Voting extreme learning machine based distributed denial of service attack detection in cloud computing

JJ, Kumar SNP. An approach for DoS attack detection in cloud computing using sine cosine anti coronavirus optimized deep maxout network

Purpose

The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method.

Design/methodology/approach

This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques.

Findings

The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively.

Originality/value

The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.

Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

Intelligent Cyber Security Framework Based on SC-AJSO Feature Selection and HT-RLSTM Attack Detection

Cyber security is identified as an emerging concern for information technology management in business and society, owing to swift advances in telecommunication and wireless technologies. Cyberspace security has had a tremendous impact on numerous crucial infrastructures. Along with current security status data, historical data should be acquired by the system to implement the latest cyber security defense and protection. It also makes intelligent decisions that can provide adaptive security management and control. An intelligent cyber security framework using Hyperparameter Tuning based on Regularized Long Short-Term Memory (HT-RLSTM) technique was developed in this work to elevate the security level of core system assets. To detect various attacks, the proposed framework was trained and tested on the collection of data. Owing to missing values, poor scaling, imbalanced and overlapped data, the data was primarily incomplete and inconsistent. To elevate the decision making for detecting attacks, the inconsistent or unstructured data issue was addressed. The missing values were handled by this work along with scaling performance using the developed Kernelized Robust Scaler (KRS). Using the developed Random Over Sample-Based Density-Based Spatial Clustering Associated with Noise (ROS-DBSCAN), the imbalanced and overlapped data were handled, which was followed by the relevant feature selection of data utilizing the Sine Cosine-Based Artificial Jellyfish Search Optimization (SC-AJSO) technique. The data were split under the provision of Stratified K-Fold cross-validation along being trained in the proposed HT-RLSTM. The experimental analysis depicted that better accuracy was attained in detecting attacks by the proposed work for different datasets. When analogized with prevailing state-of-the-art methods, a low false detection rate, as well as computation time, was attained by the proposed scheme.

Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm

This study proposes a novel framework to improve intrusion detection system (IDS) performance based on the data collected from the Internet of things (IoT) environments. The developed framework relies on deep learning and metaheuristic (MH) optimization algorithms to perform feature extraction and selection. A simple yet effective convolutional neural network (CNN) is implemented as the core feature extractor of the framework to learn better and more relevant representations of the input data in a lower-dimensional space. A new feature selection mechanism is proposed based on a recently developed MH method, called Reptile Search Algorithm (RSA), which is inspired by the hunting behaviors of the crocodiles. The RSA boosts the IDS system performance by selecting only the most important features (an optimal subset of features) from the extracted features using the CNN model. Several datasets, including KDDCup-99, NSL-KDD, CICIDS-2017, and BoT-IoT, were used to assess the IDS system performance. The proposed framework achieved competitive performance in classification metrics compared to other well-known optimization methods applied for feature selection problems.

An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection

The increasing use of Internet of Things (IoT) applications in various aspects of our lives has created a huge amount of data. IoT applications often require the presence of many technologies such as cloud computing and fog computing, which have led to serious challenges to security. As a result of the use of these technologies, cyberattacks are also on the rise because current security methods are ineffective. Several artificial intelligence (AI)-based security solutions have been presented in recent years, including intrusion detection systems (IDS). Feature selection (FS) approaches are required for the development of intelligent analytic tools that need data pretreatment and machine-learning algorithm-performance enhancement. By reducing the number of selected features, FS aims to improve classification accuracy. This article presents a new FS method through boosting the performance of Gorilla Troops Optimizer (GTO) based on the algorithm for bird swarms (BSA). This BSA is used to boost performance exploitation of GTO in the newly developed GTO-BSA because it has a strong ability to find feasible regions with optimal solutions. As a result, the quality of the final output will increase, improving convergence. GTO-BSA’s performance was evaluated using a variety of performance measures on four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 and BoT-IoT. The results were compared to those of the original GTO, BSA, and several state-of-the-art techniques in the literature. According to the findings of the experiments, GTO-BSA had a better convergence rate and higher-quality solutions.

Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture

COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network AlexNet as the backbone, and enhance it with the following two aspects: 1) adding batch normalization to help accelerate the training, reducing the internal covariance shift; 2) replacing the fully connected layer in AlexNet with three classifiers: SNN, ELM, and RVFL. Therefore, we have three novel models from the deep COVID network (DC-Net) framework, which are named DC-Net-S, DC-Net-E, and DC-Net-R, respectively. After comparison, we find the proposed DC-Net-R achieves an average accuracy of 90.91% on a private dataset (available upon email request) comprising of 296 images while the specificity reaches 96.13%, and has the best performance among all three proposed classifiers. In addition, we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11390-020-0679-8.

The generalized extreme learning machines: Tuning hyperparameters and limiting approach for the Moore-Penrose generalized inverse

In this paper, we propose the generalized extreme learning machine (GELM). GELM is an ELM that incorporates the analyzed hyperparameters of ELM, such as sizes and ranks of weight matrices, and a limiting approach for the Moore-Penrose generalized inverse (M-P GI) into the learning process. ELM overcomes shortcomings of traditional deep learning, such as time-consuming due to iterative executions, as it learns quickly by removing the adjustment time of hyperparameters. There are desirable numbers of hidden nodes in ELM for single hidden layer feedforward neural networks, minimizing prediction error. However, it is difficult to use the desired number because it is related to the number of data used and datasets tend to be large. We consider ELM for multiple hidden layer feedforward neural networks. We analyze matrices derived in the network and figure out the characteristics of weight matrices and biases considering accurate prediction and learning speed, based on mathematical theories and a limiting approach for the M-P GI. The final output matrix of GELM is formulated explicitly. Experiments are conducted to verify the analysis using network traffic data, including DDoS attacks. The performances of GLEM, such as accuracies and learning speed, are compared for the networks with single and multiple hidden layers. Numerical results show the advantages of GELM in the performance measures, and the use of multiple hidden layers in GELM does not significantly affect performance. The theory-based prediction performances obtained from GELM will be the criterion for the margin of deep learning performance.