Acceptance of Deep Learning Models by Cyber security Managers in Detecting Fake Digital Identifies: A Case Study of Detection of Fake News on Social Media Platforms

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Acceptance of Deep Learning Models by Cybersecurity Managers in Detecting Fake Digital Identifies: A Case Study of Detection of Fake News on Social Media Platforms

Introduction

The literature review provided a review of past studies on the use of deep learning models and approaches in the detection of fake digital identities. The key focus will be using the case study of detection of fake news on social media platforms while determining the acceptance of deep learning models by cybersecurity managers.  The chapter will also discuss the interrelationship between the study variables in the conceptual framework.

Deep Learning Approach

Deep learning is a subset of machine learning that uses artificial neural networks with many layers to learn from data. It is inspired by the way the human brain works, where information is processed through layers of interconnected neurons. The deep learning approach involves training a neural network using a large amount of data to make accurate predictions or classifications. The neural network is designed to learn from the data by adjusting the weights and biases of the network’s neurons. The goal is to minimize the error between the predicted output and the actual output, which is achieved through a process called backpropagation. Deep learning is particularly well-suited to handling large and complex datasets, such as images, videos, and natural language, where traditional machine learning algorithms may struggle to extract meaningful patterns. It has been successfully applied in a wide range of domains, including computer vision, natural language processing, speech recognition, and robotics. Overall, the deep learning approach has revolutionized the field of artificial intelligence, enabling breakthroughs in many areas and paving the way for new and exciting applications in the future.

Deep Learning Approaches Implemented In Cybersecurity

Intrusion detection is the process of identifying malicious activity on a computer network or system (Berman et al., 2019). Deep learning has shown promising results in intrusion detection as it can learn to detect complex patterns and anomalies in network traffic data. One approach to intrusion detection using deep learning is to train a neural network to classify network traffic as either normal or malicious. This can be done by feeding the network labeled data, where the labels indicate whether each network flow is normal or malicious. The network can then learn to identify patterns and features in the network traffic that are associated with malicious activity. Another approach is to use autoencoders to detect anomalies in network traffic. An autoencoder is trained to learn a compressed representation of the normal network traffic, and then any new traffic that does not match this representation is flagged as an anomaly (Berman et al., 2019). This approach does not require labeled data, making it useful in situations where labeled data is scarce or expensive. Deep learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have also been used for intrusion detection. CNNs can be used to detect patterns in network packet payloads, while RNNs can be used to analyze network traffic over time and detect temporal patterns.

Deep autoencoders are neural networks designed for unsupervised learning that can learn to represent high-dimensional data in a compressed, low-dimensional space. An autoencoder consists of two parts: an encoder and a decoder (Zhou & Paffenroth, 2017). The encoder maps the input data to a lower-dimensional latent representation, and the decoder maps the latent representation back to the original input data. A deep autoencoder is a type of autoencoder that has multiple hidden layers in both the encoder and decoder. The additional layers allow the autoencoder to capture more complex and abstract features of the input data. Deep autoencoders are often used for tasks such as dimensionality reduction, feature extraction, and data compression. Training a deep autoencoder typically involves minimizing the reconstruction error between the original input data and the reconstructed output data. This can be done using a variety of optimization techniques, such as stochastic gradient descent, and different loss functions, such as mean squared error (Krizhevsky & Hinton, 2011). Deep autoencoders have been used in a variety of applications, including image and speech recognition, anomaly detection, and natural language processing. They are particularly useful in situations where labeled training data is scarce or expensive, as they can learn to represent the underlying structure of the data without explicit supervision.

Restricted Boltzmann machines

 Restricted Boltzmann machines (RBMs) are a type of artificial neural network that belong to the family of unsupervised learning algorithms. They are composed of two layers of nodes, visible and hidden, where the nodes in each layer are fully connected to each other but there are no connections between nodes within the same layer (Fischer, A., & Igel, 2012). The main goal of an RBM is to learn a probability distribution over the input data. This is achieved by adjusting the weights of the connections between the visible and hidden layers through a process called training. During training, the RBM learns to reconstruct the input data from the hidden layer activations and vice versa. RBMs are particularly useful in the context of dimensionality reduction, feature learning, and generative modeling. They have been successfully applied in a wide range of domains, such as image recognition, natural language processing, and recommendation systems.

Recurrent neural networks

Recurrent Neural Networks (RNNs) are a class of neural networks designed to work with sequential data such as time series or text. Unlike feedforward neural networks, RNNs have feedback connections that allow information to persist over time, allowing them to capture temporal dependencies in data (Grossberg, 2013). At each time step, an RNN takes an input vector and its hidden state vector from the previous time step as inputs, and produces an output vector and a new hidden state vector as outputs. The hidden state vector acts as a kind of memory that stores information about previous inputs, and is updated at each time step. One of the key advantages of RNNs is their ability to handle variable-length inputs, as the network can process each input sequentially, one at a time. This makes RNNs well-suited for tasks such as language modeling, speech recognition, and machine translation.

