Review articleDeep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review
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:
- i.
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.
- ii.
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.
- iii.
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.
- iv.
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.
Section snippets
Cybersecurity attacks
The cybersecurity system is affected by different kinds of attacks such as a denial of service, probe, malware, zero-day, phishing, sinkhole, user root, adversarial attacks, poisoning attack, evasive attack, Integrity attack, and causative attack. Most of the researchers have used deep learning concepts for the detection of these attacks. In this survey, we analyzed different papers related to cybersecurity attack detection with the help of deep learning concept and few of the attacks are
Deep learning and its classification trend of cybersecurity
The most important subsection of machine learning is the deep learning technique. The classification of deep learning based on the cybersecurity attacks is shown below. The classifications of deep learning are portrayed in Fig. 2 and its subsections are discussed in the below sections.
Analysis and discussion
The cybersecurity system is used to detect different kinds of attack based on deep learning method, which is discussed in this survey. The cybersecurity issues are occurred in everywhere for example mails, computer systems, vehicles, entertainments, banks, companies, financial institutions, online data storage, etc... In this survey, we chose a deep learning-based cybersecurity attack detection concept. There are approximately 80 papers are selected interrelated to the survey topic. The
Open issues and future research directions
All these survey papers were produced an effective deep learning method for security attack detection procedure. Each performance results such as accuracy, precision, recall, sensitivity, specificity, and acuteness are best and highly accepted but it contains few complications based on their method, platform, algorithms, etc... So the number of papers are introduced to solve these issues successfully and here a few of the open challengeable issues are scheduled as follows:
- •
The inputs are
Conclusion
The rapid development of cybersecurity attack detection based on deep learning algorithms is summarized in this paper. The applications of deep learning in cybersecurity attacks are successfully discussed. In this survey, nearly 80 papers are selected from the year 2014 to 2019. Here, we introduced several architectures of deep learning methods and their applications. Each survey paper is collected from different kinds of journals such as Elsevier, IEEE, Springer, Sage, Conference papers, and
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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