1,721,009 research outputs found
GREPHRO: Nature-inspired optimization duo for Internet-of-Things
No full textThe optimization techniques usually work with the maximization or minimization of the problem to obtain the local loci or cumulative global loci. Two-dimensional bio-inspired optimization techniques face convexing problems towards a global solution and use an increased number of iterations. Besides, the duality principle considers the dual optimization aspects of the problems leading to a large duality gap of uncertain deviations and optimization errors between any prime solution and its dual solution. Moreover, several problems exist where one objective function requires maximization and another objective function requires minimization using the same set of parameters and some chaining of the feedback process. In such cases, we generally use two different optimization problems as per the best suit to the problem environment and obtain the different sub-solutions of the individual problems. This increases the complexity of the system and often deviates from the original optimal solution. We address these problems of dual optimization in our present work. In this paper, we introduce the first optimization duo model for computing services. To be specific, our proposed model is the first optimization model that works in a dual combined mode with maximization and minimization simultaneously to obtain a global optimum value or loci. We call our model GREen PHotosynthesis and Respiratory-based Optimization (GREPHRO). GREPHRO is primarily motivated by the observation of plants’ photosynthesis and respiratory processes, which work on the same set of environmental variables and have a chaining process. Further, our proposed nature-inspired GREPHRO can value the global optima or infimum point considering a single objective function serving as maximization and minimization combined. GREPHRO uses the Lagrange dual principle and non-linear parameters to obtain a linear solution for the infimum optima. We use a set of experiments on the GREPHRO model in the domain of Wireless Sensor Network (WSN)-based Internet of Thing (IoT) to derive the use case of our proposed work. The experimental results and the comparative analysis with the two of our previous works show that GREPHRO takes fewer iterations with more stability of the optimum solution. Moreover, the computational and memory complexities are also less. Therefore, GREPHRO is efficient and suitable for two-dimensional optimization problems in resource-constrained environments for IoTs
Pribadi: A decentralized privacy-preserving authentication in wireless multimedia sensor networks for smart cities
Full text linkDAWS: A Comprehensive Solution Against De-anonymization Attacks in Blockchains
No full textDe-anonymization attacks in blockchains are significant concerns as they compromise the privacy of users on a public ledger. Such attacks, in the form of network analysis and transaction patterns, aim to link a blockchain address to the identity of its owner, potentially revealing sensitive information. Though researchers introduce various solutions using Tor, VPN, and i2P to protect against de-anonymization in blockchains, they have certain limitations: i) non-verification of the private transactions, ii) reveal of the transaction graph, and iii) requirement of a trusted setup that is itself vulnerable to the adversary. All these lead to the revocation of de-anonymization problems. In this paper, we show a novel privacy assurance framework for blockchains. The proposed framework is called De-Anonymization Withstanding Solution (DAWS). DAWS is the first privacy-preserved blockchain framework against de-anonymization attacks. DAWS uses privacy-classifying smart contract execution and a novel consensus called Proof-of-Privacy (PoPri). A set of experiments is executed on PoPri as well as DAWS. The blockchain transactions are modified by including user-defined privacy labels. DAWS can handle attacker advantage ≥0.008 with a privacy breach probability < 0.01% under our threat model. Besides, an improvement in the throughput of DAWS is noticed as compared to Ethereum (almost 80 times) with the Hyperledger configuration for consensus. The gas consumption improvement is 20%. All the listed features enhance the appeal of the proposed DAWS as a robust privacy-preserving solution against blockchain de-anonymization attacks
PETRAK: A solution against DDoS attacks in vehicular networks
No full textIn recent years, the frequently reported incidents of Distributed Denial of Service assaults on vehicular networks in various countries have made researchers find new protective solutions. DDoS attacks can propagate through the charging points for electric vehicles in a charging station and affect the production of critical infrastructures such as electric grids. Existing solutions are efficient in attack detection; however, current systems do not offer multi-level protection, and zero-day vulnerabilities are prone to escape from the detection systems. In this paper, we address the problems mentioned above and introduce the first Machine Learning (ML)–based DDoS protective solution to combine prevention and detection mechanisms in vehicular networks. To be more specific, our proposed model is the first to consider the adaptive traffic threshold to generate the alarm for a suspicious amount of traffic flow in an Intrusion Detection Prevention System (IDPS). We call our proposed approach..
Adaptive Intrusion Detection in Edge Computing using Cerebellar Model Articulation Controller and Spline Fit
Full text linkInternet-of-Thing (IoT) faces various security attacks. Different solutions exist to mitigate the intrusion problems. However, the existing solutions lack behind in dealing with heterogeneity of attack sources and features. The future anticipated demand of devices' connections also urge the need of new solutions addressing the concerns of time consumption and complexity. In this article, we show a novel solution for the intrusion detection in IoT framework. We configure the intrusion detection in the edge computing layer so that the effect of the attack is not propagated to the clouds. Our solution uses cerebellar model articulation controller with kernel map. This combination is very new in the direction of intrusion detection; hence, it emphasizes the novelty of our proposed intrusion detection solution. We name our solution as Cerebellar Model Articulation Controller based Intrusion Detection System (CMACIDS). Additionally, we use spline fitting to the kernel mapping for the model fit; this adds on another novel contribution to CMACIDS. The results obtained with our detection system are compared with the state-of-the-art solutions in terms of complexity, false alarms, and precision of detection. The analysis of the comparative study proves the efficiency of the solution and makes CMACIDS suitable for IoT paradigm. </p
Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey
Full text linkAALMOND: Decentralized Adaptive Access Control of Multi-Party Data Sharing in Industrial Networks
No full textQuantification of Residual Stresses in Relation with Microstructural Changes during Hot Rolling of Titanium Alloys
No full textA fully recrystallized titanium alloy (Ti-6Al-4V) was hot rolled at different
reduction rates of 20%, 40% and 60% deformation. The samples were prepared
with the help of mechanical polishing followed by electro-polishing for subsequent
characterization. A gradient in microstructure have been observed which is reflect
in residual stress development across the thickness of the rolled sheet. The above
measurements were done using EBSD technique and X-ray diffraction,
respectively. It has been observed that the as received material contains bimodal
grain structure. A grain refinement has been observed across the thickness of the
rolled sheet with increase in percentage of deformation. Finer grains were observed
at 60% deformation at mid-thickness (T/2). The stress gradient in the rolling
direction (RD0increases from top surface (T0) to mid-thickness (T/2) of the rolled
sheet. However, it showed a decrease in gradient in the perpendicular direction. The
stresses were compressive in nature throughout the thickness
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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