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The most pressing issue in network security is the establishment of an approach that is capable of detecting violations in computer systems and networks. There have been several efforts for improving it from various points of view. One example is the improvement of the classification of packets on the network, which is imperative in detecting abnormal traffic and hence any potential intrusion. Thus, this study proposes a new approach for intrusion detection that is implemented using an enhanced Bat algorithm (EBat) for training an artificial neural network. The goal of the current study is to increase the accuracy of the classification for malicious and un-malicious network traffic. The proposed study herein includes a comparison with nine other metaheuristic algorithms (conventional and new algorithms) that are used to evaluate the new approach alongside the related works. Firstly, the EBat algorithm was developed and used to select suitable weights and biases. Next, the neural network was employed using the found optimal weights and biases to realize the intrusion detection approach. Four types of intrusion detection evaluation datasets were used to compare the proposed approach against the other algorithms. The findings revealed that the proposed method outperformed the other nine classification algorithms and it is unparalleled for the network intrusion detection. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced honey encryption (HE) scheme for reinforcing the security of instant messaging systems and confounding the time and resources of malicious persons is presented. HE offers security beyond the brute-force bound by yielding plausible-looking but fake plaintext upon decryption with an incorrect key. Recent developments have seen the application of HE in the security of specific real-world systems, such as passwords and credit cards. However, applying the HE scheme to address other economic problems remains a daunting task as it requires modifying the HE algorithm to fit into the problem-in-view. For instance, applying the scheme for robust transmission of chat-messages upon decryption with an incorrect key will demand to generate contextually correct, valid-looking but fake chat-message which is indistinguishable from a human-generated message. This paper enhances the HE scheme by leveraging natural language processing techniques to build semantically plausible but fake chat-messages which will be served to the adversary during his attacks. Findings from evaluations reveal that the novel system is resilient to eavesdropping as an adversary is unable to distinguish decoy messages from the plaintext upon decryption with an incorrect key. [ABSTRACT FROM AUTHOR]

Arguably the most recurring issue concerning network security is building an approach that is capable of detecting intrusions into network systems. This issue has been addressed in numerous works using various approaches, of which the most popular one is to consider intrusions as anomalies with respect to the normal traffic in the network and classify network packets as either normal or abnormal. Improving the accuracy and efficiency of this classification is still an open problem to be solved. The study carried out in this article is based on a new approach for intrusion detection that is mainly implemented using the Hybrid Artificial Bee Colony algorithm (ABC) and Monarch Butterfly optimization (MBO). This approach is implemented for preparing an artificial neural system (ANN) in order to increase the precision degree of classification for malicious and non-malicious traffic in systems. The suggestion taken into consideration was to place side-by-side nine other metaheuristic algorithms that are used to evaluate the proposed approach alongside the related works. In the beginning the system is prepared in such a way that it selects the suitable biases and weights utilizing a hybrid (ABC) and (MBO). Subsequently the artificial neural network is retrained by using the information gained from the ideal weights and biases which are obtained from the hybrid algorithm (HAM) to get the intrusion detection approach able to identify new attacks. Three types of intrusion detection evaluation datasets namely KDD Cup 99, ISCX 2012, and UNSW-NB15 were used to compare and evaluate the proposed technique against the other algorithms. The experiment clearly demonstrated that the proposed technique provided significant enhancement compared to the other nine classification algorithms, and that it is more efficient with regards to network intrusion detection. [ABSTRACT FROM AUTHOR]

This article introduces a new variation of a known metaheuristic method for solving global optimization problems. The proposed algorithm is based on the Bat algorithm (BA), which is inspired by the micro-bat echolocation phenomenon, and addresses the problems of local-optima trapping using a special mutation operator that enhances the diversity of the standard BA, hence the name enhanced Bat algorithm (EBat). The design of EBat is introduced and its performance is evaluated against 24 of the standard benchmark functions, and compared to that of the standard BA, as well as to several well-established metaheuristic techniques. We also analyze the impact of different parameters on the EBat algorithm and determine the best combination of parameter values in the context of numerical optimization. The obtained results show that the new EBat method is indeed a promising addition to the arsenal of metaheuristic algorithms and can outperform several existing ones, including the original BA algorithm. [ABSTRACT FROM AUTHOR]

The objective of this article is twofold. On the one hand, we introduce a cognitively inspired hybridization metaheuristic that combines the strengths of two existing metaheuristics: the artificial bee colony (ABC) algorithm and the dragonfly algorithm (DA). The aim of this hybridization is to reduce the problems of slow convergence and trapping into local optima, by striking a good balance between global and local search components of the constituent algorithms. On the other hand, we use the proposed metaheuristic to train a multi-layer perceptron (MLP) as an alternative to existing traditional- and metaheuristic-based learning algorithms; this is for the purpose of improving overall accuracy by optimizing the set of MLP weights and biases. The proposed hybrid ABC/DA (HAD) algorithm comprises three main components: the static and dynamic swarming behavior phase in DA and two global search phases in ABC. The first one performs global search (DA phase), the second one performs local search (onlooker phase), and the third component implements global search (modified scout bee phase). The resultant metaheuristic optimizer is employed to train an MLP to reach a set of weights and biases that can yield high performance compared to traditional learning algorithms or even other metaheuristic optimizers. The proposed algorithm was first evaluated using 33 benchmark functions to test its performance in numerical optimization problems. Later, using HAD for training MLPs was evaluated against six standard classification datasets. In both cases, the performance of HAD was compared with the performance of several new and old metaheuristic methods from swarm intelligence and evolutionary computing. Experimental results show that HAD algorithm is clearly superior to the standard ABC and DA algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value, convergence speed, avoiding local minima and accuracy of trained MLPs. The proposed algorithm is a promising metaheuristic technique for general numerical optimization and for training MLPs. Specific applications and use cases are yet to be explored fully but they are supported by the encouraging results in this study. [ABSTRACT FROM AUTHOR]

