EMITTER - International Journal of Engineering Technology
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Web Application Security Education Platform Based on OWASP API Security Project
Full text linkThe trend of API-based systems in web applications in the last few years keeps steadily growing. API allows web applications to interact with external systems to enable business-to-business or system-to-system integration which leads to multiple application innovations. However, this trend also comes with a different surface of security problems that can harm not only web applications, but also mobile and IoT applications. This research proposed a web application security education platform which is focused on the OWASP API security project. This platform provides different security risks such as excessive data exposure, lack of resources and rate-limiting, mass assignment, and improper asset management which cannot be found in monolithic security learning application like DVWA, WebGoat, and Multillidae II. The development also applies several methodologies such as Capture-The-Flag (CTF) learning model, vulnerability assessment, and container virtualization. Based on our experiment, we are successfully providing 10 API vulnerability challenges to the platform with 3 different levels of severity risk rating which can be exploited using tools like Burp Suite, SQLMap, and JWTCat. In the end, based on our performance experiment, all of the containers on the platform can be deployed in approximately 16 seconds with minimum storage resource and able to serve up to 1000 concurrent users with the average throughput of 50.58 requests per second, 96.35% successful requests, and 15.94s response time
Bearing/Incipient/Open Phase Fault Detection and Diagnosis of Multi-Phase Induction Motor Drives Equipped By GBDTI2HO Technique
Full text linkIn this paper, a hybrid system is performed with fault detection and diagnosis on multi-phase induction motor (IM). The proposed method is hybrid of integrated Harris Hawk optimization (IHHO) and gradient boosting decision trees (GBDT) thus called the GBDTI2HO method. Here, additional operators are included in this paper to improve HHO’s search behaviour namely crossover and mutation. Distorted waveforms are generated by different frequency patterns to indicate the time domain frequency as an assessment of failure. For this signal representation, the discrete wavelet transformation (DWT) is suggested. It extracts the characteristics and forwards them to IHHO technique to form the possible data sets. After the generation of the data set, GBDT classifies the ways of failure reached as winding of stator in multi-phase IM. The implementation of the proposed system is compared with existing systems, such as ANN, S-Transform and GBDT. The proposed method is executed on MATLAB/Simulink work platform to demonstrate the successfulness of proposed system, statistical measures are determined, as precision, sensitivity and specificity, mean median and standard deviation. For demonstrating the successfulness of proposed system, statistical measures are determined as precision, sensitivity, specificity, mean median as well as standard deviation. In 50 trails the proposed method, 0.98 for accuracy, 0.96 for specificity, 1.60 for recall as well as 0.97 for precision. In 100 trail the proposed method, 0.96 for accuracy, 0.93 for specificity, 0.87 for recall as well as 0.99 for precision
Revisiting Routing Protocols to Design Energy Aware Wireless Body Area Network
Full text linkWireless body area networks (WBANs) a special type of wireless sensor networks (WSNs) in which sensor nodes to actualize continuous wearable wellbeing observing of patients are able to provide improved healthcare services in a distributed infrastructure less environments. However, the mobile node, due to less battery power, can easily suffer from the problem of energy level when control packets are transfer among nodes—a problem that can occurs by the fact that some sensor nodes may select wrong cluster head with inappropriate path and waste the resources. Although many energy efficient methods have been designed for the traditional sensor networks, there has been limited focus on incorporating WBANs into energy efficient schemes. Therefore, in order to incorporate above issue we revisit the already designed traditional energy efficient methods with cluster head selection protocols and optimal path transformation. Therefore, we encourage researchers to insert WBANs with existing methods to improve performance. However, some work has been done in WBANs that uses energy efficient methods to manage the routing issue, this research domain requires further research attention. Therefore, we discuss the current research work and purpose many future directions of research
The Determination of Optimal Operating Condition For an Off-Grid Hybrid Renewable Energy Based Micro-Grid: A Case Study in Izmir, Turkey
Full text linkNowadays, off-grid systems, which do not require grid connection investment instead of grid connected systems, have become quite feasible. In this study, a feasibility analysis was carried out for a hybrid energy system using solar and wind energy sources to supply to uninterrupted electricity demand of a region with 100 villas in Izmir, Turkey. It has been shown that how changes cost of the hybrid energy system sizing according to the control strategies by using the HOMER software. In the paper, two different control strategies are determined as Cycle Charging (CC) and Load Following (LF), and then the control strategies are compared. According to the results obtained as a result of the simulations, it has been revealed that the research region to operate with CC can supply to the electrical energy demand with lower capacity system architecture. The CC was found to be more suitable for the research region than LF in terms of both Cost of Energy (COE) and Net Preset Cost (NPC)
SDN-Based Network Intrusion Detection as DDoS defense system for Virtualization Environment
Full text linkNowadays, DDoS attacks are often aimed at cloud computing environments, as more people use virtualization servers. With so many Nodes and distributed services, it will be challenging to rely solely on conventional networks to control and monitor intrusions. We design and deploy DDoS attack defense systems in virtualization environments based on Software-defined Networking (SDN) by combining signature-based Network Intrusion Detection Systems (NIDS) and sampled flow (sFlow). These techniques are practically tested and evaluated on the Proxmox production Virtualization Environment testbed, adding High Availability capabilities to the Controller. The evaluation results show that it promptly detects several types of DDoS attacks and mitigates their negative impact on network performance. Moreover, it also shows good results on Quality of Service (QoS) parameters such as average packet loss about 0 %, average latency about 0.8 ms, and average bitrate about 860 Mbit/s
