The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range comm...The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising alternative.Decentralized content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content requests.Several articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content replication.This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.展开更多
Telemarketing is a well-established marketing approach to offering products and services to prospective customers.The effectiveness of such an approach,however,is highly dependent on the selection of the appropriate c...Telemarketing is a well-established marketing approach to offering products and services to prospective customers.The effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested ones.The introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising results.However,the selection of influential features and the construction of effective learning models for improved performance remain a challenge.Furthermore,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate models.Additionally,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over time.This newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the literature.Extensive experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6%accuracy in classifying customers)and outperforms recent competing models in the literature by a considerable margin of 3%on a widely used dataset.展开更多
Internet security has become a major concern with the growing use of the Internet of Things(IoT)and edge computing technologies.Even though data processing is handled by the edge server,sensitive data is generated and...Internet security has become a major concern with the growing use of the Internet of Things(IoT)and edge computing technologies.Even though data processing is handled by the edge server,sensitive data is generated and stored by the IoT devices,which are subject to attack.Since most IoT devices have limited resources,standard security algorithms such as AES,DES,and RSA hamper their ability to run properly.In this paper,a lightweight symmetric key cipher termed randomized butterfly architecture of fast Fourier transform for key(RBFK)cipher is proposed for resource-constrained IoT devices in the edge computing environment.The butterfly architecture is used in the key scheduling system to produce strong round keys for five rounds of the encryption method.The RBFK cipher has two key sizes:64 and 128 bits,with a block size of 64 bits.The RBFK ciphers have a larger avalanche effect due to the butterfly architecture ensuring strong security.The proposed cipher satisfies the Shannon characteristics of confusion and diffusion.The memory usage and execution cycle of the RBFK cipher are assessed using the fair evaluation of the lightweight cryptographic systems(FELICS)tool.The proposed ciphers were also implemented using MATLAB 2021a to test key sensitivity by analyzing the histogram,correlation graph,and entropy of encrypted and decrypted images.Since the RBFK ciphers with minimal computational complexity provide better security than recently proposed competing ciphers,these are suitable for IoT devices in an edge computing environment.展开更多
Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions.Failure to prevent the intrusions could degrade the credibility of security services,e.g.da...Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions.Failure to prevent the intrusions could degrade the credibility of security services,e.g.data confidentiality,integrity,and availability.Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats,which can be broadly classified into Signature-based Intrusion Detection Systems(SIDS)and Anomaly-based Intrusion Detection Systems(AIDS).This survey paper presents a taxonomy of contemporary IDS,a comprehensive review of notable recent works,and an overview of the datasets commonly used for evaluation purposes.It also presents evasion techniques used by attackers to avoid detection and discusses future research challenges to counter such techniques so as to make computer systems more secure.展开更多
Profile hidden Markov models （HMMs） based on classical HMMs have been widely applied for protein sequence identification. The formulation of the forward and backward variables in profile HMMs is made under statistic...Profile hidden Markov models （HMMs） based on classical HMMs have been widely applied for protein sequence identification. The formulation of the forward and backward variables in profile HMMs is made under statistical independence assumption of the probability theory. We propose a fuzzy profile HMM to overcome the limitations of that assumption and to achieve an improved alignment for protein sequences belonging to a given family. The proposed model fuzzifies the forward and backward variables by incorporating Sugeno fuzzy measures and Choquet integrals, thus further extends the generalized HMM. Based on the fuzzified forward and backward variables, we propose a fuzzy Baum-Welch parameter estimation algorithm for profiles. The strong correlations and the sequence preference involved in the protein structures make this fuzzy architecture based model as a suitable candidate for building profiles of a given family, since the fuzzy set can handle uncertainties better than classical methods.展开更多
Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions.Failure to prevent the intrusions could degrade the credibility of security services,e.g.da...Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions.Failure to prevent the intrusions could degrade the credibility of security services,e.g.data confidentiality,integrity,and availability.Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats,which can be broadly classified into Signature-based Intrusion Detection Systems(SIDS)and Anomaly-based Intrusion Detection Systems(AIDS).This survey paper presents a taxonomy of contemporary IDS,a comprehensive review of notable recent works,and an overview of the datasets commonly used for evaluation purposes.It also presents evasion techniques used by attackers to avoid detection and discusses future research challenges to counter such techniques so as to make computer systems more secure.展开更多
文摘The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising alternative.Decentralized content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content requests.Several articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content replication.This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.