The aim of the study was to propose a new metaheuristic algorithm that combines parts of the well-known artificial bee colony (ABC) optimization with elements from the recent monarch butterfly optimization (MBO) algorithm. The idea is to improve the balance between the characteristics of exploration and exploitation in those algorithms in order to address the issues of trapping in local optimal solution, slow convergence, and low accuracy in numerical optimization problems. This article introduces a new hybrid approach by modifying the butterfly adjusting operator in MBO algorithm and uses that as a mutation operator to replace employee phase of the ABC algorithm. The new algorithm is called Hybrid ABC/MBO (HAM). The HAM algorithm is basically employed to boost the exploration versus exploitation balance of the original algorithms, by increasing the diversity of the ABC search process using a modified operator from MBO algorithm. The resultant design contains three components: The first and third component implements global search, while the second one performs local search. The proposed algorithm was evaluated using 13 benchmark functions and compared with the performance of nine metaheuristic methods from swarm intelligence and evolutionary computing: ABC, MBO, ACO, PSO, GA, DE, ES, PBIL, and STUDGA. The experimental results show that the HAM algorithm is clearly superior to the standard ABC and MBO algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value and convergence speed. The proposed HAM algorithm is a promising metaheuristic technique to be added to the repertory of optimization techniques at the disposal of researchers. The next step is to look into application fields for HAM. [ABSTRACT FROM AUTHOR]

Bandwidth abuse is a critical Internet service violation. However, its origins are difficult to detect and trace given similarities between abusive and normal traffic. So far, there is no capable and scalable mechanism to deal with bandwidth abuse. This paper proposes a distributed edge-to-edge model for monitoring service level agreement (SLA) violations and tracing abusive traffic to its origins. The mechanism of policing misbehaving user traffic at a single random early detection (RED) gateway is used in the distributed monitoring of SLA violations, including violations carried out through several gateways. Each RED gateway reports misbehaving users who have been sent notifications of traffic policing to an SLA monitoring unit. Misbehaving users are considered suspicious users and their consumed bandwidth shares are aggregated at every gateway to be compared with SLA-specified ratios. Bandwidth is abused when SLA-specified ratios are exceeded. By reporting bandwidth abuse, illegitimate users can be isolated from legitimate ones and source hosts of abusive traffic may be traced. Approximate simulation results show that the proposed model can detect any SLA violation and identify abusive users. In addition, the proposed model can trace user violations back to their source machines in real time. [ABSTRACT FROM AUTHOR]

Image analysis is a branch of signal analysis that focuses on the extraction of meaningful information from images through digital image processing techniques. Convolution is a technique used to enhance specific characteristics of an image, while deconvolution is its inverse process. In this work, we focus on the deconvolution process, defining a new approach to retrieve filters applied in the convolution phase. Given an image I and a filtered image I ′ = f (I) , we propose three mathematical formulations that, starting from I and I ′ , are able to identify the filter f ′ that minimizes the mean absolute error between I ′ and f ′ (I) . Several tests were performed to investigate the applicability of our approaches in different scenarios. The results highlight that the proposed algorithms are able to identify the filter used in the convolution phase in several cases. Alternatively, the developed approaches can be used to verify whether a specific input image I can be transformed into a sample image I ′ through a convolution filter while returning the desired filter as output. [ABSTRACT FROM AUTHOR]

Lane–Emden differential equations describe different physical and astrophysical phenomena that include forms of stellar structure, isothermal gas spheres, gas spherical cloud thermal history, and thermionic currents. This paper presents a computational approach to solve the problems related to fractional Lane–Emden differential equations based on neural networks. Such a solution will help solve the fractional polytropic gas spheres problems which have different applications in physics, astrophysics, engineering, and several real-life issues. We used artificial neural network (ANN) framework in its feed-forward back-propagation learning scheme. The efficiency and accuracy of the presented algorithm are checked by testing it on four fractional Lane–Emden equations and compared with the exact solutions for the polytropic indices n = 0,1,5 and those of the series expansions for the polytropic index n = 3. The results we obtained prove that using the ANN method is feasible and accurate and may outperform other methods. [ABSTRACT FROM AUTHOR]

As an expanded classical 0-1 knapsack problem (0-1 KP), the discounted {0-1} knapsack problem (DKP) is proposed based on the concept of discount in the commercial world. The DKP contains a set of item groups where each group includes three items, whereas no more than one item in each group can be packed in the knapsack, which makes it more complex and challenging than 0-1 KP. At present, the main two algorithms for solving the DKP include exact algorithms and approximate algorithms. However, there are some topics which need to be further discussed, i.e., the improvement of the solution quality. In this paper, a novel multi-strategy monarch butterfly optimization (MMBO) algorithm for DKP is proposed. In MMBO, two effective strategies, neighborhood mutation with crowding and Gaussian perturbation, are introduced into MMBO. Experimental analyses show that the first strategy can enhance the global search ability, while the second strategy can strengthen local search ability and prevent premature convergence during the evolution process. Based on this, MBO is combined with each strategy, denoted as NCMBO and GMMBO, respectively. We compared MMBO with other six methods, including NCMBO, GMMBO, MBO, FirEGA, SecEGA and elephant herding optimization. The experimental results on three types of large-scale DKP instances show that NCMBO, GMMBO and MMBO are all suitable for solving DKP. In addition, MMBO outperforms other six methods and can achieve a good approximate solution with its approximation ratio close to 1 on almost all the DKP instances. [ABSTRACT FROM AUTHOR]