly for online implementation with the developed iterative algorithm. The simulated-based case studies in this paper show the effectiveness of the proposed approach. However, a more thorough validation of the method in practice is necessary before its integration with the production standard.

Security performance analysis of active intelligent reflective surface assisted wireless communication

As a new communication technology, Intelligent Reflecting Surface (IRS) can intelligently reconfigure the wireless propagation environment by integrating many passive/active reflective elements on the plane. According to the characteristics that IRS can adjust the propagation channel intelligently, this paper applies IRS to wireless security communication, and studies how to make the security rate reach the optimal security capacity from the perspective of optimization technology. In this paper, two schemes of passive/active IRS are considered, and the corresponding safety rate maximization algorithm is proposed. In view of the nonconvexity of the objective function, on the one hand, in the passive IRS scheme, the Dinkelbach method is used to transform the objective function into a form that is easy to handle, and the original problem is transformed into a convex problem through the continuous convex approximation method; On the other hand, under the active IRS scheme, aiming at the complexity of the original problem, the first order Taylor expansion is used to obtain the lower bound of the optimization problem, and a minimax optimization algorithm is proposed. Finally, the performance of the proposed algorithm is verified by simulation. The simulation results show that the algorithm designed with active IRS has better security rate than the algorithm designed with passive IRS under the same parameter settings.

Statistical Analysis of Remote Health Monitoring Based IoT Security Models & Deployments From a Pragmatic Perspective

Remote health monitoring-based Internet of Things (IoT) network security is a multi-domain task, that involves identification of network attack, evaluation of mitigation strategies, design of performance aware data security models, integration of privacy models, and modeling of device-level security methods. Internal designs for each of these models is highly complex, and varies in terms of quantitative & qualitative performance measures. This is due to their variation in terms of design nuances, functional advantages, context-based limitations, and possible deployment-specific future scopes. Due to this variation, it is highly ambiguous to select these models for performance-specific IoT deployments. Moreover, these models also vary in terms of security level, Quality of Service (QoS) parameters, scalability performance, computational complexity, deployment costs, and other performance metrics. Thus, to identify optimum models, researchers & network designers are required to test & validate multiple security models for their deployments. Due to which, the cost & time to market for IoT devices is increased, thereby affecting viability of IoT products. To overcome these selection issues, an empirical survey of different IoT security models including block-chains, encryption techniques, hashing models, privacy preservation techniques, machine learning based security methods, etc. are discussed in this text. This text also discusses various attack mitigation models that provide node-level security, network-level security, physical security, & route-level security. This discussion will assist in initially evaluating different operating characteristics of these models, which will allow readers to identify most suited models for their application-specific use cases. This article also assesses the models’ performance in terms of computational latency, energy consumption, security levels, deployment complexity, and scalability measures. These metrics are compared between different security models, which will further assist readers to identify optimum models for their performance-specific use cases. To further assist in model selection, this text proposes evaluation of a novel IoT Security Performance Rank (ISRP), that combines various performance metrics to form a singular rank which can be used to describe overall performance of these models. Readers will be able to consider optimal security approaches for new and current IoT installations based on this ranking.

Security-Aware Collaboration Plan Recommendation for Dynamic Multiple Workflow Processes

Currently, recommending collaboration plans of dynamic multiple workflow processes under security requirements or constraints has become a hot topic, as it not only can reduce the risk during execution, but also can avoid enormous financial loss caused by security breaches. However, the existing methods cannot solve the parametric problem caused by dynamic relations of multiple workflow processes or recommend an optimal collaboration plan because of erroneous information. In this article, we propose a new approach to recommend security-aware collaboration plans for multiple workflow processes with dynamic relations. First, we develop three basic computing patterns of dynamic relations and construct a parametric sprouting graph for multiple workflow processes with dynamic relations. Second, we present the procedure of recommending an optimal secure collaboration plan that satisfy security requirements. By comparison with the existing methods via two experiments, our approach not only can solve the parametric problem caused by dynamic relations of multiple workflow processes, but also can recommend an optimal secure collaboration plan under security requirements.

