Cloud computing is one of the most attractive and cost-saving models,which provides online services to end-users.Cloud computing allows the user to access data directly from any node.But nowadays,cloud security is one...Cloud computing is one of the most attractive and cost-saving models,which provides online services to end-users.Cloud computing allows the user to access data directly from any node.But nowadays,cloud security is one of the biggest issues that arise.Different types of malware are wreaking havoc on the clouds.Attacks on the cloud server are happening from both internal and external sides.This paper has developed a tool to prevent the cloud server from spamming attacks.When an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)classification.Cloudflare will block those IP addresses that the attacker will use and prevent spamming attacks.However,the KNN classifiers will determine which area the spammer belongs to.At the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.展开更多
利用单颗粒气溶胶质谱仪(single particle aerosol mass spectrometer,SPAMS),采用ART-2a自适应神经网格分类法和后向轨迹模型,探究西安市夏季颗粒物的组分特征、粒径分布和潜在来源。结果表明:监测期间SPAMS采样得到的颗粒数和PM2.5日...利用单颗粒气溶胶质谱仪(single particle aerosol mass spectrometer,SPAMS),采用ART-2a自适应神经网格分类法和后向轨迹模型,探究西安市夏季颗粒物的组分特征、粒径分布和潜在来源。结果表明:监测期间SPAMS采样得到的颗粒数和PM2.5日平均浓度具有一定相关性(r=0.41,P<0.05)。根据颗粒物质谱特征相似度,将颗粒物分为6大类18小类:富钾类颗粒(K-EC、K-EC-SEC、K-NO_(3)-PO_(3)、K-NO_(3)-SiO_(3)、K-SEC,37.41%)、碳质颗粒(EC、EC-SEC、HEC、HEC-SEC、OC-SEC、ECOC-SEC、PAH-SEC,33.62%)、扬尘颗粒(Dust-HEC、Dust-SEC,13.23%)、富钠类颗粒(Na-Cl-NO_(3)、Na-SEC,3.47%)、重金属(HM,1.36%)及生物质(LEV,4.30%)。K-SEC、K-EC-SEC、ECOC-SEC颗粒数浓度在早晚交通高峰期出现峰值,主要由机动车尾气排放贡献;HEC-SEC、EC-SEC、Na-Cl-NO_(3)和Na-SEC颗粒数在午间出现峰值,发生非均相光化学反应加速其生成,HEC-SEC、EC-SEC粒径比HEC、EC较大;LEV颗粒数在夜间达到峰值,与生物质燃烧源和燃煤源有关。各气团轨迹中K-Rich和HM平均颗粒数占比分别最大(40.49%±2.43%)和最低(1.72%±0.61%)。来自重庆北部和河南西部的气团会带来高浓度的颗粒物污染,其中K-Rich和EC类颗粒含量较高,分别主要来自与生物质燃烧有关过程和一次污染源;来自蒙古国的气团轨迹颗粒数浓度较低,含有较多的OC颗粒,与其以燃煤和生物质燃烧为主要能源供应方式有关。SPAMS测定的颗粒数浓度可以反映当地细颗粒物污染状况,6类颗粒中绝大部分包含NO_(2)^(−)、NO_(3)^(−)、SO_(4)^(−)等二次离子组分,NO_(2)^(−)、NO_(3)^(−)由氮氧化物在大气中经过光化学反应和氧化反应与其他气态物质结合生成,SO_(4)^(−)由二氧化硫在大气中与氧气反应,并在气溶胶水滴中转化成硫酸盐,表明采集到的颗粒物大都经历了不同的老化,或与二次组分进行了不同程度的混合。西安市夏季颗粒物受本地排放和河南、内蒙古、新疆、重庆及湖南等地外来传输影响。展开更多
Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartph...Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartphones and increased Internet connectivity,SMS spam has emerged as a prevalent threat.Spammers have recognized the critical role SMS plays in today’s modern communication,making it a prime target for abuse.As cybersecurity threats continue to evolve,the volume of SMS spam has increased substantially in recent years.Moreover,the unstructured format of SMS data creates significant challenges for SMS spam detection,making it more difficult to successfully combat spam attacks.In this paper,we present an optimized and fine-tuned transformer-based Language Model to address the problem of SMS spam detection.We use a benchmark SMS spam dataset to analyze this spam detection model.Additionally,we utilize pre-processing techniques to obtain clean and noise-free data and address class imbalance problem by leveraging text augmentation techniques.The overall experiment showed that our optimized fine-tuned BERT(Bidirectional Encoder Representations from Transformers)variant model RoBERTa obtained high accuracy with 99.84%.To further enhance model transparency,we incorporate Explainable Artificial Intelligence(XAI)techniques that compute positive and negative coefficient scores,offering insight into the model’s decision-making process.Additionally,we evaluate the performance of traditional machine learning models as a baseline for comparison.This comprehensive analysis demonstrates the significant impact language models can have on addressing complex text-based challenges within the cybersecurity landscape.展开更多
Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable se...Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security risks.Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data breaches.To address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam emails.This framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual data.This approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated components.The model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the field.These findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection systems.The framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.展开更多
