This document summarizes an article that analyzes existing anti-spam techniques including content-based, non-content based, and hybrid methods. It also proposes using the sender's name as a parameter for fuzzy string matching to cluster related spam domains. The key methods discussed are naïve Bayesian filtering, support vector machines, neural networks, domain blacklisting, and an existing hybrid method that clusters spam based on subject and IP address similarity. Experimental results on a large spam dataset show the sender's name can effectively be used as an additional parameter for fuzzy matching, identifying large clusters of related spam.
This document discusses techniques for detecting compromised machines ("zombies") that are involved in spamming activities on a network. It proposes using heuristic search and message partitioning/replication to minimize spam access from zombies while ensuring data confidentiality and integrity. Zombies are controlled by botnet herders and use various techniques to send large volumes of spam while remaining untraceable, such as exploiting vulnerabilities on Windows systems to use infected machines as mail relays or sending spam from dynamic IP addresses. The document analyzes spam sent from different IPs to examine the extent to which spam originates from a small number of hosts.
Detecting Spambot as an Antispam Technique for Web Internet BBSijsrd.com
Spam which is one of the most popular and also the most relevant topic that needs to be understood in the current scenario. Everyone whether it may be a small child or an old person are using emails everyday all around the world. The scenario which we are seeing is that almost no one is aware or in simple sentence they do not know what actually the spam is and what they will do in their systems. Spam in general means unsolicited or unwanted mails. Botnets are considered one of the main source of the spam. Botnet means the group of software's called bots and the function of these bots is to run on several compromised computers autonomously and automatically. The main objective of this paper is to detect such a bot or spambots for the Bulletin Board System (BBS). BBS is a computer that is running software that allows users to leave a message and access information of general interest. Originally BBSes were accessed only over a phone line using a modem, but nowadays some BBSes allowed access via a Telnet, packet switched network, or packet radio connection. The main methodology that we are going to focus is on Behavioural-based Spam Detection (BSD) method. Behavioral-based Spam Detector (BSD) combines several behaviours of the spam bots at different stages including the behaviour of spam preparation before the spam session when the spammers search for an open relay SMTP service to send e-mails through, and the behaviour of spammers while connecting to the mail server. Detecting the abnormal behaviour produced by the spam activities gives a high rate of suspicion on the existence of bots.
This document summarizes a research paper that develops an ant colony optimization (ACO) approach for filtering spam emails. It begins by noting the negative impacts of spam and how machine learning techniques have improved spam filtering over traditional rule-based methods. It then provides an overview of how ACO has been applied to data mining problems. The document proposes an ACO-based spam filtering model called AntSFilter and evaluates its performance on a public email dataset compared to other classifiers like Naive Bayes and Ripper. The preliminary results found AntSFilter yielded better accuracy with smaller rule sets, highlighting important features for identifying email categories.
AutoRE is a software developed by Microsoft to detect spam emails generated by botnets. It combines content-based and non-content-based detection methods. It first pre-processes URLs from emails, groups similar URLs into domains, and generates domain-agnostic regular expressions to identify patterns. This allows it to detect botnets even if they change domains. AutoRE's analysis of botnet characteristics informed future related work on real-time reputation systems and large-scale botnet detection using behavior analysis and IP address distribution. However, AutoRE itself was not fully implemented in real-time.
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of communication so are the attempts to misuse it to
take undue advantage of its low cost and high reachability. However, as email communication
is very cheap, spammers are taking advantage of it for advertising their products, for
committing cybercrimes. So, researchers are working hard to combat with the spammers. Many
spam detections techniques and systems are built to fight spammers. But the spammers are
continuously finding new ways to defeat the existing filters. This paper describes the existing
spam filters techniques and proposes a multi-level architecture for spam email detection. We
present the analysis of the architecture to prove the effectiveness of the architecture.
AN ANALYSIS OF EFFECTIVE ANTI SPAM PROTOCOL USING DECISION TREE CLASSIFIERSijsrd.com
As the internet usage increases in day to day activities, there is an inherent corresponding increase in usage of communication through it with email being the mainstay or rather in the forefront of modern day communication methodologies for businesses and general persons as well. This has led to get customer attention in the form of unwanted and unsolicited bombarding of the customers mail accounts with advertisements, offers, phishing activities, viruses, worms, trojans, generating hate crimes, making the customer to part with sensitive information like passwords, and other media as well which is known as spam. Spam is mass mailing or flooding of mail account servers with unwanted trash data causing damage some times. Spam filters have been in use from the time such mail flooding happens. Most of the spam filters are manual meaning which the user after identifying a mail in his account blocks the sender and henceforth the system will not allow mails to the inbox from such addresses. However the spammers are resilient and send spam mails from different identities and flood the inboxes. This study focuses on algorithms and data mining techniques used to unearth spam mails. They filter the inbox mails as they arrive at the server depending on certain rules which are already defined known as supervised learning methods. Such technologies are known as knowledge engineering techniques. Here a decision classifier is used to train such mails with varying words to filter and identify the words in the mail as spam. The Decision Tree model is used to analyze the mails and identify spam mails and block them. The number of mails sent, content, subject, type whether reply or forward, language etc. are identified using the decision classifier like Naves Bayes and analyzed accordingly to filter the emails.
Spam and Anti-spam - Sudipta Bhattacharyasankhadeep
The document discusses spam emails and anti-spam techniques. It defines spam emails, describes how spammers earn money and send spam emails. It also discusses the costs of spam emails, various types of spam like email spam, chat spam and search engine spam. The document then covers techniques used by individuals, email administrators and email senders to prevent spam emails. These include filtering, blocking, authentication and legal enforcement. The conclusion states that no single technique can fully solve the spam problem and both users and administrators need to use different anti-spam methods.
Spam exists in various forms of internet communication like email, instant messages, discussion boards and internet telephony. Email spam is the most common type and involves receiving unwanted advertisements and marketing emails. Spam grows as communication technologies evolve and spammers find new cheap ways to advertise. While spam can never be fully stopped, individuals can take steps like using strong passwords, email filters and antivirus software to reduce the amount of spam received. Other forms of spam include SPIM (spam over instant messages), SPIT (spam over internet telephony) and avoiding public exposure of contact details can help limit these.
