International Journal of Power Electronics and Drive Systems, Jun 1, 2023
To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optim... more To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optimized eventually. However, the optimized methods produce gratuitous performance overhead due to the limited exploration of advanced phishing cues. Thus, a phishing uniform resource locator-based predictive model is enhanced by this work to defeat this deficiency using deep learning algorithms. This model's architecture encompasses preprocessing of the effective feature space that is made up of 60 mutual uniform resource locator (URL) phishing features, and a dual deep learningbased model of convolution neural network with bi-directional long shortterm memory (CNN-BiLSTM). The proposed predictive model is trained and tested on a dataset of 14,000 phish URLs and 28,074 legitimate URLs. Experimentally, the performance outputs are remarked with a 0.01% false positive rate (FPR) and 99.27% testing accuracy.
Journal of theoretical and applied information technology, 2014
Phishing has become a substantial threat for internet users and a major cause of financial losses... more Phishing has become a substantial threat for internet users and a major cause of financial losses. In these attacks the cybercriminals carry out user credential information and users can fall victim. The current solution against phishing attacks are not sufficient to detect and work against novel phishes. This paper presents a systematic review of the previous and current research waves done on Internet phishing mitigation in different areas of expertise and highlighted phishing attacks types and some existing antiphishing approaches. Further the discussion about novel phishes and identify the elements of issues highlighted. The review can be valuable source of information to find and identify recent gap and challenges to fulfill the security flaws.
International Journal of Advanced Computer Science and Applications, 2015
Recently, limited anti-phishing campaigns have given phishers more possibilities to bypass throug... more Recently, limited anti-phishing campaigns have given phishers more possibilities to bypass through their advanced deceptions. Moreover, failure to devise appropriate classification techniques to effectively identify these deceptions has degraded the detection of phishing websites. Consequently, exploiting as new; few; predictive; and effective features as possible has emerged as a key challenge to keep the detection resilient. Thus, some prior works had been carried out to investigate and apply certain selected methods to develop their own classification techniques. However, no study had generally agreed on which feature selection method that could be employed as the best assistant to enhance the classification performance. Hence, this study empirically examined these methods and their effects on classification performance. Furthermore, it recommends some promoting criteria to assess their outcomes and offers contribution on the problem at hand. Hybrid features, low and high dimensional datasets, different feature selection methods, and classification models were examined in this study. As a result, the findings displayed notably improved detection precision with low latency, as well as noteworthy gains in robustness and prediction susceptibilities. Although selecting an ideal feature subset was a challenging task, the findings retrieved from this study had provided the most advantageous feature subset as possible for robust selection and effective classification in the phishing detection domain.
Phishing attack is new type of network attach and become a major nuisance on the internet. The ra... more Phishing attack is new type of network attach and become a major nuisance on the internet. The rapidly progressof phishing attacksthe present secure socket layer is not able to provide and protect users from fraudulent websites. The attacks have been targeted the non-cryptographic security protocols likeTransport layer security and secure socket layerprotocols. These protocols are not sufficient to tackle phishing attacks and need additional mechanisms. In this paper, we discussed phishing attacks and related weaknesses of Secure socket layer (SSL) protocol.Further we discuss and evaluate some proposed solutions against phishing and web spoofing attacks.
Advances in Intelligent Systems and Computing, 2018
Machine learning classifiers enriched the anti-phishing schemes with effective phishing classific... more Machine learning classifiers enriched the anti-phishing schemes with effective phishing classification models. However, they were constrained by their deficiency of inductive factors like learning on big and imbalanced data, deploying rich sets of features, and learning classifiers actively. That resulted in heavyweight phishing classifiers with massive misclassifications in real-time phishing detection. To diminish this deficiency, this paper proposed a new Phishing Hybrid Feature-Based Classifier (PHFBC) which hybridized two machine learning algorithms (Naïve Base) and (Decision Tree) with a statistical criterion of Phish Ratio. In conjunction, a Recursive Feature Subset Selection Algorithm (RFSSA) was also proposed to characterize phishing holistically with a robust selected subset of features. Outcomes of performance assessment via simulations, real-time validation, and comparative analysis demonstrated that PHFBC was highly distinctive among its competitors in terms of classification accuracy and minimal misclassification of novel phishes on the Web.
