Deep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review

Abstract

Cybersecurity mainly prevents the hardware, software, and data present in the system that has an active internet connection from external attacks. Organizations mainly deploy cybersecurity for their databases and systems to prevent it from unauthorized access. Different forms of attacks like phishing, spear-phishing, a drive-by attack, a password attack, denial of service, etc. are responsible for these security problems In this survey, we analyzed and reviewed the usage of deep learning algorithms for Cybersecurity applications. Deep learning which is also known as Deep Neural Networks includes machine learning techniques that enable the network to learn from unsupervised data and solve complex problems. Here, 80 papers from 2014 to 2019 have been used and successfully analyzed. Deep learning approaches such as Convolutional Neural Network (CNN), Auto Encoder (AE), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Generative Adversal Network (GAN) and Deep Reinforcement Learning (DIL) are used to categorize the papers referred. Each specific technique is effectively discussed with its algorithms, platforms, dataset, and potential benefits. The paper related to deep learning with cybersecurity is mainly published in the year 2018 in a large number and 18% of published articles originate from the UK. In addition, the papers are selected from a variety of journals, and 30% of papers used are from the Elsevier journal. From the experimental analysis, it is clear that the deep learning model improved the accuracy, scalability, reliability, and performance of the cybersecurity applications when applied in realtime.

Introduction

Nowadays, cyberspace development is increasing rapidly because of cloud computing [1], big data [2], Internet of Things, and software-based network growth. One of the common problems in cyberspace is cybersecurity. Cybersecurity is a means of safeguarding the systems, applications, and networks from potential digital attacks. The main aim of the adversaries which conducts these attacks is to modify/access the confidential information, laundering money from the users, and interrupting the normal business operations. The challenges associated with implementing the cybersecurity policies on organizations are the large number of devices connected to the network and the novel attacks conducted by hackers. The different kinds of attacks are prevented by using tools like the intrusion detection system, firewalls, scanner, and antivirus software, etc. The devices connected to the network are often subjected to various attacks. The internet offers interconnection between networks as well as supports hardware, intelligence, software, information, and data to be exchanged between each other. Hence, computer networks are very vulnerable to malware or other cybersecurity attacks.

The attackers are experienced to trace out the data from cyberspace [3]. The huge volume of data and confidential information is shielded with cybersecurity and if any attacks happen they automatically alert the whole organization about the same. Moreover, the anomalous detection characteristics, event correlation, and pattern identification are classified using data science concepts applied to cybersecurity. The mobile devices cannot be protected by the Intrusion Detection System(IDS) because of the limited battery power, mobility, and energy consumption characteristics. A protective shield can be built to safeguard the applications using cybersecurity with the help of machine learning algorithms [4], [5], [6], [7], [8]. The modern computer system adds additional computational complexity when processing a huge amount of information and while offering security.

This challenge can be overcome by incorporating techniques of Artificial Intelligence(AI) [9]. The rapid development of computer-based research, methods, and applications to replicate human intelligence is called artificial intelligence (AI). The AI techniques can easily identify the malware present in the application and can take robust actions. It is also used to process the vast amount of information the users generate on a daily basis. Machine learning (ML) with more amounts of security detection software, encoding, and thread extraction characteristics are required to identify these attacks [10]. But, the deep learning concept is more efficient to detect the cybersecurity issues. Deep learning is one of the powerful machine learning techniques powered by AI and this research focuses on the same. The deep learning techniques can process a vast amount of information present in the cybersecurity datasets efficiently by withstanding the attacks [11]. Hence, many of the researchers focused on cybersecurity issues with deep learning concepts [12].

These researchers [13], [14], [15], [16], [17], [18], [19], [20] proposed an elaborate survey of existing cybersecurity applications utilizing deep learning techniques. These researches were mainly conducted to motivate various researchers pursuing their research in the same field to upgrade the security of different organizations vulnerable to various potential attacks. However, these articles did not cover the broad area of cybersecurity datasets used and the weaknesses present in these deep learning techniques. Therefore, the basic objective of this work is to introduce a bibliometric analysis of the deep learning approach used for the detection of potential threats to cybersecurity. Effectively, we have chosen the research papers from the year 2011 to 2020, which are based on cybersecurity issues with deep learning concepts. Ultimately, we analyzed 80 research papers from different kinds of journals and the deeply analyzed survey are effectively mentioned in the below section. Therefore, the outline structure of deep learning based on cybersecurity attack detection is described in Fig. 1.

The contribution of this review article is explained as follows:

• We identify the different cybersecurity attacks namely denial of service, probe, malware, zero-day, phishing, sinkhole, and user root attacks, and how deep learning models solve these attacks.

• Next, the different variants of deep neural network models are analyzed and their functionalities are specified. The different types of neural networks studied are Convolutional Neural Network, Autoencoder, Deep Belief Network, Recurrent Neural Network, Generative Adversal Network, and Deep Reinforcement Learning.

• A comparative analysis is conducted to review the different attacks encountered, the diverse platform used, datasets, and learning models of various researchers in the field of cybersecurity using Deep Learning.

• This survey also provides the challenges faced by existing research and open issues

The rest of this paper is organized as: The cybersecurity attacks are formulated in Section 2. In Section 3, the deep learning-based cybersecurity attacks are discussed. Moreover, the current trend discussion and analysis are carried out in Section 4 as well as the challengeable open issues, and future research directions are formulated in Section 5. At last, the paper is summarized in Section 6.