SMS spam poses a significant challenge to maintaining user privacy and security.Recently,spammers have employed fraudulent writing styles to bypass spam detection systems.This paper introduces a novel two-level detect...SMS spam poses a significant challenge to maintaining user privacy and security.Recently,spammers have employed fraudulent writing styles to bypass spam detection systems.This paper introduces a novel two-level detection system that utilizes deep learning techniques for effective spam identification to address the challenge of sophisticated SMS spam.The system comprises five steps,beginning with the preprocessing of SMS data.RoBERTa word embedding is then applied to convert text into a numerical format for deep learning analysis.Feature extraction is performed using a Convolutional Neural Network(CNN)for word-level analysis and a Bidirectional Long Short-Term Memory(BiLSTM)for sentence-level analysis.The two-level feature extraction enables a complete understanding of individual words and sentence structure.The novel part of the proposed approach is the Hierarchical Attention Network(HAN),which fuses and selects features at two levels through an attention mechanism.The HAN can deal with words and sentences to focus on the most pertinent aspects of messages for spam detection.This network is productive in capturing meaningful features,considering both word-level and sentence-level semantics.In the classification step,the model classifies the messages into spam and ham.This hybrid deep learning method improve the feature representation,and enhancing the model’s spam detection capabilities.By significantly reducing the incidence of SMS spam,our model contributes to a safer mobile communication environment,protecting users against potential phishing attacks and scams,and aiding in compliance with privacy and security regulations.This model’s performance was evaluated using the SMS Spam Collection Dataset from the UCI Machine Learning Repository.Cross-validation is employed to consider the dataset’s imbalanced nature,ensuring a reliable evaluation.The proposed model achieved a good accuracy of 99.48%,underscoring its efficiency in identifying SMS spam.展开更多
本研究利用单颗粒气溶胶质谱仪(SPAMS)于2021年1月1日至31日在广州市对大气中含铁颗粒的化学组成、混合状态、来源及其在污染过程中的演变特征进行了研究。结果表明,含铁颗粒主要分为Fe-BB、Fe-C、Fe-D、Fe-HM、Fe-N、Fe-S、Fe-SN等七...本研究利用单颗粒气溶胶质谱仪(SPAMS)于2021年1月1日至31日在广州市对大气中含铁颗粒的化学组成、混合状态、来源及其在污染过程中的演变特征进行了研究。结果表明,含铁颗粒主要分为Fe-BB、Fe-C、Fe-D、Fe-HM、Fe-N、Fe-S、Fe-SN等七种类型,以Fe-N、Fe-SN和Fe-HM三类颗粒为主,这三类颗粒在所有含铁颗粒中数量占比达85%。大部分含铁颗粒与二次无机离子尤其是硝酸盐混合,部分含铁颗粒与生物质燃烧源特征离子有机氮、元素碳、沙尘特征组分或重金属混合。在1月13~16日PM2.5重污染过程期间,发生了三次PM2.5突升,分别为污染前期、中期和后期。在污染前期和中期,PM2.5浓度升高受到来自工业排放和机动车尾气的一次排放的含铁颗粒的影响,在污染后期,受到含铁颗粒上硝酸盐生成的影响。To investigate the particle types, mixing states, sources and evolution during pollution of Fe-containing particles, a single particle aerosol mass spectrometer was used to perform an campaign in Guangzhou from January 1 to 31, 2021. The results showed that the Fe-containing particles were classified as Fe-BB, Fe-C, Fe-D, Fe-HM, Fe-N, Fe-S and Fe-SN, which Fe-N, Fe-SN and Fe-HM was the main types, accounted for 85% of all Fe-containing particles. Most of the Fe-containing particles were mixed with secondary inorganic ions, especially nitrate, and some were mixed with the characteristic ions of biomass combustion of organic nitrogen, elemental carbon, dust characteristic components or heavy metals. During the heavy PM2.5 pollution event from January 13 to 16, three spikes in PM2.5 concentrations occurred, which were divided into the early, middle and later stages of pollution. In the early and middle stages of pollution, the increase of PM2.5 concentration was influenced by Fe-containing particles from primary emissions of industrial and vehicle. In the later stages of pollution, the formation of nitrate was the main driver.展开更多