Generative adversarial networks

Generative adversarial networks (GANs) are a type of machine learning model that are used for generating new data samples that are similar to a given dataset. GANs consist of two neural networks: a generator network and a discriminator network (Creswell et al., 2018). The generator network takes in a random noise vector as input and generates a new sample that is intended to be similar to the training data. The discriminator network takes in both real training samples and generated samples from the generator network as input, and its goal is to distinguish between the real and generated samples. The two networks are trained in an adversarial fashion, with the generator network trying to produce samples that can fool the discriminator network, and the discriminator network trying to correctly identify the real samples (Wang et al., 2017). Through this process of back-and-forth training, the generator network learns to produce samples that are increasingly similar to the training data, while the discriminator network learns to correctly distinguish between the real and generated samples. Once the GAN has been trained, the generator network can be used to generate new samples that are similar to the original training data.

Using Deep Learning Models In Detection of Fake News on Social Media Platforms

The detection of fake news on social media platforms is a challenging problem that has attracted significant attention in recent years. Deep learning models can be used to address this problem by automatically analyzing large amounts of textual data to identify patterns and characteristics that are indicative of fake news (Tashtoush et al., 2022). One approach to using deep learning models for fake news detection is to train a neural network on a large dataset of labeled news articles, where the labels indicate whether the news is real or fake. The neural network can then be used to predict the label of new articles by analyzing their content and identifying patterns that are consistent with fake news. Several techniques can be used to preprocess the textual data before feeding it into the neural network, such as word embedding and natural language processing techniques. Additionally, various architectures of neural networks can be used, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which have been shown to be effective in processing sequential data (Aldhyani & Alkahtani, 2023). Another approach to using deep learning models for fake news detection is to combine them with other techniques, such as network analysis and fact-checking. For example, the spread of fake news on social media can be tracked and analyzed using network analysis techniques, and fact-checking can be used to verify the accuracy of the news. Overall, using deep learning models in the detection of fake news on social media platforms is a promising area of research that has the potential to help mitigate the negative effects of fake news and promote the spread of accurate information.

Theoretical Framework

The theoretical framework that will be applied for this research study is the unified theory of acceptance and use of technology-3 (UTAUT-3) model. The UTAUT-3 model will be applied in the research study because its description agrees with the research objective. The UTAUT-3 model was developed by Farooq et al. (2017) as the UTAUT-2 framework extension and comprises eight technology acceptance determinants that include performance expectancy (PE), social behaviour (SB), effort expectancy (EE), facilitating conditions (FC), habit (HB), price value (PV), personal innovativeness in IT (PI), and hedonic motivation (HM). The UTAUT-3 model authors claim that it has technology adoption prediction potency of 66 per cent in terms of explanation. In addition to the eight constructs, the model has four moderators of the constructs that include age, gender, experience, and voluntariness. The UTAUT-3 model will be preferred in this research study since it will adequately respond to the research questions (Farooq et al., 2017).

The model predicts adequately the incidence of technology acceptance and adoption. Thus, the UTAUT-3 model is useful for researchers to evaluate the possibility of technology adoption depending on how the model variables correlate. Data collection will be done using those constructs in connection with the variables digital driver’s license verification devices, and acceptance and adoption of the technology. The UTAUT-3 variables data will be analyzed using some analysis tools to determine various factors like the influence of a variable on the cybersecurity issues pertaining to use of DDL verification technology. It is important to note that only four of the main UTAUT-3 model constructs will be applied in the research study, and they include performance expectancy, facilitating conditions, social influence, and price value. Through applying the theoretical model in a new social and technological context, the study will contribute in the field of information technology acceptance (Farooq et al., 2017).

The performance expectancy (PE) variable will be instrumental in this study since it is defined as the user conviction of the target technology to improve the operations to obtain gains in terms of business success (Venkatesh et al., 2012). In this study, PE implies that IT managers’ belief that acceptance and adoption of DDL verification devices will enhance security of the organization’s clients and the staff. Thus, it will be used to ascertain the level of effectiveness to ensure that the cyber space is free from any insecurity concerns. Therefore, the PE variable will be used to test the hypotheses H01 and Ha1.

The facilitating Conditions (FC) variable refers to the belief by the user that the availability of infrastructure and institutional support assists targeted technology use (Venkatesh et al., 2012). Typically, infrastructure and technical support that help in usage of a technology system are classified under the facilitating conditions. The Facilitating Conditions affect both the actual usage and the user intention (Venkatesh et al., 2012). Therefore, the variable will be used to test the hypotheses H03 and Ha3: Facilitating Conditions influence the IT managers’ acceptance of DDL verification devices and FCs influence the adoption of the DDL verification devices by the IT managers in their organizations.

The social influence refers to the extent which a person believes that the immediate society expects him to adopt some technology (Venkatesh et al., 2012). In the context of this research study, social influence implies the external pressure that affects the acceptance and adoption of DDL verification devices by IT managers in organizations. Therefore, this study will test the hypothesis stating that social influence affects the behavior intention of IT managers to accept and adopt the DDL verification devices in organizations.

The Price Value (PV) variable refers to the consumers’ context whereby an individual weighs the perceived benefits against the amount spent to obtain an item. The study will test the IT managers’ perception about the cost of investing in the DDL verification technology. Therefore, the study will test the hypotheses H02 and Ha2: PV influences the acceptance of technology, and PV influence on the adoption of DDL verification devices by IT managers of organizations.

Conclusion

References

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