This document discusses techniques for detecting compromised machines ("zombies") that are involved in spamming activities on a network. It proposes using heuristic search and message partitioning/replication to minimize spam access from zombies while ensuring data confidentiality and integrity. Zombies are controlled by botnet herders and use various techniques to send large volumes of spam while remaining untraceable, such as exploiting vulnerabilities on Windows systems to use infected machines as mail relays or sending spam from dynamic IP addresses. The document analyzes spam sent from different IPs to examine the extent to which spam originates from a small number of hosts.
Detecting Spambot as an Antispam Technique for Web Internet BBSijsrd.com
Spam which is one of the most popular and also the most relevant topic that needs to be understood in the current scenario. Everyone whether it may be a small child or an old person are using emails everyday all around the world. The scenario which we are seeing is that almost no one is aware or in simple sentence they do not know what actually the spam is and what they will do in their systems. Spam in general means unsolicited or unwanted mails. Botnets are considered one of the main source of the spam. Botnet means the group of software's called bots and the function of these bots is to run on several compromised computers autonomously and automatically. The main objective of this paper is to detect such a bot or spambots for the Bulletin Board System (BBS). BBS is a computer that is running software that allows users to leave a message and access information of general interest. Originally BBSes were accessed only over a phone line using a modem, but nowadays some BBSes allowed access via a Telnet, packet switched network, or packet radio connection. The main methodology that we are going to focus is on Behavioural-based Spam Detection (BSD) method. Behavioral-based Spam Detector (BSD) combines several behaviours of the spam bots at different stages including the behaviour of spam preparation before the spam session when the spammers search for an open relay SMTP service to send e-mails through, and the behaviour of spammers while connecting to the mail server. Detecting the abnormal behaviour produced by the spam activities gives a high rate of suspicion on the existence of bots.
This document summarizes a research paper that develops an ant colony optimization (ACO) approach for filtering spam emails. It begins by noting the negative impacts of spam and how machine learning techniques have improved spam filtering over traditional rule-based methods. It then provides an overview of how ACO has been applied to data mining problems. The document proposes an ACO-based spam filtering model called AntSFilter and evaluates its performance on a public email dataset compared to other classifiers like Naive Bayes and Ripper. The preliminary results found AntSFilter yielded better accuracy with smaller rule sets, highlighting important features for identifying email categories.
AutoRE is a software developed by Microsoft to detect spam emails generated by botnets. It combines content-based and non-content-based detection methods. It first pre-processes URLs from emails, groups similar URLs into domains, and generates domain-agnostic regular expressions to identify patterns. This allows it to detect botnets even if they change domains. AutoRE's analysis of botnet characteristics informed future related work on real-time reputation systems and large-scale botnet detection using behavior analysis and IP address distribution. However, AutoRE itself was not fully implemented in real-time.
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of communication so are the attempts to misuse it to
take undue advantage of its low cost and high reachability. However, as email communication
is very cheap, spammers are taking advantage of it for advertising their products, for
committing cybercrimes. So, researchers are working hard to combat with the spammers. Many
spam detections techniques and systems are built to fight spammers. But the spammers are
continuously finding new ways to defeat the existing filters. This paper describes the existing
spam filters techniques and proposes a multi-level architecture for spam email detection. We
present the analysis of the architecture to prove the effectiveness of the architecture.
AN ANALYSIS OF EFFECTIVE ANTI SPAM PROTOCOL USING DECISION TREE CLASSIFIERSijsrd.com
As the internet usage increases in day to day activities, there is an inherent corresponding increase in usage of communication through it with email being the mainstay or rather in the forefront of modern day communication methodologies for businesses and general persons as well. This has led to get customer attention in the form of unwanted and unsolicited bombarding of the customers mail accounts with advertisements, offers, phishing activities, viruses, worms, trojans, generating hate crimes, making the customer to part with sensitive information like passwords, and other media as well which is known as spam. Spam is mass mailing or flooding of mail account servers with unwanted trash data causing damage some times. Spam filters have been in use from the time such mail flooding happens. Most of the spam filters are manual meaning which the user after identifying a mail in his account blocks the sender and henceforth the system will not allow mails to the inbox from such addresses. However the spammers are resilient and send spam mails from different identities and flood the inboxes. This study focuses on algorithms and data mining techniques used to unearth spam mails. They filter the inbox mails as they arrive at the server depending on certain rules which are already defined known as supervised learning methods. Such technologies are known as knowledge engineering techniques. Here a decision classifier is used to train such mails with varying words to filter and identify the words in the mail as spam. The Decision Tree model is used to analyze the mails and identify spam mails and block them. The number of mails sent, content, subject, type whether reply or forward, language etc. are identified using the decision classifier like Naves Bayes and analyzed accordingly to filter the emails.
Spam and Anti-spam - Sudipta Bhattacharyasankhadeep
The document discusses spam emails and anti-spam techniques. It defines spam emails, describes how spammers earn money and send spam emails. It also discusses the costs of spam emails, various types of spam like email spam, chat spam and search engine spam. The document then covers techniques used by individuals, email administrators and email senders to prevent spam emails. These include filtering, blocking, authentication and legal enforcement. The conclusion states that no single technique can fully solve the spam problem and both users and administrators need to use different anti-spam methods.
Spam exists in various forms of internet communication like email, instant messages, discussion boards and internet telephony. Email spam is the most common type and involves receiving unwanted advertisements and marketing emails. Spam grows as communication technologies evolve and spammers find new cheap ways to advertise. While spam can never be fully stopped, individuals can take steps like using strong passwords, email filters and antivirus software to reduce the amount of spam received. Other forms of spam include SPIM (spam over instant messages), SPIT (spam over internet telephony) and avoiding public exposure of contact details can help limit these.
This document provides an overview of spam, including its history, statistics, types, solutions, and the law against spamming in India. It defines spam as unsolicited and unwanted social media posts or suspicious emails sent to many users. The first major spam incident began in 1994 via email. By 2009, English was the predominant language of spam, though spammers began translating spam to other languages. Common types of spam include email, social networking, mobile apps, and video sharing sites. Suggested solutions are self-management, whitelisting, blacklisting, and software/filters. Currently, India lacks legislation directly addressing spam regulation.
In an Osterman Research survey conducted during January 2011, decision makers and influencers demonstrated that they are decidedly pessimistic about the future of spam and malware problems for 2011.
