Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiers

Enhanced intelligent water drops and cuckoo search algorithms for solving the capacitated vehicle routing problem

Arabic natural language processing: An overview

Intrusion detection system (IDS) is an essential cyber security tool which is used to detect abnormal activity on a network or a host A general approach towards designing IDS models is to use classifiers as detection units But a large feature space including noisy, redundant and irrelevant features often leads to low detection and high misclassification rates by the classifier To address this drawback, the process of selecting most relevant key features for classification is highly important The objective of this work is to optimize the process of feature selection in a way that improves the accuracy of the classifier This paper presents an IDS model wherein an intelligent water drops (IWD) algorithm-based feature selection method is proposed This method uses the IWD algorithm, a nature-inspired optimization algorithm for the feature subset selection along with support vector machine as a classifier for evaluation of the features selected The experiments are conducted using KDD CUP’99 dataset, and the performance is compared with earlier designs The experimental results show that the proposed model performs better in terms of higher detection rate, low false alarm rate and improved accuracy than the existing approaches

An IWD-based feature selection method for intrusion detection system

Energy efficiency and environmental issues have been largely neglected in logistics. In a traditional supply chain, the objective of improving energy efficiency is targeted at the level of single parts of the value making chain. Industry 4.0 technologies make it possible to build hyperconnected logistic solutions, where the objective of decreasing energy consumption and economic footprint is targeted at the global level. The problems of energy efficiency are especially relevant in first mile and last mile delivery logistics, where deliveries are composed of individual orders and each order must be picked up and delivered at different locations. Within the frame of this paper, the author describes a real-time scheduling optimization model focusing on energy efficiency of the operation. After a systematic literature review, this paper introduces a mathematical model of last mile delivery problems including scheduling and assignment problems. The objective of the model is to determine the optimal assignment and scheduling for each order so as to minimize energy consumption, which allows to improve energy efficiency. Next, a black hole optimization-based heuristic is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to increase energy efficiency in last mile logistics.

Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm.

A modified Intelligent Water Drops algorithm and its application to optimization problems

In this article; Intelligent Water Drops (IWD) algorithm is adapted for feature selection with Rough Set (RS). Specifically, IWD is used to search for a subset of features based on RS dependency as an evaluation function. The resulting system, called IWDRSFS (Intelligent Water Drops for Rough Set Feature Selection), is evaluated with six benchmark data sets. The performance of IWDRSFS are analysed and compared with those from other methods in the literature. The outcomes indicate that IWDRSFS is able to provide competitive and comparable results. In summary, this study shows that IWD is a useful method for undertaking feature selection problems with RS.

Intelligent water drops algorithm for rough set feature selection

The Internet Control Message Protocol version Six (ICMPv6) is categorized as the most important part of the Internet Protocol version Six (IPv6) due to its core functionalities. However, ICMPv6 protocol is vulnerable to different types of attacks such as Distributed Denial of Services (DDoS) attacks that are based on ICMPv6 messages. ICMPv6-based DDoS attacks are the most performed attacks against IPv6 networks and considered a grave problem of today Internet. Intrusion Detection Systems (IDSs) under different categories have been proposed to detect ICMPv6-based DDoS attacks. However, these IDSs are inefficient in detecting the attacks due to their limitations. The main limitation of the existing IDSs is the dependency on packet-based representation and features which are unsuitable for detecting DDoS attacks as experimentally proven. Therefore, this research proposes a new IDS, based on a flow-based representation of traffic, and a set of novel features for detecting the attacks. This is the first time a flow-based representation and features are proposed to detect ICMPv6-based DDoS attacks. Cross-validation and supplied set testing approaches have been applied to evaluate the proposed IDS using seven classifiers. The evaluation experiments were conducted based on real datasets and showed that the proposed flow-based IDS with the proposed novel features is efficient and reliable in detecting ICMPv6-based DDoS attacks with acceptable detection accuracies and false positive rates.

Flow-Based IDS for ICMPv6-Based DDoS Attacks Detection

In this paper, β-Hill Climbing algorithm, the recent local search-based meta-heuristic, are tailored for Sudoku puzzle. β-Hill Climbing algorithm is a new extended version of hill climbing algorithm which has the capability to escape the local optima using a stochastic operator called β-operator. The Sudoku puzzle is a popular game formulated as an optimization problem to come up with exact solution. Some Sudoku puzzle examples have been applied for evaluation process. The parameters of the β-Hill Climbing is also studied to show the best configuration used for this game. β-Hill Climbing in its best parameter configuration is able to find solution for Sudoku puzzle in 19 iterations and 2 seconds.

β-Hill Climbing Algorithm for Sudoku Game

Master River Multiple Creeks Intelligent Water Drops (MRMC-IWD) is an ensemble model of the intelligent water drop, whereby a divide-and-conquer strategy is utilized to improve the search process. In this paper, the potential of the MRMC-IWD using real-world optimization problems related to feature selection and classification tasks is assessed. An experimental study on a number of publicly available benchmark data sets and two real-world problems, namely human motion detection and motor fault detection, are conducted. Comparative studies pertaining to the features reduction and classification accuracies using different evaluation techniques (consistency-based, CFS, and FRFS) and classifiers (i.e., C4.5, VQNN, and SVM) are conducted. The results ascertain the effectiveness of the MRMC-IWD in improving the performance of the original IWD algorithm as well as undertaking real-world optimization problems.

Basem O. Alijla

Papers

A modified Intelligent Water Drops algorithm and its application to optimization problems

Intelligent water drops algorithm for rough set feature selection

Flow-Based IDS for ICMPv6-Based DDoS Attacks Detection

β-Hill Climbing Algorithm for Sudoku Game

An ensemble of intelligent water drop algorithm for feature selection optimization problem