Perspective Chapter: Artificial Intelligence in Security Platform

*Helmy Hany, El Diasty Sherif and Shatila Hazem*

#### **Abstract**

Artificial intelligence (AI) has revolutionized numerous industries and cybersecurity is no exception. AI-powered security platforms are becoming increasingly popular as they provide enhanced protection against cyber threats. These platforms use machine learning algorithms to analyze and learn from data, enabling them to detect and respond to threats more effectively than traditional security systems. In this answer, we will explore the role of AI in security platforms, their benefits, and the future of cybersecurity.

**Keywords:** artificial neural network, cyber security, deep learning, conventional neural network, artificial intelligence

#### **1. Introduction**

AI is used in security platforms to perform various tasks, including Anomaly Detection: AI algorithms can identify patterns in network traffic and system behavior that are outside the norm, indicating potential threats, Intrusion Detection: AI can detect and alert on potential intrusions in real-time, allowing for quick response and mitigation, Malware Detection: AI-powered systems can identify and flag malware infections, enabling organizations to take prompt action to contain and remove the threat, Predictive Analytics: AI algorithms can analyze historical data and predict future threats, enabling organizations to take proactive measures to prevent attacks, Incident Response: AI can automate incident response, reducing the time and resources required to respond to and remediate threats [1].

The use of AI in security platforms offers several benefits, including Improved Accuracy: AI algorithms can detect threats more accurately than traditional security systems, reducing the number of false positives and false negatives, Increased Efficiency: AI-powered systems can automate many security tasks, freeing up human resources for more strategic activities, Enhanced Visibility: AI provides real-time visibility into network traffic and system behavior, enabling organizations to quickly identify and respond to threats, Proactive Defense: AI-powered systems can predict future threats, enabling organizations to take proactive measures to prevent attacks, Cost Savings: AI-powered security platforms can reduce the cost of security operations by automating many tasks and improving incident response times [2].

## **2. Future of cybersecurity**

#### **2.1 The use of AI in security platforms**


## **3. Artificial neural network**

#### **3.1 The structure**

An artificial neural network (ANN) is a computational model inspired by the structure and function of the human brain [3]. It consists of interconnected nodes or neurons that process and transmit information. The basic structure of an ANN includes the following components:


*Perspective Chapter: Artificial Intelligence in Security Platform DOI: http://dx.doi.org/10.5772/intechopen.114020*

**Figure 1.** *The structure of artificial neural network (ANN) [2].*

#### **4. The relation between artificial neural networks and security platforms**

Artificial neural networks (ANNs) and security platforms are two rapidly evolving technologies that are increasingly being combined to create powerful and effective security solutions. ANNs, also known as deep learning networks, are a type of machine learning that is inspired by the structure and function of the human brain. They are particularly well-suited for tasks that involve pattern recognition, such as image and speech recognition, natural language processing, and predictive analytics. Security platforms, on the other hand, are software systems that are designed to protect computer systems and networks from various types of threats, such as malware, viruses, and unauthorized access [6].

The combination of ANNs and security platforms has the potential to revolutionize the field of cybersecurity. By leveraging the strengths of both technologies, security professionals can create more effective and efficient security solutions that can detect and respond to threats in real-time [7]. In this answer, we will explore the relationship between ANNs and security platforms in more detail, including the benefits and challenges of combining these technologies set of nodes, analogous to neurons, organized in layers. A set of weights representing the connections between each neural network layer and the layer beneath it. The layer beneath may be another neural network layer or some other kind of layer [8].

#### **4.1 Benefits of combining ANNs and security platforms**

There are several benefits to combining ANNs and security platforms:

#### *4.1.1 Improved threat detection*

ANNs can be trained to detect and classify various types of threats, such as malware, viruses, and unauthorized access attempts. By integrating ANNs with security platforms, security professionals can create more effective and efficient threat detection systems that can identify and respond to threats in real-time [9].

#### *4.1.2 Enhanced incident response*

ANNs can also be used to enhance incident response capabilities. By analyzing patterns in network traffic and system logs, ANNs can help security professionals identify and respond to security incidents more quickly and effectively [10].