This document discusses various techniques for filtering image spam in emails. It begins with introducing email spam and image spam, then describes types of image spam and spam content. It discusses the lifecycle of spam and various antispam techniques, including techniques that operate before spam is sent, after it is sent, and after it reaches mailboxes. It also covers existing techniques like analyzing spam characteristics, transmission protocols, local changes, language-based filters, non-content features, content-based classification, and hybrid filters. In the end, it emphasizes that hybrid techniques can effectively combine various filtering models.
This document summarizes spamming and spam filtering techniques. It discusses how spamming works by sending unsolicited messages from individual email accounts or open relay servers. It then outlines various spam filtering methods like blacklist, whitelist, content-based filters that analyze words or use heuristics. The document implements a simple spam sending program and shows how gmail and outlook spam filters work. It concludes by discussing the effectiveness of different filtering approaches and references further reading on minimizing spam effects.
This white paper provides a technical explanation of NDR Spam and recommend solutions that can prevent or limit exposure to this kind of unsolicited email.
Email spam, also known as junk email or unsolicited bulk email, is a subset of electronic spam that involves sending nearly identical unsolicited messages to numerous recipients. Spam has grown significantly since the early 1990s, with about 80% of spam now sent using botnets. Spammers collect email addresses from various sources and sites to send spam messages advertising products like pharmaceuticals. Fighting spam involves techniques like using email providers that utilize blacklists to block spam, protecting email addresses, and effectively reporting spam messages.
IRJET- Image Spam Detection: Problem and Existing SolutionIRJET Journal
This document summarizes the problem of image spam detection and existing solutions. It begins by defining image spam as spam where the message text is presented as a picture in an image file, allowing it to evade normal email filtering. It then discusses how image spam undermines current filters' ability to efficiently detect spam within images due to obfuscation techniques. Finally, it outlines the impact of spam, how spammers collect emails, and different types of spam threats like phishing, appending, and blank spam that pose risks to users' inboxes.
The document discusses spam filtering techniques. It defines spam as unsolicited bulk electronic messages, especially advertising. It describes different types of spam like email, comment, instant messaging, junk fax, and text messages. It then discusses current spam filtering works like Bayesian filtering models and other machine learning approaches. It proposes a collaborative intelligence approach to warn users of potential spam messages. Finally, it provides references on spam statistics and filtering techniques.
The document describes a hypothetical "Satan virus" that uses incentives and disincentives to convince users to willingly host malware on their systems. It outlines a design where the virus (1) tempts a user with access to another's private files in exchange for installing the virus, (2) monitors the user's activities to gather potentially incriminating information, and (3) threatens to expose this information if the user tries to remove the virus, ensuring the user's continued cooperation. It analyzes how such a virus could propagate by having infected users recruit new hosts under threat of blackmail. The document argues this approach forms a continuum between legitimate software and malware by manipulating user behavior rather than relying on technical exploits.
EMAIL SPAM CLASSIFICATION USING HYBRID APPROACH OF RBF NEURAL NETWORK AND PAR...IJNSA Journal
Email is one of the most popular communication media in the current century; it has become an effective
and fast method to share and information exchangeall over the world. In recent years, emails users are
facing problem which is spam emails. Spam emails are unsolicited, bulk emails are sent by spammers. It
consumes storage of mail servers, waste of time and consumes network bandwidth.Many methods used for
spam filtering to classify email messages into two groups spam and non-spam. In general, one of the most
powerful tools used for data classification is Artificial Neural Networks (ANNs); it has the capability of
dealing a huge amount of data with high dimensionality in better accuracy. One important type of ANNs is
the Radial Basis Function Neural Networks (RBFNN) that will be used in this work to classify spam
message. In this paper, we present a new approach of spam filtering technique which combinesRBFNN and
Particles Swarm Optimization (PSO) algorithm (HC-RBFPSO). The proposed approach uses PSO
algorithm to optimize the RBFNN parameters, depending on the evolutionary heuristic search process of
PSO. PSO use to optimize the best position of the RBFNN centers c. The Radii r optimize using K-Nearest
Neighbors algorithmand the weights w optimize using Singular Value Decomposition algorithm within
each iterative process of PSO depending the fitness (error) function. The experiments are conducted on
spam dataset namely SPAMBASE downloaded from UCI Machine Learning Repository. The experimental
results show that our approach is performed in accuracy compared with other approaches that use the
same dataset.
This document provides instructions for using various features of Yahoo Mail, including:
- Setting general preferences and adding a signature
- Managing drafts, sent messages, and folders
- Using auto-responds and sending email attachments
- Filtering mail and protecting against spam
- Importing and exporting contacts
- Switching to the Yahoo Mail beta version for additional features
Identifying Malicious Data in Social MediaIRJET Journal
This document discusses two approaches for identifying malicious data in social media: Shannon entropy and power law distribution. The Shannon entropy approach calculates the entropy of features like source/destination IP addresses and port numbers to detect anomalous network traffic patterns. The power law distribution approach models malware propagation across networks and finds that malware distribution transitions from exponential to power law over time. Experimental results on social media datasets found the Shannon entropy approach could detect malware based on the number of applications installed, while power law distribution identified good and malicious files shared between users. Both techniques aim to improve detection of malicious content shared over social networks.
Secure and Reliable Data Transmission in Generalized E-MailIJERA Editor
Email is a basic service for computer users, while email malware poses critical security threats. The technique of email-borne malware will be highly effective. Email malware focuses on modeling the propagation dynamics which is a fundamental technique for developing countermeasures to reduce email malware’s spreading speed and prevalence. Modern email malware exhibits two new features, reinjection and self-start. Reinjection is an infected user sends out malware copies whenever this user visits the malicious hyperlinks or attachments. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. For address this problem, to derive a novel difference equation based analytical model by introducing a new concept of virtual dirty user. Propose a new analytical model to enhanced OLSR protocol which is a trust based technique to secure the OLSR nodes against the attack. The proposed solution called EOLSR is an enhancement of the basic OLSR routing protocol, which will be able to detect the presence of malicious nodes in the network.