Phishing is a serious threat to the web economy and the Internet communication, because phishers ... more Phishing is a serious threat to the web economy and the Internet communication, because phishers put both users and organizations at risk of identity theft and financial losses. Phishers continually exploit new sophisticated features to impersonate legitimate web pages, modify their components and host their phishes. Furthermore, the prediction susceptibilities of features that were previously investigated become a key challenge for discriminating the evolving phishes. Accordingly, this paper investigated the prediction susceptibility of 58 hybrid features. It was observed that the investigated features were highly exploited in the content and hosted the URLs of phish webpages. The prediction susceptibility of the proposed features was experimentally examined in the suspected webpages using the SVM machine learning classification technique. The results revealed that the introduced features could be considered as potentially predictive ones and they could be utilized in the upcoming ...
Phishing Classification Models an Empirical Study of Induction Factors for Effective Classification
2017 Second Al-Sadiq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA), 2017
recently, researchers have devoted prominent machine learning-based anti-phishing models to survi... more recently, researchers have devoted prominent machine learning-based anti-phishing models to survive a supreme cyber-security versus phishing evolution on the cyberspace. Yet, such models remain incompetent to detect new phish in a real-time application. In this concern, this paper advocates an empirical analysis with the recently published works via a chronological validation. Chronological validation achieved by testing the works on three benchmarking data sets to appraise the causality between their detection outcomes and their limitations. Throughout chronological validation, the tested works have fallen short at detecting new phish web pages with an accessible detection accuracy. High to moderate faults and misclassifications are resulted as implications for their limitations and fixed real-time settings. Accordingly, this paper infers that by elevating the tested models in terms of using new and hybrid features, robust subset of features, and actively learned classifiers; an ad...
Journal of theoretical and applied information technology, 2015
Phishers usually evolve their web exploits to defeat current anti-phishing community. Accordingly... more Phishers usually evolve their web exploits to defeat current anti-phishing community. Accordingly, that becomes a serious web threat and puts both users and enterprises at the risks of identity theft and monetary losses day by day. In the literature, most computational efforts were dedicated to justify well-performed phishing detection against evolving phish exploits. However, facets like exploration of new and predictive features, selecting minimal and robust features compactness still raise as key challenges to optimize the detection scenarios over vast and strongly interrelated web. In this study, we proposed a set of new hybrid features, and refine it as few, maximum relevant, minimum redundant, and robust features as possible. In the presence of a machine learning classifier and some assessment criteria that recommended for this purpose, the reported results experimentally demonstrated that our remedial scenario could be used to optimize a phish detection model for any anti-phi...
Zero-day ransomware still threaten users’ and enterprises’ survival in the cyber-space by disturb... more Zero-day ransomware still threaten users’ and enterprises’ survival in the cyber-space by disturbing electronic amenities, damaging information systems, and causing data and money losses. The publically used anti-ransomware software are trying to mitigate this security issue, however they are limited at identifying zero-day ransomware variants effectively in the real-time without performance overhead. Thus, this paper proposed intelligent, real-time, and three-tier model of ransomware detection tool to be performed well for protecting windows-based information systems. The proposed ransomware detection tool comprises a hybrid machine learning algorithm which hybridizes the decisive functions of two topmost machine learning algorithms (Naïve Bays and Decision Tree) to holistically characterize and accurately classify zero-day ransomware variants in real-time application. Empirical, comparative and realistic assessments demonstrate the adaptability and effectiveness of the proposed ra...
