Figure 2. ”Cyber security” and ”Deep learning” popularity scores in a range of 0 (min) to 100 (max) through time, with the x-axis representing timestamp information and the y-axis representing the associated popularity score [8].
Deep learning provides advantages for extensive data analytics and statistical machine learning to detect cyber dangers in a cognitive state. However, designing extensive data systems for edge computing environments might be difficult. One of the most critical concerns for CPS is security, both electronic and physical, and the interconnection of physical and cyber systems. Information assurance and data protection in transit from physical and electronic domains and storage facilities are required for such security. Furthermore, asset management and access control are essential for granting or refusing requests for information and processing services, mainly because CPS will interact with nontechnical users and may have an impact beyond administrative borders. Techniques are required to handle unique vulnerabilities resulting from life cycle difficulties such as dwindling manufacturing resources and asset updates. These include technologies such as flexibly time-triggered architectures and structural dynamics control for designing system dynamics over many time scales [9]. In 1998, DARPA (Defense Advanced Research Project Agency) launched the first effort to create an intrusion detection system dataset. The datasets are generated and provided by the MIT Lincoln Laboratory’s Cyber Infrastructure and Technology Division (previously the DARPA Intrusion Detection Assessment Group) to evaluate computer network intrusion detection systems under the supervision of DARPA and AFRL/SNHS. One of the most often used datasets for intrusion detection is the KDD Cup 99 dataset, which contains network traffic records with over forty feature characteristics and one class identification. There are four forms of assaults in the dataset: DoS, R2L, U2R, PROB, and regular data. To better understand security data, we’ve shown the features of intrusion detection datasets, including the elements and their different forms, such as integer, float, or nominal [10].
The Canadian Institute for Cybersecurity established another dataset, the ISCX. The notion of profiles was used to explain assault and distribution techniques in a network setting. Several genuine traces were processed to build reliable profiles of assaults and other events to evaluate intrusion detection systems. The Canadian Cyber Security Institute recently established a new dataset, CSE- CIC-IDS2018, based on a user profile that records network events and behavior. The MAWI dataset is a collection of research and academic institutions that the Japanese network uses to compute the global internet situation across a large territory. The dataset is updated daily to track new traffic. Some researchers utilize this data collection to detect DDoS attacks. Because MAWI involves actual data traffic, the sorts of attacks caught there are diverse. The ADFA data set is a collection of host-level intrusion detection system data sets published by the Australian Securities Academy (ADFA) and widely utilized in intrusion detection product testing. Hydra-FTP, Hydra-SSH, Add Consumer, Java-MeterPerter, Webshell, and two forms of simple assaults, such as Training and Validation, is among the five types of attacks [11].
Equinix tracks legitimate and attacks traces in the CAIDA’08 dataset (Chicago and San Jose data centers). The attack generally comprises SYN, ICMP, and HTTP flood traffic. This dataset is highly skewed towards DDoS assaults since much of the excellent material was deleted after collecting the traffic. Fractions were obtained in Chicago and San Jose on March 19, 2008 and July 17, 2008. The ISOT’10 dataset combines malicious and non-malicious datasets created by research in Information Security and Object Technology at the University of Victoria (ISOT). ISCX’12 simulates traffic from a real-world physical test environment that generates network traffic containing centralized botnets. The Ericsson Research Laboratory and Lawrence Berkeley National Lab retrieved non-harmful traffic, whereas Hon-eynet obtained decentralized botnet data for malicious traffic [12].
The CTU- 13 dataset is a botnet traffic dataset registered at the University of CTU in the Czech Republic in 2011. The Alexa Top Sites list is a popular source of innocuous domain names because it contains one million domain names. The malicious domain names are OSINT and DGArchive. The University of New South Wales created the UNSW-NB15 dataset in 2015. It has 49 distinct features and about 257,700 documents that span nine main types of contemporary assaults. A method for creating benchmark datasets for intrusion detection has been given in. In recent years, the malware industry has evolved into a well-organized market involving enormous money. The most prevalent source of regular information in malware tests is top applications from the Google Play Store. While these applications are not guaranteed to be malware-free, they are the most likely to be because of Google’s testing and the apps’ widespread availability [13].
Perceptron Multilayer (MLP) A supervised learning approach, the multilayer perceptron is a feedforward artificial neural network (ANN). Deep learning or deep neural networks (DNN) use it as their foundation architecture.
Because MLPs are entirely linked, each node in one layer connects to each node in the next layer at a certain weight. Several activation functions are employed to determine the output of a network, including ReLU (Rectified Linear Unit), Tanh, Sigmoid, and Softmax. These activation functions, sometimes called transfer functions, introduce non-linear features into the network, allowing it to learn complicated functional mappings from the input. MLP trains feedforward neural networks using a supervised learning technique known as ”Backpropagation,” the most ”basic building block” of a neural network and the most extensively used algorithm for training feedforward neural networks. Convolutional neural networks were created with the diversity of 2D forms in mind. Image and video recognition, medical image analysis, recommender systems, image classification, image segmentation, natural language processing, financial time series, and other applications employ CNN’s extensively. Although CNNs are most typically employed to analyze visual information, they may also be utilized in cybersecurity. For example, in IoT Networks, CNN-based deep learning models are used for intrusion detection, such as denial-of-service (DoS) assaults, malware detection, and android malware detection. A phishing detection model based on convolutional neural networks has also been demonstrated. A multi-CNN fusion-based model can be deployed for intrusion detection in the region. Although CNN has a higher computational cost, it has the benefit of automatically discovering essential characteristics without the need for human intervention, making it more potent than traditional ANN. Depending on the problem domain and data characteristics, several advanced CNN-based deep learning models, such as AlexNet, Xception, Inception, visual geometry group (VGG), ResNet, and others, or alternative lightweight architecture of the model can be employed to reduce the difficulties.
