Evaluation of Machine Learning approaches for resource constrained IIoT devices

Akubathini, Premkumar; Chouksey, Sameer; Satheesh, Hariram

doi:10.1109/icitee53064.2021.9611880

Cited by 6 publications

(7 citation statements)

References 4 publications

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“…For system evaluation, the specific set will be applied to a modeling example, where assuming that [ 43 – 45 ]:

is the m x 1 state vector, it is now easy to represent the state-space representation as below:

…”

Section: Phishing Campaigns Identificationmentioning

confidence: 99%

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Tang

2022

Applied Bionics and Biomechanics

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Most of the sophisticated attacks in the modern age of cybercrime are based, among other things, on specialized phishing campaigns. A challenge in identifying phishing campaigns is defining a classification of patterns that can be generalized and used in different areas and campaigns of a different nature. Although efforts have been made to establish a general labeling scheme in their classification, there is still limited data labeled in such a format. The usual approaches are based on feature engineering to correctly identify phishing campaigns, exporting lexical, syntactic, and semantic features, e.g., previous phrases. In this context, the most recent approaches have taken advantage of modern neural network architectures to record hidden information at the phrase and text levels, e.g., Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). However, these models lose semantic information related to the specific problem, resulting in a variation in their performance, depending on the different data sets and the corresponding standards used for labeling. In this paper, we propose to extend word embeddings with word vectors that indicate the semantic similarity of each word with each phishing campaigns template tag. These embedded keywords are calculated based on semantic subfields corresponding to each phishing campaign tag, constructed based on the automatic extraction of keywords representing these tags. Combining general word integrations with vectors is calculated based on word similarity using a set of sequential Kalman filters, which can then power any neural architecture such as LSTM or CNN to predict each phishing campaign. Our experiments use a data indicator to evaluate our approach and achieve remarkable results that reinforce the state-of-the-art.

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“…For system evaluation, the specific set will be applied to a modeling example, where assuming that [ 43 – 45 ]:

is the m x 1 state vector, it is now easy to represent the state-space representation as below:

…”

Section: Phishing Campaigns Identificationmentioning

confidence: 99%

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Tang

2022

Applied Bionics and Biomechanics

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“…From the literature above, we conclude that researchers have identified that the sheer increase in cyberattacks can only be effectively dealt with the help of machine learning methodologies [ 12 , 13 ].…”

Section: Related Literaturementioning

confidence: 99%

“…This particular sports volleyball team has several IoT technologies that do not make the most of their capabilities in the context of cyber security. The scenario aims to eliminate technology waste, make the most of already installed products, and maintain the responsibility of business application and information technology partners [ 13 , 19 ].…”

Section: The Use Case Of the Volleyball Industrymentioning

confidence: 99%

Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

Xiao

Chen

2022

Computational Intelligence and Neuroscience

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Individual and team performance can be improved by utilizing “smart” devices and applications that are connected through networks. In sports, the Internet of Things (IoT) refers to all of the “smart” devices and applications linked through networks to reduce injuries to the bare minimum, develop advanced training techniques, and apply analytical advanced sports improvement methodologies to improve sports performance in general. The Internet of Things (IoT) in sports is closely related to the objective of both security and privacy in sports, which has become a topic of crucial concern for the sports business in recent years, as evidenced by the adoption of IoT in sports years. For this reason, security flaws can have catastrophic consequences, including the disclosure of personal data, the manipulation of statistical findings, the harming of organizations’ reputations, and enormous financial losses for the sporting organization. One or more of the consequences, as previously mentioned, is related to sports organizations and the athletes who are members of those organizations, and they have a direct impact on the corresponding set of sports-related, medical-related, and paramedical enterprises, specifically those that provide specialized sports equipment and associated services. A critical need to detect and quantify threats has long been recognized to better support decision-making when adopting or constructing a safe and reliable sports Internet-of-Things infrastructure, which is becoming increasingly common. Using advanced machine learning algorithms, this research provides a methodology for technology optimization in cybersecurity defenses that is then used in a unique case study utilizing volleyball players to demonstrate its effectiveness. In conjunction with a Monte Carlo optimization technique, an upgraded variant of fuzzy cognitive maps (FCM) is presented in greater detail. This model is utilized for a specific scenario of risk identification of volleyball industry, assessment, and optimization for IoT sports networks.

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“…When tackling energy network security issues, researchers should take a systematic approach and examine all phases of the holistic resilience cycle. To enhance the cyber-physical integrity of electrical power networks [ 6 , 7 ], the idea of the Holistic Resilience Cycle was presented. This is a structured method to power system security that is defined by four steps (prevention and planning, detection, mitigation and reaction, and system recovery) as being inextricably linked and comprehensible only in context.…”

Section: Introductionmentioning

confidence: 99%

Using Advanced Analytic Techniques to Optimize Cyber-Physical Defensive Plans in Sports Infrastructures and Facilities

Wang

2022

Computational Intelligence and Neuroscience

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The technical projects for securing a network of infrastructures and processes are designed, financed, carried out, maintained, and operated within a general infrastructure system, which can gather activities and funds of the private or public sector. The importance of such projects for modern society is enormous, and there is a positive correlation between the size of infrastructure projects and the strength of the national economy. At the same time, it falls within the critical infrastructure sector most of the time. This work, taking into account the massive importance of investments made in the field of sports and the corresponding significance of the design and implementation of robust cybersecurity systems, presents an innovative optimization system for the design, performance, and adaptation of safeguards of technical projects, which require a high level of security standards. A realistic optimization system of low computational complexity is proposed and tested, dividing the problem into a series of subproblems of one-step optimization, which can be solved with great ease and without requirements on computational resources. The great innovation of the proposed system is that the separation is done so that the solution results from the optimal individual solutions of the subproblems without affecting the final result.

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Evaluation of Machine Learning approaches for resource constrained IIoT devices

Cited by 6 publications

References 4 publications

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

A Deep Learning Filter that Blocks Phishing Campaigns Using Intelligent English Text Recognition Methods

Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

Using Advanced Analytic Techniques to Optimize Cyber-Physical Defensive Plans in Sports Infrastructures and Facilities

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