#### *4.1.3 Improved security analytics*

ANNs can be used to analyze large amounts of security-related data, such as network traffic and system logs, to identify patterns and anomalies that may indicate a security threat. By integrating ANNs with security platforms, security professionals can gain more insights into their security posture and make more informed decisions about how to improve their security defenses.

#### *4.1.4 Reduced false positives*

One of the biggest challenges in security is dealing with false positives, which are alerts that are triggered by legitimate activities rather than actual threats. ANNs can help reduce false positives by more accurately identifying threats and distinguishing them from legitimate activities.

#### **4.2 Challenges of combining ANNs and security platforms**

While there are many benefits to combining ANNs and security platforms, there are also several challenges that must be addressed:


A security platform is designed to detect and mitigate various attack scenarios to ensure the protection of an organization's network, systems, and data. These attack scenarios can be categorized into different types, including network attacks, application attacks, malware attacks, social engineering attacks, and insider threats.

#### *4.2.1 Network attacks*

Network attacks target vulnerabilities in network infrastructure and protocols to gain unauthorized access or disrupt network services. A security platform can detect and prevent these attacks through various mechanisms such as intrusion detection systems (IDS), intrusion prevention systems (IPS), firewalls, and network behavior analysis (NBA). Some common network attack scenarios that a security platform can detect include:


#### *4.2.2 Application attacks*

Application attacks exploit vulnerabilities in software applications to gain unauthorized access or manipulate data [14]. A security platform can employ various techniques such as web application firewalls (WAF), code analysis, and vulnerability scanning to detect and prevent application-level attacks. Some common application attack scenarios that a security platform can detect include:


#### *4.2.3 Malware attacks*

Malware attacks involve the distribution and execution of malicious software to compromise systems and steal sensitive information. A security platform can employ various techniques such as antivirus software, sandboxing, and behavioral analysis to detect and prevent malware attacks. Some common malware attack scenarios that a security platform can detect include:


#### *4.2.4 Social engineering attacks*

Social engineering attacks exploit human psychology to deceive individuals into revealing sensitive information or performing actions that compromise security. While security platforms cannot directly detect social engineering attacks, they can provide awareness and protection against associated risks. Some common social engineering attack scenarios include:


#### *4.2.5 Insider threats*

Insider threats involve malicious or negligent actions by individuals within an organization that compromise security. While security platforms cannot completely prevent insider threats, they can monitor user behavior and detect anomalous activities that may indicate insider attacks. Some common insider threat scenarios that a security platform can detect include:


#### **5. Results and recommendations**

#### **5.1 Website traffic forecasting using python**

Website Traffic Forecasting means forecasting traffic on a website during a particular period. It is one of the best use cases of Time Series Forecasting [16].

The dataset I am using for Website Traffic Forecasting is collected from the daily traffic data of a website. It contains data about daily traffic data from June 2021 to June 2022. Our website traffic data is seasonal because the traffic on the website increases during the weekdays and decreases during the weekends. It is valuable to know if the dataset is seasonal or not while working on the problem of Time Series Forecasting. Below is how we can have a look at whether our dataset is stationary or seasonal: will be using the Seasonal ARIMA (SARIMA) model to forecast traffic on the website. Before using the SARIMA model, it is necessary to find the p, d, and q values.

So, this is how you can forecast website traffic for a particular period. Website traffic prediction is one of the best data science project ideas (**Figures 2**–**5**) [17].

**Figure 2.** *Partial autocorrelation [17].*

**Figure 3.** *Autocorrelation [13].*

**Figure 4.** *Modeling predication of the training data [14].*

#### **5.2 Network security with machine learning**

Network security is a critical aspect of protecting computer networks from unauthorized access, data breaches, and other malicious activities. With the increasing complexity and sophistication of cyber threats, traditional security measures alone may not be sufficient to defend against emerging attacks. This has led to the exploration and adoption of machine learning techniques in network security to enhance detection, prevention, and response capabilities [13].

Machine learning is a subset of artificial intelligence that focuses on developing algorithms and models that enable computers to learn from data and make

#### *Perspective Chapter: Artificial Intelligence in Security Platform DOI: http://dx.doi.org/10.5772/intechopen.114020*

#### **Figure 5.**

*Daily traffic of modeling prediction website [18].*

predictions or decisions without being explicitly programmed. In the context of network security, machine learning algorithms can analyze large volumes of network traffic data, identify patterns, and detect anomalies or potential threats that may go unnoticed by traditional security systems [7].