Received Feb 20, 2021 Revised Jul 4, 2021 Accepted Jul 7, 2021 In the present time, dam managemen... more Received Feb 20, 2021 Revised Jul 4, 2021 Accepted Jul 7, 2021 In the present time, dam management is considered one of the important challenges for e-government in Iraq, becuase it needs information technology infrastructure, data integrity, and protection of user privacy against Internet threats that render such vital infrastructure ineffective. This struggle between the proposed dam management system (DMS) and a multi-tier secure model specifically for the Fallujah dam (and generally for all dams) which is addressed in this paper as a case study. To do this, a relational database design will discuss the development of a multi-tier secure model for integration of the dam management framework with its functions. This paper will discusse encryption and decryption of the dam data using the advanced encryption standard (AES) algorithm with derived keys via PBKDF2 and RNG sequences generator and Slave key for salting protection. The experimental results and analysis on the speed of enc...
Desktop and portable platform-based information systems become the most tempting target of crypto... more Desktop and portable platform-based information systems become the most tempting target of crypto and locker ransomware attacks during the last decades. Hence, researchers have developed anti-ransomware tools to assist the Windows platform at thwarting ransomware attacks, protecting the information, preserving the users’ privacy, and securing the inter-related information systems through the Internet. Furthermore, they utilized machine learning to devote useful anti-ransomware tools that detect sophisticated versions. However, such anti-ransomware tools remain sub-optimal in efficacy, partial to analyzing ransomware traits, inactive to learn significant and imbalanced data streams, limited to attributing the versions’ ancestor families, and indecisive about fusing the multi-descent versions. In this paper, we propose a hybrid machine learner model, which is a multi-tiered streaming analytics model that classifies various ransomware versions of 14 families by learning 24 static and d...
Phishers frequently craft novel deceptions on their websites and circumvent existing anti-phishin... more Phishers frequently craft novel deceptions on their websites and circumvent existing anti-phishing techniques for insecure intrusions, users’ digital identity theft, and then illegal profits. This raises the needs to incorporate new features for detecting novel phish websites and optimizing the existing anti-phishing techniques. In this light, 58 new hybrid features were proposed in this paper and their prediction susceptibilities were evaluated by using feature co-occurrence criterion and a baseline machine learning algorithm. Empirical test and analysis showed the significant outcomes of the proposed features on detection performance. As a result, the most influential features are identified, and new insights are offered for further detection improvement.
International Journal of Power Electronics and Drive Systems, Jun 1, 2023
To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optim... more To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optimized eventually. However, the optimized methods produce gratuitous performance overhead due to the limited exploration of advanced phishing cues. Thus, a phishing uniform resource locator-based predictive model is enhanced by this work to defeat this deficiency using deep learning algorithms. This model's architecture encompasses preprocessing of the effective feature space that is made up of 60 mutual uniform resource locator (URL) phishing features, and a dual deep learningbased model of convolution neural network with bi-directional long shortterm memory (CNN-BiLSTM). The proposed predictive model is trained and tested on a dataset of 14,000 phish URLs and 28,074 legitimate URLs. Experimentally, the performance outputs are remarked with a 0.01% false positive rate (FPR) and 99.27% testing accuracy.
Journal of theoretical and applied information technology, 2014
Phishing has become a substantial threat for internet users and a major cause of financial losses... more Phishing has become a substantial threat for internet users and a major cause of financial losses. In these attacks the cybercriminals carry out user credential information and users can fall victim. The current solution against phishing attacks are not sufficient to detect and work against novel phishes. This paper presents a systematic review of the previous and current research waves done on Internet phishing mitigation in different areas of expertise and highlighted phishing attacks types and some existing antiphishing approaches. Further the discussion about novel phishes and identify the elements of issues highlighted. The review can be valuable source of information to find and identify recent gap and challenges to fulfill the security flaws.
