International journal of smart security technologies
IGI Global
About: International journal of smart security technologies is an academic journal published by IGI Global. The journal publishes majorly in the area(s): Computer science & Blockchain. It has an ISSN identifier of 2640-4079. Over the lifetime, 7 publications have been published receiving 3 citations. The journal is also known as: IJSST.
TL;DR: This work is proposing a phase based human verification process using a combination of neural network and machine learning to avoid spamming over the internet.
Abstract: Human verification is important to avoid spamming over the internet. Internet has grown tremendously in the last decade. Introduction to preference personalization make the users experience and attract more people. This has deemed it necessary to verify that some of the actions are carried out by a human rather than a computer program to generation of invalid traffic and prevent fraudulence. Methods like Captcha and ReCaptcha have extensively used to overcome this challenge but with the advancements in machine leaning and artificial intelligence, these techniques have started to become obsolete. So, to address this issue we are proposing a phase based human verification process using a combination of neural network and machine learning.
TL;DR: In this paper , the authors presented results of studies of the impacts of substation monitoring on reliability indices of distribution systems and demonstrated that the fault detection and reporting time can be greatly reduced by the use of intelligent monitoring system and reliability indices.
Abstract: This paper presents results of studies of the impacts of substation monitoring on reliability indices of distribution systems. It was detected that the fault detecting and repair time was the main cause of the prolonged outages experienced in the system which increases the poor reliability indices of the system. The intelligent monitoring device detect open circuit faults and relay to the base station in 30s compared with 3 to 24 hours it was taking to detect fault. The intelligent substation monitoring device reduced the fault detection time and hence improves the reliability of distribution systems. The system was simulated, and result showed that SAIDI was reduced from 244 hours of outage per annum to just 98 hours of outage per annum while CAIDI was improved from 10 hours of interruption per outage to 5 hours of interruption per outage. This paper demonstrated that the fault detection and reporting time can be greatly reduced by the use of intelligent monitoring system and reliability indices.
TL;DR: The authors evaluate the performance of two FHE schemes (BFV and CKKS) based on data: encoding speed, encryption speed, arithmetic operations (addition and multiplication) speed, and decryption decoding speed using two Python libraries (TenSEAL and PyFHEl).
Abstract: Internet of things (IoT) devices and applications are on the rise, generating large amounts of sensitive and confidential data that need to be processed securely. Due to resource constraints, the data generated is often stored and processed in the cloud. The drawback of data cloud storage and processing is the fact that it can be hacked, leaked, or sold by cloud companies. Fully homomorphic encryption (FHE) allows computation on encrypted data using basic mathematical operations and has recently been successfully implemented using schemes and libraries with better performance. In this paper, the authors propose a mixture of edge-cloud-based security schemes using FHE to secure IoT data. The authors evaluate the performance of two FHE schemes (BFV and CKKS) based on data: encoding speed, encryption speed, arithmetic operations (addition and multiplication) speed, and decryption decoding speed using two Python libraries (TenSEAL and PyFHEl). The encryption and decryption are done at the edge node using a Raspberry Pi 4, while the processing is done at the cloud node using a laptop.
TL;DR: An optimized mobility management approach using Software-Defined Network (SDN) in the future vehicular networks is proposed and a new handover management mechanism is proposed that allows vehicles to select the most optimal network based on multi-criteria metrics.
Abstract: With the rapid development of communication technology, vehicular communications systems are evolving to Intelligent Transportation System (ITS) by providing its wireless network services with increasing demand for high data rate. However, the highly mobile feature of vehicles and varying network densities in such communication systems pose challenges for the mobility management, including frequent handovers, increased delay and failure of the handover process. In this work, we propose an optimized mobility management approach using Software-Defined Network (SDN) in the future vehicular networks. In addition, we have proposed a new handover management mechanism. This new mechanism allows vehicles to select the most optimal network based on multi-criteria metrics. The simulation results show that the proposed approach performs well and achieves an improvement in terms of handover delay and handover failure rate, compared to existing approach.
TL;DR: In numerous nations, laws have not stayed aware of the innovation, leaving critical holes, and in different nations, law implementation and insight offices have been given critical exceptions.
Abstract: In numerous nations, laws have not stayed aware of the innovation, leaving critical holes. In different nations, law implementation and insight offices have been given critical exceptions. At last, without sufficient oversight and implementation, the simple presence of a law may not give satisfactory protection. The expanding complexity of data innovation with its ability to gather, dissect, and spread data on people who have acquainted a desire to move quickly with the interest for enactment. Moreover, new improvements in clinical exploration and care, broadcast communications, progressed transportation frameworks, and monetary exchanges have significantly expanded the degree of data produced by every person