A Review on Forwarding Strategies in NDN based Vehicular Networks
Full text linkNamed Data Networking (NDN) is a model that has been proposed by many researchers to alter the long-established IP based networking model. It derives the content centric approach rather than host-based approach. This is gaining even more traction in the wireless network and is able to replace the conventional IP-based networking. Up to now, NDN has proven to be fruitful when used with certain limitations in vehicular networks. Vehicular networks deal with exchanging information across fast moving complex vehicle network topology. The sending and receiving of information in such a scenario acts as a challenge and thus requires an effective forwarding strategy to address this problem. Different research work has provided with multiple forwarding strategy that solves the current problem up to some limit but further research work is still longed for to get an optimum solution. This paper provides a brief survey on current existing forwarding strategies related to vehicular networks using NDN as well as providing information on various resources and technologies used in it
Exploring the Time-efficient Evolutionary-based Feature Selection Algorithms for Speech Data under Stressful Work Condition
Full text linkInitially, the goal of Machine Learning (ML) advancements is faster computation time and lower computation resources, while the curse of dimensionality burdens both computation time and resource. This paper describes the benefits of the Feature Selection Algorithms (FSA) for speech data under workload stress. FSA contributes to reducing both data dimension and computation time and simultaneously retains the speech information. We chose to use the robust Evolutionary Algorithm, Harmony Search, Principal Component Analysis, Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, and Bee Colony Optimization, which are then to be evaluated using the hierarchical machine learning models. These FSAs are explored with the conversational workload stress data of a Customer Service hotline, which has daily complaints that trigger stress in speaking. Furthermore, we employed precisely 223 acoustic-based features. Using Random Forest, our evaluation result showed computation time had improved 3.6 faster than the original 223 features employed. Evaluation using Support Vector Machine beat the record with 0.001 seconds of computation time
Analysis of control factors and surface integrity during wire-EDM of Inconel 718 alloy using T-GRA approach
Full text linkIn today’s competitive modern manufacturing sectors, there is a vital need of utter precision and rigorous processing using various manufacturing approaches that directly influences the cost and processing duration of mechanized materials in addition to the consistency of the finished products. Therefore, it’s essential to figure out the required output by adjusting the control factors of any machining techniques which resulted in optimal values of the desired outcome. In this study, machining evaluation and process optimization is carried out on volumetric extraction of material namely material removal rate (MRR), kerf obtained during the machining (KW) and surface roughness (SR) of Inconel 718 superalloy during CNC controlled wire- electrical discharge machining. Four controllable factors- pulse interval, wire speed, pulse duration and peak current are considered to investigate the influence on performance measures. Taguchi's L16 has been used to construct the set of experiments before physical experimental runs and most influencing factors have been evaluated using ANOVA. SEM images and EDXS analysis have been resorted to examine the morphology of Inconel 718. These findings assist in identifying the topography of the machined surface. Further, the optimum integration has been obtained for the best yield and recorded using grey relational analysis integrated with Taguchi’s technique (T-GRA). The unfamiliarity of the work is based on consideration of zinc coated thin wire electrode and Taguchi-Grey combined approach of modelling with four levels of experimental design
Plant disease prediction using convolutional neural network
Full text linkEvery year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely feature extraction, downsizing image and classification. In CNN, the convolutional layer extracts the feature from plant image. The pooling layer downsizing the image. The disease classification was done in dense layer. The proposed model can recognize 38 differing types of plant diseases out of 14 different plants with the power to differentiate plant leaves from their surroundings. The performance of VGG16 and Resnet34 was compared. The accuracy, sensitivity and specificity was taken as performance Metrix. It helps to give personalized recommendations to the farmers based on soil features, temperature and humidit
Virtual Reality Technology and Speech Analysis for People Who Stutter
Full text linkVirtual reality (VR) technology provides an interactive computer-generated experience that artificially simulates real-life situations by creating a virtual environment that looks real and stimulates the user’s feelings. During the past few years, the use of VR technology in clinical interventions for assessment, rehabilitation and treatment have received increased attention. Accordingly, many clinical studies and applications have been proposed in the field of mental health, including anxiety disorders. Stuttering is a speech disorder in which affected individuals have a problem with the flow of speech. This can manifest in the repetition and prolongation of words or phrases, as well as in involuntary silent pauses or blocks during which the individual is unable to produce sounds. Stuttering is often accompanied by a social anxiety disorder as a secondary symptom, which requires separate treatment. In this study, we evaluated the effectiveness of using a VR environment as a medium for presenting speech training tasks. In addition, we evaluated the accuracy of a speech analyzer module in detecting stuttering events