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
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.
