Abstract:Among various data mining concepts like prediction, clustering, classification, association and outlier discovery, association is a useful technique to extract the interesting relations among data items effectively. Association technique is applied in a number of applications like marketing, education, chemical, bioinformatics, computational linguistics and etc. The important purpose of association is to provide useful information of buying preferences of customers in supermarket in order to increase the sales… Show more
“…In addition to removing CC's restrictions on IoT application development, FC creates new opportunities for 5G (Saranyadevi et al, 2021) and embedded AI (Nguyen et al, 2021). Due to emerging trends in the processing power of EC and developments in hardware architecture, FC will find new usage in a variety of sectors.…”
Section: Fog Networking Outlinementioning
confidence: 99%
“…Intruders often investigate the technique for R2L vulnerability after weaponizing a target, which results in the rise of higher-level process by U2R threat. According to Saranyadevi et al (2021), several R2L risks, like imap & sendmail are brought on by BO in network programmes. A visitor abuses security measures that are either inefficient or incorrectly designed.…”
Section: Threats and Cyber-attacks In F2t Computingmentioning
confidence: 99%
“…For instance, resource-intensive malware attacks that are covert might infect IoT devices, leading to DoS attacks on sensors and actuators. Data is encrypted and locked onto a machine by a virus known as ransomware (Saranyadevi et al, 2021) until a ransom is paid. Small FC network devices are unquestionably prime candidates for ransomware and other malware attacks.…”
Section: Threats and Cyber-attacks In F2t Computingmentioning
confidence: 99%
“…(Sufyan & Banerjee, 2021). Many SL approaches, like DT, KNN, and SNN, have been in use for a very long time (Ahmad & Shah, 2022) (Saranyadevi et al, 2021). DL algorithms include the CNN, LSTM, RNN, GAN, RBFN, and MLP, for instance.…”
Section: Introductionmentioning
confidence: 99%
“…It is effective for application in a diversity of fields (DIP and Computer Vision), attributable to training stability, generalisation, and adaptability for huge data sets. The improvements in software and hardware that facilitated the introduction of DL and concentrated on BD classifications for traffic analysis (Saranyadevi et. al.,2021) point to its potential usage in cyber security domains like CAVID.…”
IoT devices generate enormous amounts of data, which deep learning algorithms can learn from more effectively than shallow learning algorithms. The approach for threat detection may ultimately benefit fog computing or fog networking (fogging). The authors present a cutting-edge distributed DL method for detecting cyberattacks and vulnerability injection (CAVID) in this paper. In terms of the evaluation metrics tested in the tests, the DL model performs better than the SL models. They demonstrated a distributed DL-driven fog computing CAVID approach using the open-source NSL-KDD dataset. A pre-trained SAE was utilised for feature engineering, whereas Softmax was employed for categorization. They used parametric evaluation for system assessment to evaluate the model in comparison to SL techniques. For scalability, accuracy across several worker nodes was taken into consideration. In addition to the robustness, effectiveness, and optimization of distributed parallel learning among fog nodes for enhancing accuracy, the findings demonstrate DL models exceeding classic ML architectures.
“…In addition to removing CC's restrictions on IoT application development, FC creates new opportunities for 5G (Saranyadevi et al, 2021) and embedded AI (Nguyen et al, 2021). Due to emerging trends in the processing power of EC and developments in hardware architecture, FC will find new usage in a variety of sectors.…”
Section: Fog Networking Outlinementioning
confidence: 99%
“…Intruders often investigate the technique for R2L vulnerability after weaponizing a target, which results in the rise of higher-level process by U2R threat. According to Saranyadevi et al (2021), several R2L risks, like imap & sendmail are brought on by BO in network programmes. A visitor abuses security measures that are either inefficient or incorrectly designed.…”
Section: Threats and Cyber-attacks In F2t Computingmentioning
confidence: 99%
“…For instance, resource-intensive malware attacks that are covert might infect IoT devices, leading to DoS attacks on sensors and actuators. Data is encrypted and locked onto a machine by a virus known as ransomware (Saranyadevi et al, 2021) until a ransom is paid. Small FC network devices are unquestionably prime candidates for ransomware and other malware attacks.…”
Section: Threats and Cyber-attacks In F2t Computingmentioning
confidence: 99%
“…(Sufyan & Banerjee, 2021). Many SL approaches, like DT, KNN, and SNN, have been in use for a very long time (Ahmad & Shah, 2022) (Saranyadevi et al, 2021). DL algorithms include the CNN, LSTM, RNN, GAN, RBFN, and MLP, for instance.…”
Section: Introductionmentioning
confidence: 99%
“…It is effective for application in a diversity of fields (DIP and Computer Vision), attributable to training stability, generalisation, and adaptability for huge data sets. The improvements in software and hardware that facilitated the introduction of DL and concentrated on BD classifications for traffic analysis (Saranyadevi et. al.,2021) point to its potential usage in cyber security domains like CAVID.…”
IoT devices generate enormous amounts of data, which deep learning algorithms can learn from more effectively than shallow learning algorithms. The approach for threat detection may ultimately benefit fog computing or fog networking (fogging). The authors present a cutting-edge distributed DL method for detecting cyberattacks and vulnerability injection (CAVID) in this paper. In terms of the evaluation metrics tested in the tests, the DL model performs better than the SL models. They demonstrated a distributed DL-driven fog computing CAVID approach using the open-source NSL-KDD dataset. A pre-trained SAE was utilised for feature engineering, whereas Softmax was employed for categorization. They used parametric evaluation for system assessment to evaluate the model in comparison to SL techniques. For scalability, accuracy across several worker nodes was taken into consideration. In addition to the robustness, effectiveness, and optimization of distributed parallel learning among fog nodes for enhancing accuracy, the findings demonstrate DL models exceeding classic ML architectures.
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