Phishing attacks remain a pervasive threat in the cybersecurity landscape,necessitating intelligent and scalable detection mechanisms.This paper suggests a deep learning-based method for phishing URL identification us...Phishing attacks remain a pervasive threat in the cybersecurity landscape,necessitating intelligent and scalable detection mechanisms.This paper suggests a deep learning-based method for phishing URL identification using Convolutional Neural Networks(CNNs)on two benchmark datasets:the Phishing and PhishTank datasets.The CNN model eliminates the need for human feature engineering by automatically learning intricate,non-linear patterns from structured information.The Phishing dataset undergoes 5-fold cross-validation to guarantee robustness,and the results are contrasted with those of conventional classifiers like XGBoost and Logistic Regression.According to the results,the CNN routinely beats these baselines in terms of accuracy and F1-score.Notably,on the PhishTank dataset,the CNN achieves exceptional performance with over 99.3%accuracy,underscoring its effectiveness and generalizability.The experimental framework is implemented using TensorFlow in Python and validated on a standard computing setup.The findings reinforce CNN’s suitability for realtime,adaptive phishing detection in dynamic threat environments.展开更多
CONSPECTUS:Industrial emissions,agricultural runoff,and waste discharge have introduced numerous hazardous pollutants into ecosystems,including volatile organic compounds(VOCs),toxic gases(e.g.,SO2,NOx,and O3),heavy m...CONSPECTUS:Industrial emissions,agricultural runoff,and waste discharge have introduced numerous hazardous pollutants into ecosystems,including volatile organic compounds(VOCs),toxic gases(e.g.,SO2,NOx,and O3),heavy metal ions,and organic contaminants(e.g.,dyes,antibiotics).These pollutants pose significant risks to environmental sustainability and human health,contributing to respiratory illnesses,waterborne diseases,and environmental harm.To address these challenges,there is an urgent need for advanced materials that can efficiently and selectively capture and degrade pollutants.Metal−organic frameworks(MOFs),with their modular nature,precise architectures,and tunable functionalities,have attracted considerable attention for environmental remediation.Their structural diversity enables the incorporation of active sites such as open metal sites,functionalized ligands,and hierarchical pores,facilitating targeted interactions with a broad range of pollutants.Despite these advantages,the practical application of MOFs remains limited by their chemical instability under harsh environmental conditions(e.g.,extreme pH,oxidative or reductive atmospheres).Most MOFs are prone to degrade via ligand displacement or framework collapse,posing a significant barrier to their use in environmental remediation.This Account provides a comprehensive overview of our recent advances in the rational design and synthesis of chemically robust MOFs for the efficient capture,degradation,and detection of air and water pollution.First,we outline a combined strategy that integrates thermodynamic stabilization through strong metal−ligand coordination and kinetic enhancement via framework interpenetration and high connectivity,ensuring structural integrity under environmental conditions.Crystal engineering enables the incorporation of versatile binding sites,such as open metal sites and low-coordination nodes,while ligand design enhances electronic properties and luminescence response for selective detection.Additionally,precise control of the pore microenvironment improves molecular transport and pollutant binding efficiency.These synergistic approaches have been successfully demonstrated across a wide range of applications,including VOC adsorption and photocatalytic degradation,the removal of reactive,toxic gases(e.g.,O3,SO2,NH3),and the detection and remediation of organic contaminants,heavy metal ions,and radioactive species in water.Finally,we also discuss ongoing challenges and future directions essential for the practical application of stable MOFs in environmental remediation.This work aims to provide design principles and valuable insights that will advance the development of next-generation MOFs as sustainable platforms for comprehensive environmental pollution control.展开更多
文摘Phishing attacks remain a pervasive threat in the cybersecurity landscape,necessitating intelligent and scalable detection mechanisms.This paper suggests a deep learning-based method for phishing URL identification using Convolutional Neural Networks(CNNs)on two benchmark datasets:the Phishing and PhishTank datasets.The CNN model eliminates the need for human feature engineering by automatically learning intricate,non-linear patterns from structured information.The Phishing dataset undergoes 5-fold cross-validation to guarantee robustness,and the results are contrasted with those of conventional classifiers like XGBoost and Logistic Regression.According to the results,the CNN routinely beats these baselines in terms of accuracy and F1-score.Notably,on the PhishTank dataset,the CNN achieves exceptional performance with over 99.3%accuracy,underscoring its effectiveness and generalizability.The experimental framework is implemented using TensorFlow in Python and validated on a standard computing setup.The findings reinforce CNN’s suitability for realtime,adaptive phishing detection in dynamic threat environments.
基金support from the Beijing Natural Science Foundation(No.Z230023)National Natural Science Foundation of China(Nos.22225803 and 22038001)Beijing Outstanding Young Scientist Program(No.JWZQ20240102008).
文摘CONSPECTUS:Industrial emissions,agricultural runoff,and waste discharge have introduced numerous hazardous pollutants into ecosystems,including volatile organic compounds(VOCs),toxic gases(e.g.,SO2,NOx,and O3),heavy metal ions,and organic contaminants(e.g.,dyes,antibiotics).These pollutants pose significant risks to environmental sustainability and human health,contributing to respiratory illnesses,waterborne diseases,and environmental harm.To address these challenges,there is an urgent need for advanced materials that can efficiently and selectively capture and degrade pollutants.Metal−organic frameworks(MOFs),with their modular nature,precise architectures,and tunable functionalities,have attracted considerable attention for environmental remediation.Their structural diversity enables the incorporation of active sites such as open metal sites,functionalized ligands,and hierarchical pores,facilitating targeted interactions with a broad range of pollutants.Despite these advantages,the practical application of MOFs remains limited by their chemical instability under harsh environmental conditions(e.g.,extreme pH,oxidative or reductive atmospheres).Most MOFs are prone to degrade via ligand displacement or framework collapse,posing a significant barrier to their use in environmental remediation.This Account provides a comprehensive overview of our recent advances in the rational design and synthesis of chemically robust MOFs for the efficient capture,degradation,and detection of air and water pollution.First,we outline a combined strategy that integrates thermodynamic stabilization through strong metal−ligand coordination and kinetic enhancement via framework interpenetration and high connectivity,ensuring structural integrity under environmental conditions.Crystal engineering enables the incorporation of versatile binding sites,such as open metal sites and low-coordination nodes,while ligand design enhances electronic properties and luminescence response for selective detection.Additionally,precise control of the pore microenvironment improves molecular transport and pollutant binding efficiency.These synergistic approaches have been successfully demonstrated across a wide range of applications,including VOC adsorption and photocatalytic degradation,the removal of reactive,toxic gases(e.g.,O3,SO2,NH3),and the detection and remediation of organic contaminants,heavy metal ions,and radioactive species in water.Finally,we also discuss ongoing challenges and future directions essential for the practical application of stable MOFs in environmental remediation.This work aims to provide design principles and valuable insights that will advance the development of next-generation MOFs as sustainable platforms for comprehensive environmental pollution control.