Prepare black list using bayesian approach to improve performance of spam fil...IAEME Publication
This document summarizes a research paper that proposes using a combination of origin-based and Bayesian filtering techniques to improve spam filter performance. It begins by introducing the problem of spam emails and limitations of current filtering methods. Then it describes an implementation that first classifies emails based on the sender's origin, using a blacklist, and then applies Bayesian classification to remaining emails. The results show this dual approach increases classification speed by reducing the number of emails Bayesian analysis must examine, while maintaining accuracy. In conclusion, combining origin-based and content-based filtering can create a faster and more effective spam filter.
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4. IEEE based on Image processing
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1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
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2. Ns2 project
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1) The document proposes a Bayesian algorithm approach to accurately detect spam emails. It aims to improve on existing near duplicate matching schemes that rely on user feedback databases.
2) A novel email abstraction scheme called SAG is introduced to represent emails for near duplicate matching in a way that captures similarities between spam emails while avoiding accidental deletion of legitimate emails.
3) The Bayesian filter is trained on the email abstractions generated by SAG to automatically classify subsequent emails as spam or legitimate. The approach aims to more effectively detect spam emails that evolve over time.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document discusses spam filtering techniques. It begins with an introduction to spam and defines it as unsolicited bulk email. It then discusses different types of spam filtering methods, including traditional methods, learning-based methods, and hybrid methods. The document also categorizes filtering methods based on where they are implemented, such as on the server-side, client-side, or in public mail servers. Overall, the document provides an overview of the spam phenomenon and a classification of various approaches to spam filtering.
This document provides an overview of spam, including its history, statistics, types, solutions, and the law against spamming in India. It defines spam as unsolicited and unwanted social media posts or suspicious emails sent to many users. The first major spam incident began in 1994 via email. By 2009, English was the predominant language of spam, though spammers began translating spam to other languages. Common types of spam include email, social networking, mobile apps, and video sharing sites. Suggested solutions are self-management, whitelisting, blacklisting, and software/filters. Currently, India lacks legislation directly addressing spam regulation.
In an Osterman Research survey conducted during January 2011, decision makers and influencers demonstrated that they are decidedly pessimistic about the future of spam and malware problems for 2011.
This document discusses various techniques for filtering image spam in emails. It begins with introducing email spam and image spam, then describes types of image spam and spam content. It discusses the lifecycle of spam and various antispam techniques, including techniques that operate before spam is sent, after it is sent, and after it reaches mailboxes. It also covers existing techniques like analyzing spam characteristics, transmission protocols, local changes, language-based filters, non-content features, content-based classification, and hybrid filters. In the end, it emphasizes that hybrid techniques can effectively combine various filtering models.
This document summarizes spamming and spam filtering techniques. It discusses how spamming works by sending unsolicited messages from individual email accounts or open relay servers. It then outlines various spam filtering methods like blacklist, whitelist, content-based filters that analyze words or use heuristics. The document implements a simple spam sending program and shows how gmail and outlook spam filters work. It concludes by discussing the effectiveness of different filtering approaches and references further reading on minimizing spam effects.
This white paper provides a technical explanation of NDR Spam and recommend solutions that can prevent or limit exposure to this kind of unsolicited email.
Email spam, also known as junk email or unsolicited bulk email, is a subset of electronic spam that involves sending nearly identical unsolicited messages to numerous recipients. Spam has grown significantly since the early 1990s, with about 80% of spam now sent using botnets. Spammers collect email addresses from various sources and sites to send spam messages advertising products like pharmaceuticals. Fighting spam involves techniques like using email providers that utilize blacklists to block spam, protecting email addresses, and effectively reporting spam messages.
IRJET- Image Spam Detection: Problem and Existing SolutionIRJET Journal
This document summarizes the problem of image spam detection and existing solutions. It begins by defining image spam as spam where the message text is presented as a picture in an image file, allowing it to evade normal email filtering. It then discusses how image spam undermines current filters' ability to efficiently detect spam within images due to obfuscation techniques. Finally, it outlines the impact of spam, how spammers collect emails, and different types of spam threats like phishing, appending, and blank spam that pose risks to users' inboxes.
The document discusses spam filtering techniques. It defines spam as unsolicited bulk electronic messages, especially advertising. It describes different types of spam like email, comment, instant messaging, junk fax, and text messages. It then discusses current spam filtering works like Bayesian filtering models and other machine learning approaches. It proposes a collaborative intelligence approach to warn users of potential spam messages. Finally, it provides references on spam statistics and filtering techniques.
The document describes a hypothetical "Satan virus" that uses incentives and disincentives to convince users to willingly host malware on their systems. It outlines a design where the virus (1) tempts a user with access to another's private files in exchange for installing the virus, (2) monitors the user's activities to gather potentially incriminating information, and (3) threatens to expose this information if the user tries to remove the virus, ensuring the user's continued cooperation. It analyzes how such a virus could propagate by having infected users recruit new hosts under threat of blackmail. The document argues this approach forms a continuum between legitimate software and malware by manipulating user behavior rather than relying on technical exploits.
EMAIL SPAM CLASSIFICATION USING HYBRID APPROACH OF RBF NEURAL NETWORK AND PAR...IJNSA Journal
Email is one of the most popular communication media in the current century; it has become an effective
and fast method to share and information exchangeall over the world. In recent years, emails users are
facing problem which is spam emails. Spam emails are unsolicited, bulk emails are sent by spammers. It
consumes storage of mail servers, waste of time and consumes network bandwidth.Many methods used for
spam filtering to classify email messages into two groups spam and non-spam. In general, one of the most
powerful tools used for data classification is Artificial Neural Networks (ANNs); it has the capability of
dealing a huge amount of data with high dimensionality in better accuracy. One important type of ANNs is
the Radial Basis Function Neural Networks (RBFNN) that will be used in this work to classify spam
message. In this paper, we present a new approach of spam filtering technique which combinesRBFNN and
Particles Swarm Optimization (PSO) algorithm (HC-RBFPSO). The proposed approach uses PSO
algorithm to optimize the RBFNN parameters, depending on the evolutionary heuristic search process of
PSO. PSO use to optimize the best position of the RBFNN centers c. The Radii r optimize using K-Nearest
Neighbors algorithmand the weights w optimize using Singular Value Decomposition algorithm within
each iterative process of PSO depending the fitness (error) function. The experiments are conducted on
spam dataset namely SPAMBASE downloaded from UCI Machine Learning Repository. The experimental
results show that our approach is performed in accuracy compared with other approaches that use the
same dataset.
