There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

The Internet Protocol.

Distributed Denial-of-Service (DDoS) attacks are increasing as the demand for Internet connectivity massively grows in recent years. Conventional shallow machine learning-based techniques for DDoS attack classification tend to be ineffective when the volume and features of network traffic, potentially carry malicious DDoS payloads, increase exponentially as they cannot extract high importance features automatically. To address this concern, we propose a hybrid approach named AE-MLP that combines two deep learning-based models for effective DDoS attack detection and classification. The Autoencoder (AE) part of our proposed model provides an effective feature extraction that finds the most relevant feature sets automatically without human intervention (e.g., knowledge of cybersecurity professionals). The Multi-layer Perceptron Network (MLP) part of our proposed model uses the compressed and reduced feature sets produced by the AE as inputs and classifies the attacks into different DDoS attack types to overcome the performance overhead and bias associated with processing large feature sets with noise (i.e., unnecessary feature values). Our experimental results, obtained through comprehensive and extensive experiments on different aspects of performance on the CICDDoS2019 dataset, demonstrate both a very high and robust accuracy rate and F1-score that exceed 98% which also outperformed the performance of many similar methods. This shows that our proposed model can be used as an effective DDoS defense tool against the growing number of DDoS attacks.

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AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification

STL-HDL: A new hybrid network intrusion detection system for imbalanced dataset on big data environment

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS) Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML–AIDS of networks and computers These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results

/pdf/benchmarking-of-machine-learning-for-anomaly-based-intrusion-3joht3lkcj.pdf

Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset

https://www.techscience.com/cmc/v69n3/44119/pdf

DeepIoT.IDS: Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

Several types of IPv6 transition mechanisms have been developed to facilitate the migration of IPv4 to the new protocol, IPv6. Although all transition mechanisms have the same objective, the process necessitates compliance with their respective capabilities. This paper focuses on the evaluation of the transition mechanisms namely 6to4 tunneling in terms of data transmission. The assessment is based upon experimental work that is conducted on a controlled environment. User-to-user network performance software is used to obtain the throughput, round trip time and tunneling overhead for TCP and UDP transmission protocol. The performance of TCP and UDP through 6to4 tunnel is then compared over the native IPv4 and IPv6 environment. As a result, the findings prove the ease of TCP data transmission via the tunnel compared to both native networks. In contrast, the UDP implementations show the slight difference for them.

Network performance evaluation of 6to4 tunneling

The potential of internet protocol version 6 (IPv6) cannot be denied as compared to the current network protocol (IPv4). The IPv6 ability which is offering huge amount of IP addresses has makes it being awaited by the Internet user. To make the IPv6 implementation successfully, a transition mechanism is needed to simplify the migration process between IPv4 to IPv6. The transition mechanism is created to support the joint operation between these protocols. However, after several years of implementation, it is believed that this mechanism has become an alternative route for intruders and attackers to penetrate the existing network. The problem kept remains without even being detected by any detection tools. Based on this problem, this study proposed an IPv6 network testbed for dealing with designation and implementation of Intrusion Detection System (IDS) on transition mechanism environment. All the equipments, tools and network are configured based on real process of transmitting IPv6 packets over IPv4 network. With fully functional operation for handling basic transition between IPv6 clients over IPv4 networks and equipped with intrusion detection tools, the testbed is used for investigating the intrusion activities behavior on the transition mechanism in the real environment. The result obtained from the testing phase shows the efficiency and the functionality of all hardware and software used. Moreover, the implementation of the testbed is expected to contribute to the realization of IPv6. Â© 2011 Asian Network for Scientific Information.

Implementation of IPv6 Network Testbed: Intrusion Detection System on Transition Mechanism

Economic and efficient energy dispatch management is compulsory to address the growth in energy demand within a limited energy resources whereas maintaining the secure power system operation. Many researches were conducted to study and develop new tools to overcome the problems during Economic Dispatch (ED) implementation. Mainly, ED problems considered on the total cost minimization at the same time the obligation of social attentions have inclined in reducing the energy conservation and pollution emission produced by power plants. As a result, a new algorithm was developed not only in minimizing the total generation cost but with an addition on minimum total emission and less system losses as the individual objective function in ED. The proposed optimization algorithm, namely New Meta-Heuristic Evolutionary Programming (NMEP) algorithm is followed to Meta-Heuristic Evolutionary Programming (Meta-EP) approach with some modification where the cloning process embedded as a significant progress during the implementation. This approach is utilized specifically to solve the single objective function which considered as minimum total generation cost, less sum of polluted emission and also a reduced amount in power system losses. The comparison evaluation between the original Meta-EP is conducted in order to show the effectiveness of the identified NMEP to overcome the ED issues. As a result, the best answer of the corresponding individual objective functions produced through NMEP approach. The simulation is tested on standard IEEE 26 bus system using the MATLAB software programming.

Nazrulazhar Bahaman

Papers

Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset

DeepIoT.IDS: Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

Network performance evaluation of 6to4 tunneling

Implementation of IPv6 Network Testbed: Intrusion Detection System on Transition Mechanism

A New Meta Heuristic Evolutionary Programming (NMEP) in Optimizing Economic Energy Dispatch