Web spamming是指故意误导搜索引擎的行为,它使得一些页面的排序值比它的应有值更高。最近几年,随着webspam的急剧增加,使得搜索引擎的搜索结果也降低了一些等级。文章首先讨论了Spam的基本概念和影响,然后详细地分析了当前的各种Spamm...Web spamming是指故意误导搜索引擎的行为,它使得一些页面的排序值比它的应有值更高。最近几年,随着webspam的急剧增加,使得搜索引擎的搜索结果也降低了一些等级。文章首先讨论了Spam的基本概念和影响,然后详细地分析了当前的各种Spamming技术,包括termspaming、link spamming和隐藏技术三种类型。我们相信本文的分析对于开发恰当的反措施是非常有用的。展开更多
文摘Cloud computing is one of the most attractive and cost-saving models,which provides online services to end-users.Cloud computing allows the user to access data directly from any node.But nowadays,cloud security is one of the biggest issues that arise.Different types of malware are wreaking havoc on the clouds.Attacks on the cloud server are happening from both internal and external sides.This paper has developed a tool to prevent the cloud server from spamming attacks.When an attacker attempts to use different spamming techniques on a cloud server,the attacker will be intercepted through two effective techniques:Cloudflare and K-nearest neighbors(KNN)classification.Cloudflare will block those IP addresses that the attacker will use and prevent spamming attacks.However,the KNN classifiers will determine which area the spammer belongs to.At the end of the article,various prevention techniques for securing cloud servers will be discussed,a comparison will be made with different papers,a conclusion will be drawn based on different results.
文摘利用单颗粒气溶胶质谱仪(single particle aerosol mass spectrometer,SPAMS),采用ART-2a自适应神经网格分类法和后向轨迹模型,探究西安市夏季颗粒物的组分特征、粒径分布和潜在来源。结果表明:监测期间SPAMS采样得到的颗粒数和PM2.5日平均浓度具有一定相关性(r=0.41,P<0.05)。根据颗粒物质谱特征相似度,将颗粒物分为6大类18小类:富钾类颗粒(K-EC、K-EC-SEC、K-NO_(3)-PO_(3)、K-NO_(3)-SiO_(3)、K-SEC,37.41%)、碳质颗粒(EC、EC-SEC、HEC、HEC-SEC、OC-SEC、ECOC-SEC、PAH-SEC,33.62%)、扬尘颗粒(Dust-HEC、Dust-SEC,13.23%)、富钠类颗粒(Na-Cl-NO_(3)、Na-SEC,3.47%)、重金属(HM,1.36%)及生物质(LEV,4.30%)。K-SEC、K-EC-SEC、ECOC-SEC颗粒数浓度在早晚交通高峰期出现峰值,主要由机动车尾气排放贡献;HEC-SEC、EC-SEC、Na-Cl-NO_(3)和Na-SEC颗粒数在午间出现峰值,发生非均相光化学反应加速其生成,HEC-SEC、EC-SEC粒径比HEC、EC较大;LEV颗粒数在夜间达到峰值,与生物质燃烧源和燃煤源有关。各气团轨迹中K-Rich和HM平均颗粒数占比分别最大(40.49%±2.43%)和最低(1.72%±0.61%)。来自重庆北部和河南西部的气团会带来高浓度的颗粒物污染,其中K-Rich和EC类颗粒含量较高,分别主要来自与生物质燃烧有关过程和一次污染源;来自蒙古国的气团轨迹颗粒数浓度较低,含有较多的OC颗粒,与其以燃煤和生物质燃烧为主要能源供应方式有关。SPAMS测定的颗粒数浓度可以反映当地细颗粒物污染状况,6类颗粒中绝大部分包含NO_(2)^(−)、NO_(3)^(−)、SO_(4)^(−)等二次离子组分,NO_(2)^(−)、NO_(3)^(−)由氮氧化物在大气中经过光化学反应和氧化反应与其他气态物质结合生成,SO_(4)^(−)由二氧化硫在大气中与氧气反应,并在气溶胶水滴中转化成硫酸盐,表明采集到的颗粒物大都经历了不同的老化,或与二次组分进行了不同程度的混合。西安市夏季颗粒物受本地排放和河南、内蒙古、新疆、重庆及湖南等地外来传输影响。
文摘Short Message Service(SMS)is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages-commonly known as SMS spam.With the rapid adoption of smartphones and increased Internet connectivity,SMS spam has emerged as a prevalent threat.Spammers have recognized the critical role SMS plays in today’s modern communication,making it a prime target for abuse.As cybersecurity threats continue to evolve,the volume of SMS spam has increased substantially in recent years.Moreover,the unstructured format of SMS data creates significant challenges for SMS spam detection,making it more difficult to successfully combat spam attacks.In this paper,we present an optimized and fine-tuned transformer-based Language Model to address the problem of SMS spam detection.We use a benchmark SMS spam dataset to analyze this spam detection model.Additionally,we utilize pre-processing techniques to obtain clean and noise-free data and address class imbalance problem by leveraging text augmentation techniques.The overall experiment showed that our optimized fine-tuned BERT(Bidirectional Encoder Representations from Transformers)variant model RoBERTa obtained high accuracy with 99.84%.To further enhance model transparency,we incorporate Explainable Artificial Intelligence(XAI)techniques that compute positive and negative coefficient scores,offering insight into the model’s decision-making process.Additionally,we evaluate the performance of traditional machine learning models as a baseline for comparison.This comprehensive analysis demonstrates the significant impact language models can have on addressing complex text-based challenges within the cybersecurity landscape.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:71-829-2024).