This document provides instructions for using various features of Yahoo Mail, including:
- Setting general preferences and adding a signature
- Managing drafts, sent messages, and folders
- Using auto-responds and sending email attachments
- Filtering mail and protecting against spam
- Importing and exporting contacts
- Switching to the Yahoo Mail beta version for additional features
Identifying Malicious Data in Social MediaIRJET Journal
This document discusses two approaches for identifying malicious data in social media: Shannon entropy and power law distribution. The Shannon entropy approach calculates the entropy of features like source/destination IP addresses and port numbers to detect anomalous network traffic patterns. The power law distribution approach models malware propagation across networks and finds that malware distribution transitions from exponential to power law over time. Experimental results on social media datasets found the Shannon entropy approach could detect malware based on the number of applications installed, while power law distribution identified good and malicious files shared between users. Both techniques aim to improve detection of malicious content shared over social networks.
Secure and Reliable Data Transmission in Generalized E-MailIJERA Editor
Email is a basic service for computer users, while email malware poses critical security threats. The technique of email-borne malware will be highly effective. Email malware focuses on modeling the propagation dynamics which is a fundamental technique for developing countermeasures to reduce email malware’s spreading speed and prevalence. Modern email malware exhibits two new features, reinjection and self-start. Reinjection is an infected user sends out malware copies whenever this user visits the malicious hyperlinks or attachments. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. For address this problem, to derive a novel difference equation based analytical model by introducing a new concept of virtual dirty user. Propose a new analytical model to enhanced OLSR protocol which is a trust based technique to secure the OLSR nodes against the attack. The proposed solution called EOLSR is an enhancement of the basic OLSR routing protocol, which will be able to detect the presence of malicious nodes in the network.
Prepare black list using bayesian approach to improve performance of spam fil...IAEME Publication
This document summarizes a research paper that proposes using a combination of origin-based and Bayesian filtering techniques to improve spam filter performance. It begins by introducing the problem of spam emails and limitations of current filtering methods. Then it describes an implementation that first classifies emails based on the sender's origin, using a blacklist, and then applies Bayesian classification to remaining emails. The results show this dual approach increases classification speed by reducing the number of emails Bayesian analysis must examine, while maintaining accuracy. In conclusion, combining origin-based and content-based filtering can create a faster and more effective spam filter.
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
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1) The document proposes a Bayesian algorithm approach to accurately detect spam emails. It aims to improve on existing near duplicate matching schemes that rely on user feedback databases.
2) A novel email abstraction scheme called SAG is introduced to represent emails for near duplicate matching in a way that captures similarities between spam emails while avoiding accidental deletion of legitimate emails.
3) The Bayesian filter is trained on the email abstractions generated by SAG to automatically classify subsequent emails as spam or legitimate. The approach aims to more effectively detect spam emails that evolve over time.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document discusses spam filtering techniques. It begins with an introduction to spam and defines it as unsolicited bulk email. It then discusses different types of spam filtering methods, including traditional methods, learning-based methods, and hybrid methods. The document also categorizes filtering methods based on where they are implemented, such as on the server-side, client-side, or in public mail servers. Overall, the document provides an overview of the spam phenomenon and a classification of various approaches to spam filtering.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
This document summarizes a research paper on using correlation-based feature subset selection to improve spam detection accuracy when using a Bayesian classifier. The researchers introduce using feature subset selection to identify the most relevant features of spam emails while removing redundant features. This improves the accuracy of a naïve Bayesian classifier for spam detection from 65-74% to over 80%. It discusses how correlation-based feature subset selection works by selecting features highly correlated with the class (spam or not spam) but uncorrelated with each other. The researchers apply this method to a spam email dataset and achieve over 92% accuracy in spam detection using a Bayesian network classifier after feature subset selection, an improvement over using the classifier alone.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in
fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data,
incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard
challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier
with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant
features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
A review of spam filtering and measures of antispamAlexander Decker
This document discusses spam filtering and measures to reduce spam. It begins by defining spam as unsolicited email messages. It then discusses the problems caused by increasing spam volumes, such as becoming a security issue for businesses. Various types of spam are described, like text spam, image spam, and content-based spam. Methods for filtering spam are outlined, including collecting spam/anti-spam data, preprocessing to reduce noise, identifying bad senders, applying classification tools to specified content types, and storing results. Techniques for anti-spam filtering mentioned include white lists, rule-based filtering, content-based filtering, and pattern detection.
A Model for Fuzzy Logic Based Machine Learning Approach for Spam FilteringIOSR Journals
This document discusses machine learning techniques for spam filtering, including Naive Bayes, artificial neural networks, artificial immune systems, and fuzzy logic. It provides details on how Naive Bayes classification and artificial immune system classification work for spam filtering. Naive Bayes classification involves correlating words in emails with spam or non-spam and using Bayesian inference to calculate probabilities. Artificial immune system classification is inspired by the human immune system and involves gene libraries, negative selection, and clonal selection to recognize spam. The document aims to describe different machine learning approaches for automatic spam filtering.
Collateral Damage:
Consequences of Spam and Virus Filtering for the E-Mail S...Peter Eisentraut
This paper takes a critical look at the impact that contemporary spam
and virus filter techniques have on the stability, performance, and usability
of the e-mail system.
A framework to detect novel computer viruses via system callsUltraUploader
This document describes a framework for detecting email viruses based on system calls. It involves injecting DLLs to monitor and log system calls from an email client. The framework includes a training period where it is exposed to known viruses to derive malicious system calls, which are stored in a database. Normal email usage is also tested to identify unique virus-related system calls. This allows detection of new viruses based on abnormal system calls, without needing pre-existing virus signatures.
The document discusses spam filtering techniques. It begins by defining spam and its purposes. It then discusses the problems caused by spam and some statistics about its prevalence and costs. The document outlines federal regulations regarding spam and how spammers harvest email addresses. It describes different types of spam filters and how Bayesian filtering uses probabilities to classify emails as spam or not spam. The document discusses how data mining can be used for spam filtering and concludes that while no technique is perfect, data mining approaches show promise.
