An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks

Khan, Muhammad Altaf; Nasralla, Moustafa M.; Umar, Muhammad Muneer; Ghani-Ur-Rehman, None; Khan, Shafiullah; Choudhury, Nikumani

doi:10.3390/s22020410

Cited by 22 publications

(13 citation statements)

References 22 publications

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“…Early detection and elimination of cyber threats have become of the utmost importance in the contemporary world, be it individuals or mega-organisations dealing with electronic information and data. Adversaries no longer just rely on conventional attack strategies and are evolving over time, which brings about a need for us to develop and evolve the pre-existing defence action plans [119][120][121][122][123][124][125][126][127]. Through this paper, we tend to bring together the various approaches put forward in recent studies through AI-enabled techniques such as ML and DL to enhance a sense of security in mobile networks .…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Gupta

Johri

Srinivasan

et al. 2022

Sensors

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Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Gupta

Johri

Srinivasan

et al. 2022

Sensors

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“…Defensive approaches against adversarial attacks on has also been studied recently [26,27]. Deep learning is also being actively researched on IoT, wireless sensor networks and edge devices [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Deep Learning Based Network Intrusion Detection System across Multiple Balanced and Imbalanced Datasets

2022

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In the modern era of active network throughput and communication, the study of Intrusion Detection Systems (IDS) is a crucial role to ensure safe network resources and information from outside invasion. Recently, IDS has become a needful tool for improving flexibility and efficiency for unexpected and unpredictable invasions of the network. Deep learning (DL) is an essential and well-known tool to solve complex system problems and can learn rich features of enormous data. In this work, we aimed at a DL method for applying the effective and adaptive IDS by applying the architectures such as Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU). CNN models have already proved an incredible performance in computer vision tasks. Moreover, the CNN can be applied to time-sequence data. We implement the DL models such as CNN, LSTM, RNN, GRU by using sequential data in a prearranged time range as a malicious traffic record for developing the IDS. The benign and attack records of network activities are classified, and a label is given for the supervised-learning method. We applied our approaches to three different benchmark data sets which are UNSW NB15, KDDCup ’99, NSL-KDD to show the efficiency of DL approaches. For contrast in performance, we applied CNN and LSTM combination models with varied parameters and architectures. In each implementation, we trained the models until 100 epochs accompanied by a learning rate of 0.0001 for both balanced and imbalanced train data scenarios. The single CNN and combination of LSTM models have overcome compared to others. This is essentially because the CNN model can learn high-level features that characterize the abstract patterns from network traffic records data.

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“…• Third party vendors and open source software: To reduce the attack surface and increase the visibility of network, trusted, product specific whitelisting should be done. There is need for framework that includes end-end enhanced network monitoring and detection techniques [22]. The open source software used in ICS/ SCADA systems leads to ease of installations and extensibility of the performance of systems.…”

Section: Future Research Directionsmentioning

confidence: 99%

Security in Supervisory Control and Data Acquisition (SCADA) based Industrial Control Systems: Challenges and Solutions

Ara

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Industrial control systems (ICS) play a vital role in monitoring and controlling the plants like power grids, oil and gas industries, manufacturing industries, and nuclear power plants. Present research and development in information and communication technologies have changed the domains of industrial control systems from traditional electromagnetic to network- based digital systems. This domain shift has created better interfaces for communication between physical processes and the control units. Eventually, making the complex process of monitoring and controlling the industries easier, with the help of internet connections and computing technologies. The field instruments such as sensors and actuators and the physical processes in industries are controlled and monitored by programmable logic controllers (PLC), remote telemetric units (RTU), and supervisory control and data acquisition systems (SCADA) with the help of communication protocols. The seamless integration of the information technologies (IT) and operational technologies (OT) make the management of the industrial environment foster. However, the inclusion of new technologies that increase the number of internet connections, the new communication protocols, and interfaces that run on open-source software, brings up new threats and challenges in addition to existing vulnerabilities in these classical legacy-based heterogeneous hardware and software systems. Due to the increase in the number of security incidents on critical infrastructures, the security considerations for SCADA systems/ICS are gaining interest among researchers. In this paper, we provide a description of SCADA/ICS components, architecture, and communication protocols. Additionally, we discuss details of existing vulnerabilities in hardware, software, and communication protocols. Further, we highlight some prominent security incidents and their motives behind them. We analyse the existing state of OT and IT security in SCADA systems by classifying the SCADA components among them. Finally, we provide security recommendations based on current trends and also discuss open research problems in SCADA security.

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An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks

Cited by 22 publications

References 22 publications

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Performance Evaluation of Deep Learning Based Network Intrusion Detection System across Multiple Balanced and Imbalanced Datasets

Security in Supervisory Control and Data Acquisition (SCADA) based Industrial Control Systems: Challenges and Solutions

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