Machine Learning for Phishing Detection and Mitigation

Alauthman, Mohammad; Almomani, Ammar; Alweshah, Mohammed; Omoush, Waleed; Alieyan, Kamal

doi:10.1201/9780429504044-2

Cited by 17 publications

(8 citation statements)

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“…These activities compromise the integrity, availability, and confidentiality of systems and have a negative impact on the global economy [117,118]. A drastic increase in the amount of cybercrimes has initiated the application of machine learning techniques to provide solutions for early detection and prevention of such cybercrimes [43]. Machine learning techniques offer better results in cases that they are trained on diverse, massive, and real-time datasets.…”

Section: B Com M Only Used Security Datasetsmentioning

confidence: 99%

“…ML techniques are playing a vital role in numerous applications of the cyber security for early detection and prediction of different attacks such as spam classification [29][30][31][32], fraud detection [33][34][35][36], malware detection [37][38][39][40], phishing [41][42][43], darkweb or deepweb sites [44,45], and intrusion detection [46][47][48][49]. ML techniques can address the scarcity available of required personnel with expertise in these niche cybercrime detection technologies.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

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Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

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Section: B Com M Only Used Security Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

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“…The use of the optimization algorithm to solve real-world problems started in the early of this century [1,2]. A well-known sector of optimization algorithms called metaheuristic algorithms, which were able to solve different types of problems in different domains [3][4][5][6]. The reasons behind the usage of metaheuristic algorithms in different fields, as they were able to find near-optimal solutions with fast search speed, and its flexibility to suit different types of problems [7][8][9] which is very important in our modern life specially in software development [10].…”

Section: Introductionmentioning

confidence: 99%

Enhancing three variants of harmony search algorithm for continuous optimization problems

Alomoush

Zamli

Alrosan

et al. 2021

IJECE

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Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.

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“…In contrast, it has serious shortcomings such as sensitivity to noise and stuck in local optimum [7], [8]. In order to solve these problems, evolutionary algorithms and variants such as firefly algorithm (FA) [9], [10], particle swarm optimization (PSO) [11], cuckoo search (CS) [12], [13], and artificial bee colony (ABC) [14] and harmony search (HS) [15]- [17] have been successes in many domains such as segmentation of images [1], [2], [8], [18]- [20], clustering [21] and in several fields [22], [23]. In this paper, several studies have been reviewed, which are related to image segmentation methods based clustering approach with evolutionary algorithms, these studies are reviewed as follow, In Alrosan et al [20], proposed fuzzy c-means were combined with ABC algorithms and called ABC-FCM, and this method was carried out by two kinds of MRI images namely the simulated brain MRI images, Alrosan et al [8], [24] presented a novel version of ABC algorithm, namely (Mean WBC) that is merged with the FCM clustering approach.…”

Section: Introductionmentioning

confidence: 99%

Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

Alomoush

Alrosan

Almomani

et al. 2021

IJECE

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Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts.

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Machine Learning for Phishing Detection and Mitigation

Cited by 17 publications

References 0 publications

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

Enhancing three variants of harmony search algorithm for continuous optimization problems

Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

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