E-mail spam, also known as junk e-mail or unsolicited bulk e-mail (UBE), involves sending nearly identical unsolicited messages to numerous recipients by e-mail. Spam has grown significantly since the 1990s, with about 80% of spam now sent using networks of virus-infected computers or botnets. The legal status of spam varies by jurisdiction, with some places like the United States declaring spam legal if it meets certain specifications, while other areas have passed stronger anti-spam laws. Spam is a major problem, making up an estimated 78% of all e-mail, and is a multi-billion dollar cost to businesses each year.
E-mail spam, also known as junk e-mail or unsolicited bulk e-mail (UBE), involves sending nearly identical unsolicited messages to numerous recipients by e-mail. Spam has grown significantly since the 1990s, with about 80% sent using networks of virus-infected computers. The legal status of spam varies by jurisdiction, though in the US it is legal if it meets certain specifications under the CAN-SPAM Act of 2003. Spam now averages 78% of all email sent and costs businesses billions each year.
E-mail spam, also known as junk e-mail or unsolicited bulk e-mail (UBE), involves sending nearly identical unsolicited messages to numerous recipients by e-mail. Spam has grown significantly since the 1990s, with about 80% sent using networks of virus-infected computers. The legal status of spam varies by jurisdiction, though in the US it is legal if it meets certain specifications under the CAN-SPAM Act of 2003. Spam now averages 78% of all email sent and costs businesses billions each year.
E-mail spam, also known as junk e-mail or unsolicited bulk e-mail (UBE), involves sending nearly identical unsolicited messages to numerous recipients by e-mail. Spam has grown significantly since the 1990s, with about 80% of spam now sent using networks of virus-infected computers or "botnets." The legal status of spam varies by jurisdiction, with some places like the United States declaring spam legal if it meets certain specifications, while other areas have passed stronger anti-spam laws. Spam is a major problem, making up an estimated 78% of all e-mail, and is a multi-billion dollar problem for businesses due to lost productivity and infrastructure costs related
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a student project on building a spam classifier. It defines spam and the problems it causes. It then introduces the goal of building a tool to identify spam messages. It reviews literature on spamming and organized cybercrime. The proposed solution discusses features of a modern spam filter, including threat detection using AI and machine learning. It provides a block diagram of the spam classifier that includes collecting an email data set, pre-processing email content, extracting and selecting features, implementing a K-Nearest Neighbors algorithm, and analyzing performance.
Similar to Overview of Existing Methods of Spam Mining and Potential Usefulness of Sender’s Name as Parameter for Fuzzy String Matching (20)
Power System State Estimation - A ReviewIDES Editor
This document provides a review of power system state estimation techniques. It discusses both static and dynamic state estimation algorithms. For static state estimation, it covers weighted least squares, decoupled, and robust estimation methods. Weighted least squares is commonly used but can have numerical instability issues. Decoupled state estimation approximates the gain matrix for faster computation. Robust estimation uses M-estimators and other techniques to handle outliers and bad data. Dynamic state estimation applies Kalman filtering, leapfrog algorithms, and other methods to continuously monitor system states over time.
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
This document summarizes a research paper that proposes using artificial intelligence techniques and FACTS controllers for reactive power planning in real-time power transmission systems. The paper formulates the reactive power planning problem and incorporates flexible AC transmission system (FACTS) devices like static VAR compensators (SVC), thyristor controlled series capacitors (TCSC), and unified power flow controllers (UPFC). Evolutionary algorithms like evolutionary programming (EP) and differential evolution (DE) are applied to find the optimal locations and settings of the FACTS controllers to minimize losses and costs. Simulation results on IEEE 30-bus and 72-bus Indian test systems show that UPFC performs best in reducing losses compared to SVC and TCSC.
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...IDES Editor
Damping of power system oscillations with the help
of proposed optimal Proportional Integral Derivative Power
System Stabilizer (PID-PSS) and Static Var Compensator
(SVC)-based controllers are thoroughly investigated in this
paper. This study presents robust tuning of PID-PSS and
SVC-based controllers using Genetic Algorithms (GA) in
multi machine power systems by considering detailed model
of the generators (model 1.1). The effectiveness of FACTSbased
controllers in general and SVC-based controller in
particular depends upon their proper location. Modal
controllability and observability are used to locate SVC–based
controller. The performance of the proposed controllers is
compared with conventional lead-lag power system stabilizer
(CPSS) and demonstrated on 10 machines, 39 bus New England
test system. Simulation studies show that the proposed genetic
based PID-PSS with SVC based controller provides better
performance.
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
This paper presents the need to operate the power
system economically and with optimum levels of voltages has
further led to an increase in interest in Distributed
Generation. In order to reduce the power losses and to improve
the voltage in the distribution system, distributed generators
(DGs) are connected to load bus. To reduce the total power
losses in the system, the most important process is to identify
the proper location for fixing and sizing of DGs. It presents a
new methodology using a new population based meta heuristic
approach namely Artificial Bee Colony algorithm(ABC) for
the placement of Distributed Generators(DG) in the radial
distribution systems to reduce the real power losses and to
improve the voltage profile, voltage sag mitigation. The power
loss reduction is important factor for utility companies because
it is directly proportional to the company benefits in a
competitive electricity market, while reaching the better power
quality standards is too important as it has vital effect on
customer orientation. In this paper an ABC algorithm is
developed to gain these goals all together. In order to evaluate
sag mitigation capability of the proposed algorithm, voltage
in voltage sensitive buses is investigated. An existing 20KV
network has been chosen as test network and results are
compared with the proposed method in the radial distribution
system.
Line Losses in the 14-Bus Power System Network using UPFCIDES Editor
Controlling power flow in modern power systems
can be made more flexible by the use of recent developments
in power electronic and computing control technology. The
Unified Power Flow Controller (UPFC) is a Flexible AC
transmission system (FACTS) device that can control all the
three system variables namely line reactance, magnitude and
phase angle difference of voltage across the line. The UPFC
provides a promising means to control power flow in modern
power systems. Essentially the performance depends on proper
control setting achievable through a power flow analysis
program. This paper presents a reliable method to meet the
requirements by developing a Newton-Raphson based load
flow calculation through which control settings of UPFC can
be determined for the pre-specified power flow between the
lines. The proposed method keeps Newton-Raphson Load Flow
(NRLF) algorithm intact and needs (little modification in the
Jacobian matrix). A MATLAB program has been developed to
calculate the control settings of UPFC and the power flow
between the lines after the load flow is converged. Case studies
have been performed on IEEE 5-bus system and 14-bus system
to show that the proposed method is effective. These studies
indicate that the method maintains the basic NRLF properties
such as fast computational speed, high degree of accuracy and
good convergence rate.
