Machine learning based solutions for security of Internet of Things (IoT): A survey

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

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Cybersecurity data science: an overview from machine learning perspective

Many fields such as data science, data mining suffered from the rapid growth of data volume and high data dimensionality. The main problems which are faced by these fields include the high computational cost, memory cost, and low accuracy performance. These problems will occur because these fields are mainly used machine learning classifiers. However, machine learning accuracy is affected by the noisy and irrelevant features. In addition, the computational and memory cost of the machine learning is mainly affected by the size of the used datasets. Thus, to solve these problems, feature selection can be used to select optimal subset of features and reduce the data dimensionality. Feature selection represents an important preprocessing step in many intelligent and expert systems such as intrusion detection, disease prediction, and sentiment analysis. An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode. Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. The first improvement includes the use of Opposition Based Learning (OBL) at initialization phase of SSA to improve its population diversity in the search space. The second improvement includes the development and use of new Local Search Algorithm with SSA to improve its exploitation. To confirm and validate the performance of the proposed improved SSA (ISSA), ISSA was applied on 18 datasets from UCI repository. In addition, ISSA was compared with four well-known optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grasshopper Optimization Algorithm, and Ant Lion Optimizer. In these experiments four different assessment criteria were used. The rdemonstrate that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets. The wrapper feature selection mode can be used in different application areas of expert and intelligent systems and this is confirmed from the obtained results over different types of datasets.

Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

Feature selection plays a vital role in building machine learning models. Irrelevant features in data affect the accuracy of the model and increase the training time needed to build the model. Feature selection is an important process to build Intrusion Detection System (IDS). In this paper, a wrapper feature selection algorithm for IDS is proposed. This algorithm uses the pigeon inspired optimizer to utilize the selection process. A new method to binarize a continuous pigeon inspired optimizer is proposed and compared to the traditional way for binarizing continuous swarm intelligent algorithms. The proposed algorithm was evaluated using three popular datasets: KDDCUP99, NLS-KDD and UNSW-NB15. The proposed algorithm outperformed several feature selection algorithms from state-of-the-art related works in terms of TPR, FPR, accuracy, and F-score. Also, the proposed cosine similarity method for binarizing the algorithm has a faster convergence than the sigmoid method.

A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer

Smart grid technology increases reliability, security, and efficiency of the electrical grids. However, its strong dependencies on digital communication technology bring up new vulnerabilities that need to be considered for efficient and reliable power distribution. In this paper, an unsupervised anomaly detection based on statistical correlation between measurements is proposed. The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent cyber-attack. The proposed method applies feature extraction utilizing symbolic dynamic filtering (SDF) to reduce computational burden while discovering causal interactions between the subsystems. The simulation results on IEEE 39, 118, and 2848 bus systems verify the performance of the proposed method under different operation conditions. The results show an accuracy of 99%, true positive rate of 98%, and false positive rate of less than 2%

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A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

Due to the widespread diffusion of network connectivity, the demand for network security and protection against cyber-attacks is ever increasing. Intrusion detection systems (IDS) perform an essential role in today's network security. This paper proposes an IDS based on feature selection and clustering algorithm using filter and wrapper methods. Filter and wrapper methods are named feature grouping based on linear correlation coefficient (FGLCC) algorithm and cuttlefish algorithm (CFA), respectively. Decision tree is used as the classifier in the proposed method. For performance verification, the proposed method was applied on KDD Cup 99 large data sets. The results verified a high accuracy (95.03%) and detection rate (95.23%) with a low false positive rate (1.65%) compared to the existing methods in the literature.

Cyber intrusion detection by combined feature selection algorithm

Feature selection is one of the major problems in an intrusion detection system (IDS) since there are additional and irrelevant features. This problem causes incorrect classification and low detect...

Multivariate correlation coefficient and mutual information-based feature selection in intrusion detection

Clustering is one of the fundamental techniques of data mining that is used for dataset analysis Clustering algorithms group available data based on similarity or distance measures Two important clustering methods used in the literature are hierarchical and density based methods A lot of algorithms have been developed based on these two concepts separately Birch and its extensions are samples of hierarchical based methods DBSCAN and its extensions are samples of density based methods In this paper, a new algorithm is proposed to use both concepts together to achieve an acceptable speed and results, simultaneously At first, it tries to make clusters using a hierarchical method If it decides to make a new cluster, then the algorithm checks for density In this manner, it tries to postpone splitting the clusters To show the effect of the proposed algorithm, some evaluations are performed on some synthetic and real datasets which show some improvements over related works

Developing of a New Hybrid Clustering Algorithm Based on Density

Diagnosis of COVID-19 is necessary to prevent and control the disease. Deep learning methods have been considered a fast and accurate method. In this paper, by the parallel combination of three well-known pre-trained networks, we attempted to distinguish coronavirus-infected samples from healthy samples. The negative log-likelihood loss function has been used for model training. CT scan images in the SARS-CoV-2 dataset were used for diagnosis. The SARS-CoV-2 dataset contains 2482 images of lung CT scans, of which 1252 images belong to COVID-19-infected samples. The proposed model was close to 97% accurate.

Diagnosis of COVID-19 disease using CT scan images and pre-trained models

In Cognitive Radio Networks, secondary users have access to the channel Opportunistically. So, it must be ensured that the severe interference, that damages the main network, does not occur. Using the resource allocation methods provides the best access to the spectrum for the secondary users, increases their performance, and primary users are protected from collisions. The Underlay method is one of the methods of the Cognitive Radio Network. In this method the secondary and the primary users simultaneously present in the channel. The power allocation in an underlay Cognitive Radio Network, may ensure that the secondary users do not interfere with the primary users. Using optimization techniques is a way to improve the performance of these networks. The NSGA-III algorithm, a multi-Objective optimization method, is efficient and offers acceptable results. This paper presents a power allocation method in the Underlay Cognitive Radio Networks based on the improvement of QoS and QoE, using the NSGA-III algorithm.

Mostafa Ghazizadeh-Ahsaee

Papers

Cyber intrusion detection by combined feature selection algorithm

Multivariate correlation coefficient and mutual information-based feature selection in intrusion detection

Developing of a New Hybrid Clustering Algorithm Based on Density

Diagnosis of COVID-19 disease using CT scan images and pre-trained models

Power Allocation in CRNs based on QoS and QoE