文摘Telemarketing is a well-established marketing approach to offering products and services to prospective customers.The effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested ones.The introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising results.However,the selection of influential features and the construction of effective learning models for improved performance remain a challenge.Furthermore,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate models.Additionally,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over time.This newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the literature.Extensive experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6%accuracy in classifying customers)and outperforms recent competing models in the literature by a considerable margin of 3%on a widely used dataset.
文摘Internet security has become a major concern with the growing use of the Internet of Things(IoT)and edge computing technologies.Even though data processing is handled by the edge server,sensitive data is generated and stored by the IoT devices,which are subject to attack.Since most IoT devices have limited resources,standard security algorithms such as AES,DES,and RSA hamper their ability to run properly.In this paper,a lightweight symmetric key cipher termed randomized butterfly architecture of fast Fourier transform for key(RBFK)cipher is proposed for resource-constrained IoT devices in the edge computing environment.The butterfly architecture is used in the key scheduling system to produce strong round keys for five rounds of the encryption method.The RBFK cipher has two key sizes:64 and 128 bits,with a block size of 64 bits.The RBFK ciphers have a larger avalanche effect due to the butterfly architecture ensuring strong security.The proposed cipher satisfies the Shannon characteristics of confusion and diffusion.The memory usage and execution cycle of the RBFK cipher are assessed using the fair evaluation of the lightweight cryptographic systems(FELICS)tool.The proposed ciphers were also implemented using MATLAB 2021a to test key sensitivity by analyzing the histogram,correlation graph,and entropy of encrypted and decrypted images.Since the RBFK ciphers with minimal computational complexity provide better security than recently proposed competing ciphers,these are suitable for IoT devices in an edge computing environment.
文摘Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions.Failure to prevent the intrusions could degrade the credibility of security services,e.g.data confidentiality,integrity,and availability.Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats,which can be broadly classified into Signature-based Intrusion Detection Systems(SIDS)and Anomaly-based Intrusion Detection Systems(AIDS).This survey paper presents a taxonomy of contemporary IDS,a comprehensive review of notable recent works,and an overview of the datasets commonly used for evaluation purposes.It also presents evasion techniques used by attackers to avoid detection and discusses future research challenges to counter such techniques so as to make computer systems more secure.
文摘Profile hidden Markov models （HMMs） based on classical HMMs have been widely applied for protein sequence identification. The formulation of the forward and backward variables in profile HMMs is made under statistical independence assumption of the probability theory. We propose a fuzzy profile HMM to overcome the limitations of that assumption and to achieve an improved alignment for protein sequences belonging to a given family. The proposed model fuzzifies the forward and backward variables by incorporating Sugeno fuzzy measures and Choquet integrals, thus further extends the generalized HMM. Based on the fuzzified forward and backward variables, we propose a fuzzy Baum-Welch parameter estimation algorithm for profiles. The strong correlations and the sequence preference involved in the protein structures make this fuzzy architecture based model as a suitable candidate for building profiles of a given family, since the fuzzy set can handle uncertainties better than classical methods.
基金carried out within the Internet Commerce Security Lab,which is funded by Westpac Banking Corporation.
文摘Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions.Failure to prevent the intrusions could degrade the credibility of security services,e.g.data confidentiality,integrity,and availability.Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats,which can be broadly classified into Signature-based Intrusion Detection Systems(SIDS)and Anomaly-based Intrusion Detection Systems(AIDS).This survey paper presents a taxonomy of contemporary IDS,a comprehensive review of notable recent works,and an overview of the datasets commonly used for evaluation purposes.It also presents evasion techniques used by attackers to avoid detection and discusses future research challenges to counter such techniques so as to make computer systems more secure.