文摘Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security risks.Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data breaches.To address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam emails.This framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual data.This approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated components.The model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the field.These findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection systems.The framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.
文摘SMS spam poses a significant challenge to maintaining user privacy and security.Recently,spammers have employed fraudulent writing styles to bypass spam detection systems.This paper introduces a novel two-level detection system that utilizes deep learning techniques for effective spam identification to address the challenge of sophisticated SMS spam.The system comprises five steps,beginning with the preprocessing of SMS data.RoBERTa word embedding is then applied to convert text into a numerical format for deep learning analysis.Feature extraction is performed using a Convolutional Neural Network(CNN)for word-level analysis and a Bidirectional Long Short-Term Memory(BiLSTM)for sentence-level analysis.The two-level feature extraction enables a complete understanding of individual words and sentence structure.The novel part of the proposed approach is the Hierarchical Attention Network(HAN),which fuses and selects features at two levels through an attention mechanism.The HAN can deal with words and sentences to focus on the most pertinent aspects of messages for spam detection.This network is productive in capturing meaningful features,considering both word-level and sentence-level semantics.In the classification step,the model classifies the messages into spam and ham.This hybrid deep learning method improve the feature representation,and enhancing the model’s spam detection capabilities.By significantly reducing the incidence of SMS spam,our model contributes to a safer mobile communication environment,protecting users against potential phishing attacks and scams,and aiding in compliance with privacy and security regulations.This model’s performance was evaluated using the SMS Spam Collection Dataset from the UCI Machine Learning Repository.Cross-validation is employed to consider the dataset’s imbalanced nature,ensuring a reliable evaluation.The proposed model achieved a good accuracy of 99.48%,underscoring its efficiency in identifying SMS spam.
文摘本研究利用单颗粒气溶胶质谱仪(SPAMS)于2021年1月1日至31日在广州市对大气中含铁颗粒的化学组成、混合状态、来源及其在污染过程中的演变特征进行了研究。结果表明,含铁颗粒主要分为Fe-BB、Fe-C、Fe-D、Fe-HM、Fe-N、Fe-S、Fe-SN等七种类型,以Fe-N、Fe-SN和Fe-HM三类颗粒为主,这三类颗粒在所有含铁颗粒中数量占比达85%。大部分含铁颗粒与二次无机离子尤其是硝酸盐混合,部分含铁颗粒与生物质燃烧源特征离子有机氮、元素碳、沙尘特征组分或重金属混合。在1月13~16日PM2.5重污染过程期间,发生了三次PM2.5突升,分别为污染前期、中期和后期。在污染前期和中期,PM2.5浓度升高受到来自工业排放和机动车尾气的一次排放的含铁颗粒的影响,在污染后期,受到含铁颗粒上硝酸盐生成的影响。To investigate the particle types, mixing states, sources and evolution during pollution of Fe-containing particles, a single particle aerosol mass spectrometer was used to perform an campaign in Guangzhou from January 1 to 31, 2021. The results showed that the Fe-containing particles were classified as Fe-BB, Fe-C, Fe-D, Fe-HM, Fe-N, Fe-S and Fe-SN, which Fe-N, Fe-SN and Fe-HM was the main types, accounted for 85% of all Fe-containing particles. Most of the Fe-containing particles were mixed with secondary inorganic ions, especially nitrate, and some were mixed with the characteristic ions of biomass combustion of organic nitrogen, elemental carbon, dust characteristic components or heavy metals. During the heavy PM2.5 pollution event from January 13 to 16, three spikes in PM2.5 concentrations occurred, which were divided into the early, middle and later stages of pollution. In the early and middle stages of pollution, the increase of PM2.5 concentration was influenced by Fe-containing particles from primary emissions of industrial and vehicle. In the later stages of pollution, the formation of nitrate was the main driver.