Enhanced Elman spike neural network for cluster head based energy aware routing in WSN

AbstractAn enhanced Elman spike neural network (EESNN) optimized with hybrid wild horse optimization and chameleon swarm algorithm is proposed in this manuscript for multi‐objective cluster head selection and energy aware routing in wireless sensor network (CH‐EESNN‐Hyb‐WH‐CSOA‐WSN). Initially, EESNN is used to intelligent selection of cluster head. Then, the optimal cluster head utilized the selected data transferring process with the consideration of multi‐objective fitness function based EESNN. Here, some of the multi‐objective fitness function factors are considered, like energy, delay, throughput, distance between the nodes, traffic rate and cluster density. The hybrid wild horse optimization and chameleon swarm algorithm (Hyb‐WH‐CSOA) is taken into account for the optimal route path selection with minimal delay. The proposed CH‐EESNN‐Hyb‐WH‐CSOA‐WSN method is activated in network simulator 3 (NS‐3) tool. The performance of the proposed method is examined under certain performance metrics, like count of alive nodes, drop, network lifetime, delay, throughput, energy consumption, and packet delivery ratio. Finally, the proposed method attains 98.78%, 97.21%, 99.61% lower delay, 98.78%, 99.21%, 96.78% higher delivery ratio, and 99.57%, 98.67%, 98.88% lower packet drop compared with the existing methods, like optimal secure cluster head placement through source coding techniques in wireless sensor networks (CHP‐HMC‐WSN), optimal placement of single cluster head in wireless sensor networks via clustering (CHP‐K‐Means C‐PSO‐WSN) and hybrid firefly approach along particle swarm optimization for energy efficient optimum cluster head selection in wireless sensor networks (CHP‐HFAPSO‐WSN) respectively.

PELUANG BISNIS PADA PENERAPAN INDUSTRIAL INTERNET OF THING (IIoT)

This research was conducted using qualitative methods and exploratory literature review, where after a number of journal references were collected, a theoretical study, research results and discussion of this literature review article were carried out in the Industrial Internet of Things (IIoT) concentration. This research presents the results of a study of business opportunities from the use of the Industrial Internet of Things (IIoT). IoT can be implemented in various fields of the modern economy, for example: health, environment, defence, quality control, transportation, logistics, clean water, building / construction, industrial production / manufacturing, energy sources, agriculture and plantations. By utilizing IIoT, companies can benefit from technology investments made from saving operational costs and increasing company revenue. The suggestions for this research include the need to develop the implementation of IIoT in various fields so that it is more effective, efficient and provides convenience to companies. Future research needs to analyze opportunities, challenges and directions for future research work related to IIoT regarding the benefits of using IIoT technology including real-time, coexistence, interoperability, optimal security and privacy.

A novel deterministic substitution box generator over elliptic curves for real-time applications

Recently, highly dynamic substitution box (S-box) generators are widely used in cryptosystems to improve the security against modern computational attacks. However, generating S-boxes is computationally costly, and frequently generating them slows down the encryption process and hence limits the encryption throughput. This fact necessitates developing new S-box generators that can provide optimal security with minimum computation cost. We propose a novel S-box generator based on a deterministic algorithm over elliptic curves to generate highly dynamic and secure S-boxes with low computation cost. Experimental results show that our generator is i times, i∈[6.720,1.3×105] faster than the fastest available S-box generator to generate S-boxes of an input size 2m,m=4,6,8,10,12,14,16 over large ECs. This fact allows us to attain significantly higher throughput as compared to the state-of-the-art schemes. Furthermore, our generator can generate highly dynamic, key-sensitive, and cryptographically secure S-boxes. As a sample application, we used our generator in an image encryption scheme. Experimental results reveal that our encryption scheme has high resistance against differential and statistical attacks, and key attacks. Furthermore, with the new S-box generator, the running time of the modified scheme reduced by two orders of magnitude.

Optimal Security Protection Strategy Selection Model Based on Q-Learning Particle Swarm Optimization.

With the rapid development of Industrial Internet of Things technology, the industrial control system (ICS) faces more and more security threats, which may lead to serious risks and extensive damage. Naturally, it is particularly important to construct efficient, robust, and low-cost protection strategies for ICS. However, how to construct an objective function of optimal security protection strategy considering both the security risk and protection cost, and to find the optimal solution, are all significant challenges. In this paper, we propose an optimal security protection strategy selection model and develop an optimization framework based on Q-Learning particle swarm optimization (QLPSO). The model performs security risk assessment of ICS by introducing the protection strategy into the Bayesian attack graph. The QLPSO adopts the Q-Learning to improve the local optimum, insufficient diversity, and low precision of the PSO algorithm. Simulations are performed on a water distribution ICS, and the results verify the validity and feasibility of our proposed model and the QLPSO algorithm.

