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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[83]. Khan, Aysha, Syeda Mona Hassan, and Shahzad Sharif Mughal. "Biological Evaluation of a Herbal Plant: Cichrorium intybus." Science and Technology 6.2 (2022): 26-38.
[84]. Muneeza Munir, Syeda Mona Hassan, Shahzad Sharif Mughal, Alvina Rafiq, Evaluation of Biological Approaches of Green Synthesized MgO Nanoparticles by Syzygium aromaticum, International Journal of Atmospheric and Oceanic Sciences. Volume 6, Issue 2, December 2022 , pp. 44-53. doi: 10.11648/j.ijaos.20220602.12
[85]. Lashari, Aamna, Syeda Mona Hassan, and Shahzad Sharif Mughal. "Biosynthesis, Characterization and Biological Applications of BaO Nanoparticles using Linum usitatissimum." American Journal of Applied Scientific Research 8.3 (2022): 58-68.
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