International Journal of Advanced Computer Science and Applications, 2015
Recently, limited anti-phishing campaigns have given phishers more possibilities to bypass throug... more Recently, limited anti-phishing campaigns have given phishers more possibilities to bypass through their advanced deceptions. Moreover, failure to devise appropriate classification techniques to effectively identify these deceptions has degraded the detection of phishing websites. Consequently, exploiting as new; few; predictive; and effective features as possible has emerged as a key challenge to keep the detection resilient. Thus, some prior works had been carried out to investigate and apply certain selected methods to develop their own classification techniques. However, no study had generally agreed on which feature selection method that could be employed as the best assistant to enhance the classification performance. Hence, this study empirically examined these methods and their effects on classification performance. Furthermore, it recommends some promoting criteria to assess their outcomes and offers contribution on the problem at hand. Hybrid features, low and high dimensional datasets, different feature selection methods, and classification models were examined in this study. As a result, the findings displayed notably improved detection precision with low latency, as well as noteworthy gains in robustness and prediction susceptibilities. Although selecting an ideal feature subset was a challenging task, the findings retrieved from this study had provided the most advantageous feature subset as possible for robust selection and effective classification in the phishing detection domain.
Phishing attack is new type of network attach and become a major nuisance on the internet. The ra... more Phishing attack is new type of network attach and become a major nuisance on the internet. The rapidly progressof phishing attacksthe present secure socket layer is not able to provide and protect users from fraudulent websites. The attacks have been targeted the non-cryptographic security protocols likeTransport layer security and secure socket layerprotocols. These protocols are not sufficient to tackle phishing attacks and need additional mechanisms. In this paper, we discussed phishing attacks and related weaknesses of Secure socket layer (SSL) protocol.Further we discuss and evaluate some proposed solutions against phishing and web spoofing attacks.
Advances in Intelligent Systems and Computing, 2018
Machine learning classifiers enriched the anti-phishing schemes with effective phishing classific... more Machine learning classifiers enriched the anti-phishing schemes with effective phishing classification models. However, they were constrained by their deficiency of inductive factors like learning on big and imbalanced data, deploying rich sets of features, and learning classifiers actively. That resulted in heavyweight phishing classifiers with massive misclassifications in real-time phishing detection. To diminish this deficiency, this paper proposed a new Phishing Hybrid Feature-Based Classifier (PHFBC) which hybridized two machine learning algorithms (Naïve Base) and (Decision Tree) with a statistical criterion of Phish Ratio. In conjunction, a Recursive Feature Subset Selection Algorithm (RFSSA) was also proposed to characterize phishing holistically with a robust selected subset of features. Outcomes of performance assessment via simulations, real-time validation, and comparative analysis demonstrated that PHFBC was highly distinctive among its competitors in terms of classification accuracy and minimal misclassification of novel phishes on the Web.
Phishing is a serious threat to the web economy and the Internet communication, because phishers ... more Phishing is a serious threat to the web economy and the Internet communication, because phishers put both users and organizations at risk of identity theft and financial losses. Phishers continually exploit new sophisticated features to impersonate legitimate web pages, modify their components and host their phishes. Furthermore, the prediction susceptibilities of features that were previously investigated become a key challenge for discriminating the evolving phishes. Accordingly, this paper investigated the prediction susceptibility of 58 hybrid features. It was observed that the investigated features were highly exploited in the content and hosted the URLs of phish webpages. The prediction susceptibility of the proposed features was experimentally examined in the suspected webpages using the SVM machine learning classification technique. The results revealed that the introduced features could be considered as potentially predictive ones and they could be utilized in the upcoming ...
Phishing Classification Models an Empirical Study of Induction Factors for Effective Classification
2017 Second Al-Sadiq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA), 2017
recently, researchers have devoted prominent machine learning-based anti-phishing models to survi... more recently, researchers have devoted prominent machine learning-based anti-phishing models to survive a supreme cyber-security versus phishing evolution on the cyberspace. Yet, such models remain incompetent to detect new phish in a real-time application. In this concern, this paper advocates an empirical analysis with the recently published works via a chronological validation. Chronological validation achieved by testing the works on three benchmarking data sets to appraise the causality between their detection outcomes and their limitations. Throughout chronological validation, the tested works have fallen short at detecting new phish web pages with an accessible detection accuracy. High to moderate faults and misclassifications are resulted as implications for their limitations and fixed real-time settings. Accordingly, this paper infers that by elevating the tested models in terms of using new and hybrid features, robust subset of features, and actively learned classifiers; an ad...