Optimal secure fuzzy based multi keyword search over ciphered e-mail in cloud

Cloud computing become increasingly more famous consistently, as numerous associations tend to outsource their information. With the outbreak of email message leakage, the protection and security of sensitive email data have become clients’ essential concerns. Encrypted email data is a superior method to ensure security, yet it will enormously restrict the searching. To take care of this difficulty keyword-based search over encrypted information is presented. The current search strategies permit the client to search utilizing just the specific keywords. There is no capacity to bear errors and format irregularities. In order to overcome those drawbacks, optimal secured fuzzy-based multi-keyword search over encrypted email data is proposed here. The email sender encrypts the email data before outsourcing the data to a cloud server. For encryption, the proposed method utilizes the optimal secure XOR (OSXOR) encryption algorithm. Here the key value is optimally selected by the mayfly optimization algorithm (MOA). After the encryption, the encrypted email is outsourced to the cloud server. The data owner creates an encrypted searchable index using an input file to enable querying across encrypted emails and then assigns either the index or the gathering of encrypted messages to a cloud server. The receiver receives them from the cloud server and is fed back information, but it is unable to comprehend the signal. The recipient of the encrypted email can decode it and create a search trapdoor in the encrypted email database. For searching, fuzzy-based multi-keyword search is proposed. The effectiveness of the proposed methodology is analyzed in terms of different metrics namely, Memory Usage, Execution time, Encryption and decryption time and search time. The experimental result shows that the proposed method takes a minimum search time is 0.51 s and it achieves maximum searching accuracy of 98%. The implementation is done in JAVA with a Cloud simulator.

Just tweak! Asymptotically optimal security for the cascaded LRW1 tweakable blockcipher

Just tweak! Asymptotically optimal security for the cascaded LRW1 tweakable blockcipher

Optimal Defense Strategy Selection Algorithm Based on Reinforcement Learning and Opposition-Based Learning

Industrial control systems (ICS) are facing increasing cybersecurity issues, leading to enormous threats and risks to numerous industrial infrastructures. In order to resist such threats and risks, it is particularly important to scientifically construct security strategies before an attack occurs. The characteristics of evolutionary algorithms are very suitable for finding optimal strategies. However, the more common evolutionary algorithms currently used have relatively large limitations in convergence accuracy and convergence speed, such as PSO, DE, GA, etc. Therefore, this paper proposes a hybrid strategy differential evolution algorithm based on reinforcement learning and opposition-based learning to construct the optimal security strategy. It greatly improved the common problems of evolutionary algorithms. This paper first scans the vulnerabilities of the water distribution system and generates an attack graph. Then, in order to solve the balance problem of cost and benefit, a cost–benefit-based objective function is constructed. Finally, the optimal security strategy set is constructed using the algorithm proposed in this paper. Through experiments, it is found that in the problem of security strategy construction, the algorithm in this paper has obvious advantages in convergence speed and convergence accuracy compared with some other intelligent strategy selection algorithms.

Еnvironmental protection and efficient methods of using drained organic soils using different methods of calculating mineral fertilizer application

Aim. To develop optimal parameters of reclamation, agricultural, economic and environmental protection solutions to ensure sustainable development of agricultural production in the humid zone on drained soils; elimination of soil degradation and improvement of their fertility based on the development of an ecologically sound system of agriculture with effective methods of calculating the application of mineral fertilizers and energy-saving technologies for growing agricultural crops in conditions of market relations. Methods. Field with a complex of biometric, laboratory-analytical, mathematical-statistical. Results. A nature protection and ecological assessment of the methods of using drained organic soils of the humid zone and the calculation of the application of mineral fertilizers for perennial grasses of different years of cultivation, sunflower, corn, oats, was carried out. The optimal security of the formation of the productivity of perennial grass stands and the economic feasibility on average for the years 2016–2020 have been established. With the warming of the climate, it is advisable to introduce highly profitable heat-loving crops (soy, sunflower, corn) into the structure of sown areas of the humid zone, which in an eight-field grass field rotation with the use of mineral fertilizers and growth stimulants (organic balance) based on the analysis of research data, provide 9.2–9.7 t/ha of dry mass, sunflower seeds 2.6 t/ha, corn grains – 8.2 t; oats 4.6 t/ha for high product quality and prevention of excessive mineralization of organic matter. Conclusions. For cultivation in grass-field crop rotation, the highest yield on average for 2016–2020. 6.8; 8.9–9.0 and 9.0 t/ha of dry weight) sunflower seeds (2.4–2.6 t/ha), corn grains (7.7–8.2 t/ha) and oat grains (4.5–4.6 t/ha) obtained by applying doses of fertilizers obtained on the basis of data analysis of long-term studies. Such doses of mineral fertilizers provided the highest yield of crops with a moderate dose of fertilizer application, moreover, the content (NPK) in the grown products did not exceed the MPC indicators.