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...IDES Editor
The size and shape of opening in dam causes the
stress concentration, it also causes the stress variation in the
rest of the dam cross section. The gravity method of the analysis
does not consider the size of opening and the elastic property
of dam material. Thus the objective of study is comprises of
the Finite Element Method which considers the size of
opening, elastic property of material, and stress distribution
because of geometric discontinuity in cross section of dam.
Stress concentration inside the dam increases with the opening
in dam which results in the failure of dam. Hence it is
necessary to analyses large opening inside the dam. By making
the percentage area of opening constant and varying size and
shape of opening the analysis is carried out. For this purpose
a section of Koyna Dam is considered. Dam is defined as a
plane strain element in FEM, based on geometry and loading
condition. Thus this available information specified our path
of approach to carry out 2D plane strain analysis. The results
obtained are then compared mutually to get most efficient
way of providing large opening in the gravity dam.
Assessing Uncertainty of Pushover Analysis to Geometric ModelingIDES Editor
Pushover Analysis a popular tool for seismic
performance evaluation of existing and new structures and is
nonlinear Static procedure where in monotonically increasing
loads are applied to the structure till the structure is unable
to resist the further load .During the analysis, whatever the
strength of concrete and steel is adopted for analysis of
structure may not be the same when real structure is
constructed and the pushover analysis results are very sensitive
to material model adopted, geometric model adopted, location
of plastic hinges and in general to procedure followed by the
analyzer. In this paper attempt has been made to assess
uncertainty in pushover analysis results by considering user
defined hinges and frame modeled as bare frame and frame
with slab modeled as rigid diaphragm and results compared
with experimental observations. Uncertain parameters
considered includes the strength of concrete, strength of steel
and cover to the reinforcement which are randomly generated
and incorporated into the analysis. The results are then
compared with experimental observations.
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...IDES Editor
This document summarizes and analyzes secure multi-party negotiation protocols for electronic payments in mobile computing. It presents a framework for secure multi-party decision protocols using lightweight implementations. The main focus is on synchronizing security features to avoid agreement manipulation and reduce user traffic. The paper describes negotiation between an auctioneer and bidders, showing multiparty security is better than existing systems. It analyzes the performance of encryption algorithms like ECC, XTR, and RSA for use in the multiparty negotiation protocols.
Selfish Node Isolation & Incentivation using Progressive ThresholdsIDES Editor
The problems associated with selfish nodes in
MANET are addressed by a collaborative watchdog approach
which reduces the detection time for selfish nodes thereby
improves the performance and accuracy of watchdogs[1]. In
the related works they make use of credit based systems, reputation
based mechanisms, pathrater and watchdog mechanism
to detect such selfish nodes. In this paper we follow an approach
of collaborative watchdog which reduces the detection
time for selfish nodes and also involves the removal of such
selfish nodes based on some progressively assessed thresholds.
The threshold gives the nodes a chance to stop misbehaving
before it is permanently deleted from the network.
The node passes through several isolation processes before it
is permanently removed. Another version of AODV protocol
is used here which allows the simulation of selfish nodes in
NS2 by adding or modifying log files in the protocol.
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...IDES Editor
Wireless sensor networks are networks having non
wired infrastructure and dynamic topology. In OSI model each
layer is prone to various attacks, which halts the performance
of a network .In this paper several attacks on four layers of
OSI model are discussed and security mechanism is described
to prevent attack in network layer i.e wormhole attack. In
Wormhole attack two or more malicious nodes makes a covert
channel which attracts the traffic towards itself by depicting a
low latency link and then start dropping and replaying packets
in the multi-path route. This paper proposes promiscuous mode
method to detect and isolate the malicious node during
wormhole attack by using Ad-hoc on demand distance vector
routing protocol (AODV) with omnidirectional antenna. The
methodology implemented notifies that the nodes which are
not participating in multi-path routing generates an alarm
message during delay and then detects and isolate the
malicious node from network. We also notice that not only
the same kind of attacks but also the same kind of
countermeasures can appear in multiple layer. For example,
misbehavior detection techniques can be applied to almost all
the layers we discussed.
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...IDES Editor
The recent advancements in the wireless technology
and their wide-spread deployment have made remarkable
enhancements in efficiency in the corporate and industrial
and Military sectors The increasing popularity and usage of
wireless technology is creating a need for more secure wireless
Ad hoc networks. This paper aims researched and developed
a new protocol that prevents wormhole attacks on a ad hoc
network. A few existing protocols detect wormhole attacks but
they require highly specialized equipment not found on most
wireless devices. This paper aims to develop a defense against
wormhole attacks as an Anti-worm protocol which is based on
responsive parameters, that does not require as a significant
amount of specialized equipment, trick clock synchronization,
no GPS dependencies.
Cloud Security and Data Integrity with Client Accountability FrameworkIDES Editor
This document summarizes a proposed cloud security and data integrity framework that provides client accountability. The framework aims to address issues like lack of user control over cloud data, need for data transparency and tracking, and ensuring data integrity. It proposes using JAR (Java Archive) files for data sharing due to benefits like portability. The framework incorporates client-side verification using MD5 hashing, digital signature-based authentication of JAR files, and use of HMAC to ensure data integrity. It also uses password-based encryption of log files to keep them tamper-proof. The framework is intended to provide both accountability and security for data sharing in cloud environments.
Genetic Algorithm based Layered Detection and Defense of HTTP BotnetIDES Editor
A System state in HTTP botnet uses HTTP protocol
for the creation of chain of Botnets thereby compromising
other systems. By using HTTP protocol and port number 80,
attacks can not only be hidden but also pass through the
firewall without being detected. The DPR based detection
leads to better analysis of botnet attacks [3]. However, it
provides only probabilistic detection of the attacker and also
time consuming and error prone. This paper proposes a Genetic
algorithm based layered approach for detecting as well as
preventing botnet attacks. The paper reviews p2p firewall
implementation which forms the basis of filtering.
