检索结果-南通市图书馆

Fake News Detection Using Machine Learning and Deep Learning Methods

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第11期77卷 2079-2096页

作者： Ammar Saeed Eesa Al Solami department of Computer Science Comsats University IslamabadWah Cantt47010Pakistan Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia

The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media platforms.Some social media sources contribute to the propagation of fake news that has no real validity,but they accumulate over time and begin to appear in the feed of every consumer producing even more ambiguity.To sustain the value of social media,such stories must be distinguished from the true ones.As a result,an automated system is required to save time and money.The classification of fake news and misinformation from social media data corpora is the subject of this research.Several preprocessing and data improvement procedures are used to gather and preprocess two fake news datasets.Deep text features are extracted using word embedding models Word2vec and Global Vectors for Word representation while textual features are extracted using n-gram approaches named Term Frequency-Inverse Document Frequency and Bag of Words from both datasets individually.Bidirectional Encoder Representations from Transformers(BERT)is also employed to derive embedded representations from the input data.Finally,three Machine Learning(ML)and two Deep Learning(DL)algorithms are utilized for fake news classification.BERT also carries out the classification of embedded outcomes generated by it in parallel with the ML and DL models.In terms of overall performance,the DL-based Convolutional Neural Network stands out in the case of the first while BERT performs better in the case of the second dataset.

关键词： Machine learning deep learning fake news feature extraction

MBB-IoT:Construction and Evaluation of IoT DDoS Traffic Dataset from a New Perspective

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第8期76卷 2095-2119页

作者： Yi Qing Xiangyu Liu Yanhui Du department of Artificial Intelligence People’s Public Security University of ChinaBeijing100038China Department of Cybersecurity People’s Public Security University of ChinaBeijing100038China

Distributed Denial of Service(DDoS)attacks have always been a major concern in the security field.With the release of malware source codes such as BASHLITE and Mirai,Internet of Things(IoT)devices have become the new source of DDoS attacks against many Internet applications.Although there are many datasets in the field of IoT intrusion detection,such as Bot-IoT,ConstrainedApplication Protocol–Denial of Service(CoAPDoS),and LATAM-DDoS-IoT(some of the names of DDoS datasets),which mainly focus on DDoS attacks,the datasets describing new IoT DDoS attack scenarios are extremely rare,and only N-BaIoT and IoT-23 datasets used IoT devices as DDoS attackers in the construction process,while they did not use Internet applications as victims either.To supplement the description of the new trend of DDoS attacks in the dataset,we built an IoT environment with mainstream DDoS attack tools such as Mirai and BASHLITE being used to infect IoT devices and implement DDoS attacks against WEB servers.Then,data aggregated into a dataset namedMBB-IoTwere captured atWEBservers and IoT nodes.After the MBB-IoT dataset was split into a training set and a test set,it was applied to the training and testing of the Random Forests classification algorithm.The multi-class classification metrics were good and all above 90%.Secondly,in a cross-evaluation experiment based on Support Vector Machine(SVM),Light Gradient Boosting Machine(LightGBM),and Long Short Term Memory networks(LSTM)classification algorithms,the training set and test set were derived from different datasets(MBB-IoT or IoT-23),and the test performance is better when MBB-IoT is used as the training set.

关键词： Intrusion detection IoT malware botnet DDoS dataset

A Novel Database Watermarking Technique Using Blockchain as Trusted Third Party

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第1期70卷 1585-1601页

作者： Ahmed S.Alghamdi Surayya Naz Ammar Saeed Eesa Al Solami Muhammad Kamran Mohammed Saeed Alkatheiri Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahKSA Abdul Wali Khan University MardanPakistan department of Computer Science COMSATS University IslamabadWah CampusWah CanttPakistan

With widespread use of relational database in various real-life applications,maintaining integrity and providing copyright protection is gaining keen interest of the researchers.For this purpose,watermarking has been used for quite a long time.Watermarking requires the role of trusted third party and a mechanism to extract digital signatures(watermark)to prove the ownership of the data under dispute.This is often inefficient as lots of processing is required.Moreover,certain malicious attacks,like additive attacks,can give rise to a situation when more than one parties can claim the ownership of the same data by inserting and detecting their own set of watermarks from the same data.To solve this problem,we propose to use blockchain technology—as trusted third party—along with watermarking for providing a means of rights protection of relational databases.Using blockchain for writing the copyright information alongside watermarking helps to secure the watermark as changing the blockchain is very difficult.This way,we combined the resilience of our watermarking scheme and the strength of blockchain technology—for protecting the digital rights information from alteration—to design and implement a robust scheme for digital right protection of relational databases.Moreover,we also discuss how the proposed scheme can also be used for version control.The proposed technique works with nonnumeric features of relational database and does not target only selected tuple or portion(subset)from the database for watermark embedding unlike most of the existing techniques;as a result,the chances of subset selection containing no watermark decrease automatically.The proposed technique employs zerowatermarking approach and hence no intentional error(watermark)is added to the original dataset.The results of the experiments proved the effectiveness of the proposed scheme.

关键词： Watermarking blockchain digital copyright protection relational databases security

A systematic review:Detecting phishing websites using data mining models

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent and Converged Networks 2023年第4期4卷 326-341页

作者： Dina Jibat Sarah Jamjoom Qasem Abu Al-Haija Abdallah Qusef the department of Business Intelligence Technology Princess Sumaya University for TechnologyAmman 11941Jordan the Department of Cybersecurity Princess Sumaya University for TechnologyAmman 11941Jordan the department of Software Engineering Princess Sumaya University for TechnologyAmman 11941Jordan

As internet technology use is on the rise globally,phishing constitutes a considerable share of the threats that may attack individuals and organizations,leading to significant losses from personal and confidential information to substantial financial losses.Thus,much research has been dedicated in recent years to developing effective and robust mechanisms to enhance the ability to trace illegitimate web pages and to distinguish them from non-phishing sites as accurately as possible.Aiming to conclude whether a universally accepted model can detect phishing attempts with 100%accuracy,we conduct a systematic review of research carried out in 2018-2021 published in well-known journals published by Elsevier,IEEE,Springer,and Emerald.Those researchers studied different Data Mining(DM)algorithms,some of which created a whole new model,while others compared the performance of several algorithms.Some studies combined two or more algorithms to enhance the detection performance.Results reveal that while most algorithms achieve accuracies higher than 90%,only some specific models can achieve 100%accurate results.