One key application of machine learning in network security is intrusion detection. Intrusion detection systems (IDS) are designed to monitor network traffic and identify any suspicious or malicious activities. Traditional IDS rely on predefined rules or signatures to detect known attacks. However, these systems may struggle to detect new or evolving threats for which no signature exists. Machine learning-based IDS can overcome this limitation by learning normal patterns of network behavior and identifying deviations that may indicate an intrusion attempt. By training on historical network traffic data, machine learning algorithms can build models that can accurately classify network traffic as either normal or malicious based on learned patterns. This enables real-time detection of novel attacks and reduces false positives compared to rule-based systems [13].

Another area where machine learning can enhance network security is in malware detection. Malware refers to any software designed to harm or exploit computer systems [15]. Traditional antivirus solutions rely on signature-based detection methods, which require regular updates to keep up with new malware variants. Machine learning approaches can complement these traditional methods by analyzing file characteristics, behavior patterns, or network traffic associated with malware infections [19]. By training on large datasets containing known malware samples, machine learning algorithms can learn to recognize common features or behaviors indicative of malicious software. This enables the detection of previously unseen malware variants and improves the overall effectiveness of malware detection systems.

Machine learning can also be applied to network anomaly detection. Anomalies in network traffic can be indicative of various security incidents, such as unauthorized access attempts, denial-of-service attacks, or data exfiltration. Traditional anomaly detection methods often rely on predefined thresholds or statistical models that

may not capture all types of anomalies or adapt to changing network conditions [9]. Machine learning algorithms can analyze historical network traffic data and learn normal patterns of behavior. By comparing real-time network traffic against these learned patterns, machine learning-based anomaly detection systems can identify deviations that may indicate an ongoing attack or suspicious activity. This enables early detection and response to potential security incidents, reducing the impact of successful attacks [11].

It is important to note that while machine learning can significantly enhance network security, it is not a silver bullet solution. Machine learning algorithms require high-quality training data and continuous updates to adapt to new threats and evolving network environments [7]. Adversarial attacks, where attackers intentionally manipulate data to deceive machine learning models, also pose challenges to the effectiveness of machine learning-based security systems. Therefore, a holistic approach that combines machine learning with other security measures, such as encryption, access controls, and regular system patching, is essential for robust network security.

#### **6. Conclusion**

AI has brought significant advancements to security platforms, enabling enhanced threat detection, real-time incident response, improved user authentication, advanced surveillance systems, efficient data analysis, and reduced false positives. However, challenges such as data privacy concerns, adversarial attacks, lack of transparency, and ethical considerations need to be addressed for the responsible and effective use of AI in security. The prospects of AI in security platforms are promising, with potential advancements in autonomous systems, cyber threat intelligence, and collaborative defense systems.

#### **Acknowledgements**

First of all, I thank ALLAH for giving me the will to achieve this work.

It is a great honor for me to take this opportunity to express my deep gratitude to Dr. Sherif El Dyasti, Assistant Professor, Electronics and Communication Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport (AAST), for his excellent cooperation, his expert help, continuous encouragement and valuable effort for completion of this work.

My special thanks and appreciation to Prof. Hazem Shatila, Virginia Polytechnic Institute and State University, Professor of Artificial Intelligence & Markovdata, CEO, thanks for spending his precious time and for his continuous encouragement that was behind the completion of this work.

#### **Conflict of interest**

The authors declare no conflict of interest.

*Perspective Chapter: Artificial Intelligence in Security Platform DOI: http://dx.doi.org/10.5772/intechopen.114020*

#### **Author details**

Helmy Hany1 \*, El Diasty Sherif<sup>2</sup> and Shatila Hazem3

1 Cairo Airport Company (CAC), Cairo, Egypt

2 Department of Electronics, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo, Egypt

3 Virginia Tech, Cairo, Egypt

\*Address all correspondence to: hany.nabil@cairo-airport.com; hnabil110@gmail.com

© 2024 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 15**