Performance evaluation is done based on precision, F-value
and probability. Layered approach reduces the computation
and overall time requirement [7]. Genetic algorithm promises
a low false positive rate.
Enhancing Data Storage Security in Cloud Computing Through SteganographyIDES Editor
This document summarizes a research paper that proposes a method for enhancing data security in cloud computing through steganography. The method hides user data in digital images stored on cloud servers. When data needs to be accessed, it is extracted from the images. The document outlines the cloud architecture and security issues addressed. It then describes the proposed system architecture, security model, and data storage and retrieval process. Data is partitioned and hidden in multiple images to improve security. The goal is to prevent unauthorized access to user data stored on cloud servers.
The main tasks of a Wireless Sensor Network
(WSN) are data collection from its nodes and communication
of this data to the base station (BS). The protocols used for
communication among the WSN nodes and between the WSN
and the BS, must consider the resource constraints of nodes,
battery energy, computational capabilities and memory. The
WSN applications involve unattended operation of the network
over an extended period of time. In order to extend the lifetime
of a WSN, efficient routing protocols need to be adopted. The
proposed low power routing protocol based on tree-based
network structure reliably forwards the measured data towards
the BS using TDMA. An energy consumption analysis of the
WSN making use of this protocol is also carried out. It is
found that the network is energy efficient with an average
duty cycle of 0:7% for the WSN nodes. The OmNET++
simulation platform along with MiXiM framework is made
use of.
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...IDES Editor
The security of authentication of internet based
co-banking services should not be susceptible to high risks.
The passwords are highly vulnerable to virus attacks due to
the lack of high end embedding of security methods. In order
for the passwords to be more secure, people are generally
compelled to select jumbled up character based passwords
which are not only less memorable but are also equally prone
to insecurity. Multiple use of distributed shares has been
studied to solve the problem of authentication by algorithms
based on thresholding of pixels in image processing and visual
cryptography concepts where the subset of shares is considered
for the recovery of the original image for authentication using
correlation function[1][2].The main disadvantage in the above
study is the plain storage of shares and also one of the shares
is being supplied to the customer, which will lead to the
possibility of misuse by a third party. This paper proposes a
technique for scrambling of pixels by key based random
permutation (KBRP) within the shares before the
authentication has been attempted. Total number of shares to
be created is dependent on the multiplicity of ownership of
the account. By this method the problem of uncertainty among
the customers with regard to security, storage, retrieval of
holding of half of the shares is minimized.
This paper presents a trifocal Rotman Lens Design
approach. The effects of focal ratio and element spacing on
the performance of Rotman Lens are described. A three beam
prototype feeding 4 element antenna array working in L-band
has been simulated using RLD v1.7 software. Simulated
results show that the simulated lens has a return loss of –
12.4dB at 1.8GHz. Beam to array port phase error variation
with change in the focal ratio and element spacing has also
been investigated.
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral ImagesIDES Editor
Hyperspectral images can be efficiently compressed
through a linear predictive model, as for example the one
used in the SLSQ algorithm. In this paper we exploit this
predictive model on the AVIRIS images by individuating,
through an off-line approach, a common subset of bands, which
are not spectrally related with any other bands. These bands
are not useful as prediction reference for the SLSQ 3-D
predictive model and we need to encode them via other
prediction strategies which consider only spatial correlation.
We have obtained this subset by clustering the AVIRIS bands
via the clustering by compression approach. The main result
of this paper is the list of the bands, not related with the
others, for AVIRIS images. The clustering trees obtained for
AVIRIS and the relationship among bands they depict is also
an interesting starting point for future research.
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...IDES Editor
A microelectronic circuit of block-elements
functionally analogous to two hydrogen bonding networks is
investigated. The hydrogen bonding networks are extracted
from â-lactamase protein and are formed in its active site.
Each hydrogen bond of the network is described in equivalent
electrical circuit by three or four-terminal block-element.
Each block-element is coded in Matlab. Static and dynamic
analyses are performed. The resultant microelectronic circuit
analogous to the hydrogen bonding network operates as
current mirror, sine pulse source, triangular pulse source as
well as signal modulator.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
"NATO Hackathon Winner: AI-Powered Drug Search", Taras KlobaFwdays
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
What is an RPA CoE? Session 2 – CoE RolesDianaGray10
In this session, we will review the players involved in the CoE and how each role impacts opportunities.
Topics covered:
• What roles are essential?
• What place in the automation journey does each role play?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
"What does it really mean for your system to be available, or how to define w...Fwdays
We will talk about system monitoring from a few different angles. We will start by covering the basics, then discuss SLOs, how to define them, and why understanding the business well is crucial for success in this exercise.
MySQL InnoDB Storage Engine: Deep Dive - MydbopsMydbops
This presentation, titled "MySQL - InnoDB" and delivered by Mayank Prasad at the Mydbops Open Source Database Meetup 16 on June 8th, 2024, covers dynamic configuration of REDO logs and instant ADD/DROP columns in InnoDB.
This presentation dives deep into the world of InnoDB, exploring two ground-breaking features introduced in MySQL 8.0:
• Dynamic Configuration of REDO Logs: Enhance your database's performance and flexibility with on-the-fly adjustments to REDO log capacity. Unleash the power of the snake metaphor to visualize how InnoDB manages REDO log files.
• Instant ADD/DROP Columns: Say goodbye to costly table rebuilds! This presentation unveils how InnoDB now enables seamless addition and removal of columns without compromising data integrity or incurring downtime.
Key Learnings:
• Grasp the concept of REDO logs and their significance in InnoDB's transaction management.
• Discover the advantages of dynamic REDO log configuration and how to leverage it for optimal performance.
• Understand the inner workings of instant ADD/DROP columns and their impact on database operations.
• Gain valuable insights into the row versioning mechanism that empowers instant column modifications.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Getting the Most Out of ScyllaDB Monitoring: ShareChat's TipsScyllaDB
ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
inQuba Webinar Mastering Customer Journey Management with Dr Graham HillLizaNolte
HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed:
Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.
From Natural Language to Structured Solr Queries using LLMsSease
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
That is where AI – and most importantly, Natural Language Processing and Large Language Model techniques – could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.