Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues

Shamshirband, Shahab; Fathi, Mahdis; Chronopoulos, Anthony Theodore; Montieri, Antonio; Palumbo, Fabio Salvatore; Pescapè, Antonio

doi:10.1016/j.jisa.2020.102582

Cited by 86 publications

(58 citation statements)

References 82 publications

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“…Malware has acquired advanced competencies and diversity in features, which significantly raises the importance of cybersecurity. Cybersecurity activities in various organizations have increased (Shamshirband et al, 2020;Shamshirband & Chronopoulos, 2019) due to its vital importance to the aforementioned problem. One of the essential cybersecurity activities is malware analysis.…”

Section: Introductionmentioning

confidence: 99%

Data augmentation based malware detection using convolutional neural networks

Çatak

Ahmed

ŞAHİNBAŞ

et al. 2021

PeerJ Computer Science

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Due to advancements in malware competencies, cyber-attacks have been broadly observed in the digital world. Cyber-attacks can hit an organization hard by causing several damages such as data breach, financial loss, and reputation loss. Some of the most prominent examples of ransomware attacks in history are WannaCry and Petya, which impacted companies’ finances throughout the globe. Both WannaCry and Petya caused operational processes inoperable by targeting critical infrastructure. It is quite impossible for anti-virus applications using traditional signature-based methods to detect this type of malware because they have different characteristics on each contaminated computer. The most important feature of this type of malware is that they change their contents using their mutation engines to create another hash representation of the executable file as they propagate from one computer to another. To overcome this method that attackers use to camouflage malware, we have created three-channel image files of malicious software. Attackers make different variants of the same software because they modify the contents of the malware. In the solution to this problem, we created variants of the images by applying data augmentation methods. This article aims to provide an image augmentation enhanced deep convolutional neural network (CNN) models for detecting malware families in a metamorphic malware environment. The main contributions of the article consist of three components, including image generation from malware samples, image augmentation, and the last one is classifying the malware families by using a CNN model. In the first component, the collected malware samples are converted into binary file to 3-channel images using the windowing technique. The second component of the system create the augmented version of the images, and the last part builds a classification model. This study uses five different deep CNN model for malware family detection. The results obtained by the classifier demonstrate accuracy up to 98%, which is quite satisfactory.

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Section: Introductionmentioning

confidence: 99%

Data augmentation based malware detection using convolutional neural networks

Çatak

Ahmed

ŞAHİNBAŞ

et al. 2021

PeerJ Computer Science

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“…In addition, a meta-heuristic method has derivation-free mechanisms which enables them to optimize problems stochastically. On the other hand, the superior of a meta-heuristic method is its ability to avoid local optima compared to conventional optimization techniques [ 45 , 46 ]. All these advantages can be considered as strengths of the GWO based hybrid models and increase the accuracy of the network.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

Claywell

Nádai

Felde³

et al. 2020

Entropy

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The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.

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“…As depicted in Fig. 5, here we use the forwarding detection method by configuring a firewall or a (stealthy) Intrusion Detection System (IDS) [56] according to the flow rules determined by the controller and check if the switch is forwarding the packets accordingly. • Switch Isolation: while matching the dump data from the switches, each switch is disconnected from its peers if the hash of the flow tables does not match the hash of the updated block in the control-layer chain.…”

Section: B Blockchain-enabled Data Layermentioning

confidence: 99%

SmartBlock-SDN: An Optimized Blockchain-SDN Framework for Resource Management in IoT

et al. 2021

Self Cite

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Software-Defined Networking (SDN) and Blockchain are leading technologies used worldwide to establish safe network communication as well as build secure network infrastructures. They provide a robust and reliable platform to address threats and face challenges such as security, privacy, flexibility, scalability, and confidentiality. Driven by these assumptions, this paper presents an optimized energyefficient and secure Blockchain-based software-defined IoT framework for smart networks. Indeed, SDN and Blockchain technologies have proven to be able to suitably manage resource utilization and to develop secure network communication across the IoT ecosystem. However, there is a lack of research works that present a comprehensive definition of such a framework that can meet the requirements of the IoT ecosystem (i.e. efficient energy utilization and reduced end-to-end delay). Therefore, in this research, we present a layered hierarchical architecture for the deployment of a distributed yet efficient Blockchain-enabled SDN-IoT framework that ensures efficient cluster-head selection and secure network communication via the identification and isolation of rouge switches. Besides, the Blockchain-enabled flow-rules record keeps track of the rules enforced in the switches and maintains the consistency within the controller cluster. Finally, we assess the performance of the proposed framework in a simulation environment and show that it can achieve optimized energy-utilization, end-to-end delay, and throughput compared to considered baselines, thus being able to achieve efficiency and security in the smart network.

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Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues

Cited by 86 publications

References 82 publications

Data augmentation based malware detection using convolutional neural networks

Data augmentation based malware detection using convolutional neural networks

Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

SmartBlock-SDN: An Optimized Blockchain-SDN Framework for Resource Management in IoT

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