关键词： phishing data mining machine learning algorithm classification

Recent advances in mobile edge computing and content caching

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Digital Communications and Networks 2020年第2期6卷 189-194页

作者： Sunitha Safavat Naveen Naik Sapavath Danda B.Rawat Data Science and cybersecurity Center(DSC2) Department of Electrical Engineering and Computer ScienceHoward UniversityWashingtonDC20059USA

The demand for digital media services is increasing as the number of wireless subscriptions is growing exponentially.In order to meet this growing need,mobile wireless networks have been advanced at a tremendous pace over recent days.However,the centralized architecture of existing mobile networks,with limited capacity and range of bandwidth of the radio access network and low bandwidth back-haul network,can not handle the exponentially increasing mobile traffic.Recently,we have seen the growth of new mechanisms of data caching and delivery methods through intermediate caching servers.In this paper,we present a survey on recent advances in mobile edge computing and content caching,including caching insertion and expulsion policies,the behavior of the caching system,and caching optimization based on wireless networks.Some of the important open challenges in mobile edge computing with content caching are identified and discussed.We have also compared edge,fog and cloud computing in terms of delay.Readers of this paper will get a thorough understanding of recent advances in mobile edge computing and content caching in mobile wireless networks.

关键词： Mobile edge computing Content caching MEC

A channel state information based virtual MAC spoofing detector

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

High-Confidence Computing

High-Confidence Computing 2022年第3期2卷 1-6页

作者： Peng Jiang Hongyi Wu Chunsheng Xin department of ECE Old Dominion UniversityNorfolkVAUSA School of cybersecurity Old Dominion UniversityNorfolkVAUSA

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years.Due to limited authentication mechanisms,MAC spoofing attack,also known as the identity attack,threatens wireless systems.In this paper,we study a new type of MAC spoofing attack,the virtual MAC spoofing attack,in a tight environment with strong spatial similarities,which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services.We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure.A deep convolutional neural network is constructed to analyze signal level information extracted from Channel State Information(CSI)between the communication peers to provide additional authentication protection at the physical layer.A significant merit of the proposed detection system is that this system can distinguish two different devices even at the same location,which was not well addressed by the existing approaches.Our extensive experimental results demonstrate the effectiveness of the system with an average detection accuracy of 95%,even when devices are co-located.

关键词： Virtual Mac spoofing Channel state information Deep learning

Neural network based approach for time to crash prediction to cope with software aging

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2015年第2期26卷 407-414页

作者： Moona Yakhchi Javier Alonso Mahdi Fazeli Amir Akhavan Bitaraf Ahmad Patooghy School of Computer Science Institute for Research in Fundamental Sciences (IPM) Institute of Advanced Studies on cybersecurity University of Leon department of Computer Engineering Iran University of Technology

Recent studies have shown that software is one of the main reasons for computer systems unavailability. A growing ac- cumulation of software errors with time causes a phenomenon called software aging. This phenomenon can result in system per- formance degradation and eventually system hang/crash. To cope with software aging, software rejuvenation has been proposed. Software rejuvenation is a proactive technique which leads to re- moving the accumulated software errors by stopping the system, cleaning up its internal state, and resuming its normal operation. One of the main challenges of software rejuvenation is accurately predicting the time to crash due to aging factors such as me- mory leaks. In this paper, different machine learning techniques are compared to accurately predict the software time to crash un- der different aging scenarios. Finally, by comparing the accuracy of different techniques, it can be concluded that the multilayer per- ceptron neural network has the highest prediction accuracy among all techniques studied.

关键词： software reliability,software rejuvenation,machine learning

Cloud Datacenter Selection Using Service Broker Policies:A Survey

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第4期139卷 1-41页

作者： Salam Al-E’mari Yousef Sanjalawe Ahmad Al-Daraiseh Mohammad Bany Taha Mohammad Aladaileh department of Information Security Faculty of ITUniversity of PetraAmman11196Jordan Department of Cybersecurity School of ITAmerican University of Madaba(AUM)Amman11821Jordan department of Computer Science School of ITAmerican University of Madaba(AUM)Amman11821Jordan department of Data Science and Artificial Intelligence School of ITAmerican University of Madaba(AUM)Amman11821Jordan

Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.

关键词： Cloud computing cloud service broker datacenter selection quality-of-service user request

Cyberattack Ramifications, The Hidden Cost of a Security Breach

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Information Security 2024年第2期15卷 87-105页

作者： Meysam Tahmasebi Department of Cybersecurity EC-Council University Albuquerque USA

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

关键词： Artificial Intelligence (AI) Business Continuity Case Studies Copyright Cost-Benefit Analysis Credit Rating Cyberwarfare cybersecurity Breaches Data Breaches Denial Of Service (DOS) Devaluation Of Trade Name Disaster Recovery Distributed Denial of Service (DDOS) Identity Theft Increased Cost to Raise Debt Insurance Premium Intellectual Property Operational Disruption Patent Post-Breach Customer Protection Recovery Point Objective (RPO) Recovery Time Objective (RTO) Regulatory Compliance Risk Assessment Service Level Agreement Stuxnet Trade Secret