Journal of theoretical and applied information technology, 2015
Phishers usually evolve their web exploits to defeat current anti-phishing community. Accordingly... more Phishers usually evolve their web exploits to defeat current anti-phishing community. Accordingly, that becomes a serious web threat and puts both users and enterprises at the risks of identity theft and monetary losses day by day. In the literature, most computational efforts were dedicated to justify well-performed phishing detection against evolving phish exploits. However, facets like exploration of new and predictive features, selecting minimal and robust features compactness still raise as key challenges to optimize the detection scenarios over vast and strongly interrelated web. In this study, we proposed a set of new hybrid features, and refine it as few, maximum relevant, minimum redundant, and robust features as possible. In the presence of a machine learning classifier and some assessment criteria that recommended for this purpose, the reported results experimentally demonstrated that our remedial scenario could be used to optimize a phish detection model for any anti-phi...
Zero-day ransomware still threaten users’ and enterprises’ survival in the cyber-space by disturb... more Zero-day ransomware still threaten users’ and enterprises’ survival in the cyber-space by disturbing electronic amenities, damaging information systems, and causing data and money losses. The publically used anti-ransomware software are trying to mitigate this security issue, however they are limited at identifying zero-day ransomware variants effectively in the real-time without performance overhead. Thus, this paper proposed intelligent, real-time, and three-tier model of ransomware detection tool to be performed well for protecting windows-based information systems. The proposed ransomware detection tool comprises a hybrid machine learning algorithm which hybridizes the decisive functions of two topmost machine learning algorithms (Naïve Bays and Decision Tree) to holistically characterize and accurately classify zero-day ransomware variants in real-time application. Empirical, comparative and realistic assessments demonstrate the adaptability and effectiveness of the proposed ra...
Received Feb 20, 2021 Revised Jul 4, 2021 Accepted Jul 7, 2021 In the present time, dam managemen... more Received Feb 20, 2021 Revised Jul 4, 2021 Accepted Jul 7, 2021 In the present time, dam management is considered one of the important challenges for e-government in Iraq, becuase it needs information technology infrastructure, data integrity, and protection of user privacy against Internet threats that render such vital infrastructure ineffective. This struggle between the proposed dam management system (DMS) and a multi-tier secure model specifically for the Fallujah dam (and generally for all dams) which is addressed in this paper as a case study. To do this, a relational database design will discuss the development of a multi-tier secure model for integration of the dam management framework with its functions. This paper will discusse encryption and decryption of the dam data using the advanced encryption standard (AES) algorithm with derived keys via PBKDF2 and RNG sequences generator and Slave key for salting protection. The experimental results and analysis on the speed of enc...
Desktop and portable platform-based information systems become the most tempting target of crypto... more Desktop and portable platform-based information systems become the most tempting target of crypto and locker ransomware attacks during the last decades. Hence, researchers have developed anti-ransomware tools to assist the Windows platform at thwarting ransomware attacks, protecting the information, preserving the users’ privacy, and securing the inter-related information systems through the Internet. Furthermore, they utilized machine learning to devote useful anti-ransomware tools that detect sophisticated versions. However, such anti-ransomware tools remain sub-optimal in efficacy, partial to analyzing ransomware traits, inactive to learn significant and imbalanced data streams, limited to attributing the versions’ ancestor families, and indecisive about fusing the multi-descent versions. In this paper, we propose a hybrid machine learner model, which is a multi-tiered streaming analytics model that classifies various ransomware versions of 14 families by learning 24 static and d...
Phishers frequently craft novel deceptions on their websites and circumvent existing anti-phishin... more Phishers frequently craft novel deceptions on their websites and circumvent existing anti-phishing techniques for insecure intrusions, users’ digital identity theft, and then illegal profits. This raises the needs to incorporate new features for detecting novel phish websites and optimizing the existing anti-phishing techniques. In this light, 58 new hybrid features were proposed in this paper and their prediction susceptibilities were evaluated by using feature co-occurrence criterion and a baseline machine learning algorithm. Empirical test and analysis showed the significant outcomes of the proposed features on detection performance. As a result, the most influential features are identified, and new insights are offered for further detection improvement.
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