Learning with Deep Transfer (DTL or Deep TL) Transfer learning is an essential strategy for tackling the fundamental problem of insufficient training data in machines and deep understanding. It is now prevalent in data science since most real-world situations do not have millions of labeled data points to train such complicated models. As a result, it reduces the requirement to train AI models by allowing neural networks to be prepared with tiny quantities of data.
Transfer learning by induction The target task is different from the source task. Several ways are essential to this, including instance transfer, feature representation transfer, parameter transfer, and relational knowledge transfer.
Learning transferable The source and target tasks are the same in this scenario, but the source and target domains are distinct. This is when technologies like instance transfer and feature representation transfer come in handy.
Transfer learning without supervision It’s comparable to the previously described inductive transfer learning, in which the target and source tasks are distinct yet connected. It’s usually investigated in the context of feature representation transfer.
Deep transfer learning may be used for natural language processing (NLP), sentiment classification, computer vision, image classification, speech recognition, medical imaging, and spam filtering, among other things. It also plays an essential part in cybersecurity because of its multiple modeling advantages, such as decreasing training time, boosting output accuracy, and using less training data. For example, the authors offer a ConvNet model for network intrusion detection that uses transfer learning. The authors present a deep feature transfer learning-based signature generation approach that drastically decreases signature production and dissemination time. In, the categorization accuracy was increased to 99.5 percent. The authors describe a feature-based transfer learning strategy utilizing a linear transformation in which they address transfer learning to discover unknown network assaults. The transfer variable enhanced the byte classifier accuracy from 94.72 to 96.90 percent in a semi-supervised transfer learning model for malware detection. Using deep neural network resnet-50 transfer learning, the authors provide the categorization of harmful software. Their results from an experiment on a sample show a 98.62 percent accuracy in categorizing malware families [14].
3. Learning using Deep Reinforcement (DRL or Deep RL)
Deep reinforcement learning (DRL or deep RL) is a machine learning and AI category in which intelligent robots may learn from their actions the same way people do. It integrates reinforcement learning (RL) methods such as Q-learning and deep understanding, such as neural network learning. The job of learning how agents in an environment might execute sequences of behaviors to maximize cumulative rewards is known as reinforcement learning (RL). The problem of a computer agent learning to make decisions through trial and error is addressed in RL. Deep learning is a type of machine learning that uses numerous layers to extract higher-level properties from raw data and make intelligent judgments using neural networks. Deep RL uses deep learning models, such as the deep neural network (DNN), as policy and value function approximators, based on the Markov decision process (MDP) concept. ”A tuple S, A, T, R, where S is a collection of states, A is a set of actions, T is a mapping specifying the transition probabilities from every state-action pair to every conceivable new state, and R is a reward function that attaches a real value (reward) to every state-action pair,” according to Wikipedia. Deep reinforcement learning can be applied to cybersecurity. Compared to typical machine learning models, the authors show that deep RL models employing deep Q-network (DQN) and double deep Q-network (DDQN) yield significant intrusion detection results [15].
Similarly, a deep RL-based adaptive intrusion detection framework for cloud infrastructure based on deep-Q-network (DQN) was given. They claimed greater accuracy and reduced false-positive rates for detecting and identifying novel and sophisticated threats. Furthermore, a hybrid network model, such as an ensemble of networks, may be utilized to construct a practical model that considers their combined benefits. For example, an LSTM network with CNN may be used to identify cyber-attacks, such as virus detection, and detect and mitigate phishing and Botnet assaults across numerous IoT devices. As a result, we may infer that the artificial neural network and deep learning techniques outlined above and their variations or modified approaches can play an essential role in meeting contemporary cybersecurity concerns [16].
4. Conclusion
We thoroughly examined cybersecurity from the standpoint of artificial neural networks and deep learning techniques. To form the perspective of this study, we also evaluated current studies in each area of neural networks. As a result, we’ve briefly covered how different types of neural networks and deep learning methods might be applied for cybersecurity solutions in various situations, as per our aim. Depending on the data properties, a successful security model must include the appropriate deep learning modeling. Before the system can aid with intelligent decision-making, the advanced learning algorithms must be taught using the acquired security data and information linked to the target application. Finally, we’ve outlined and addressed the obstacles that have been encountered, as well as future research possibilities and future initiatives in the field. As a result, the difficulties that have been highlighted present exciting research possibilities in the domain that must be addressed with practical solutions to improve security with time and expand popularity. Overall, we feel that our research on neural networks and deep learning-based security analytics points in the right direction and may be utilized as a reference guide for future research and applications in the field of cybersecurity by both academic and industry specialists.
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