Universities have been targeted by phishing campaigns for years that steal user credentials. Recently, stolen credentials have been used to alter direct deposit information and reroute funds to attacker-controlled accounts. Several universities have reported incidents where 15 to several hundred personnel were targeted. The phishing emails typically claim to be about salary increases and contain links to fake login pages that steal credentials when entered. Credentials are then used to access payroll systems and change direct deposit details. Universities are advised to implement multi-factor authentication, alert employees of changes, and educate about phishing risks.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A SURVEY ON WEB SPAM DETECTION METHODS: TAXONOMYIJNSA Journal
Web spam refers to some techniques, which try to manipulate search engine ranking algorithms in order to raise web page position in search engine results. In the best case, spammers encourage viewers to visit their sites, and provide undeserved advertisement gains to the page owner. In the worst case, they use malicious contents in their pages and try to install malware on the victim’s machine. Spammers use three kinds of spamming techniques to get higher score in ranking. These techniques are Link based techniques, hiding techniques and Content-based techniques. Existing spam pages cause distrust to search engine results. This not only wastes the time of visitors, but also wastes lots of search engine resources. Hence spam detection methods have been proposed as a solution for web spam in order to reduce negative effects of spam pages. Experimental results show that some of these techniques are
working well and can find spam pages more accurate than the others. This paper classifies web spam techniques and the related detection methods.
FEATURE SELECTION-MODEL-BASED CONTENT ANALYSIS FOR COMBATING WEB SPAM csandit
With the increasing growth of Internet and World Wide Web, information retrieval (IR) has
attracted much attention in recent years. Quick, accurate and quality information mining is the
core concern of successful search companies. Likewise, spammers try to manipulate IR system
to fulfil their stealthy needs. Spamdexing, (also known as web spamming) is one of the
spamming techniques of adversarial IR, allowing users to exploit ranking of specific documents
in search engine result page (SERP). Spammers take advantage of different features of web
indexing system for notorious motives. Suitable machine learning approaches can be useful in
analysis of spam patterns and automated detection of spam. This paper examines content based
features of web documents and discusses the potential of feature selection (FS) in upcoming
studies to combat web spam. The objective of feature selection is to select the salient features to
improve prediction performance and to understand the underlying data generation techniques.
A publically available web data set namely WEBSPAM - UK2007 is used for all evaluations.
Significant factors for Detecting Redirection Spamijtsrd
Redirection spam refers to a technique where a genuine search user is befooled and made to pass through a chain of redirections and ultimately presented with a compromised web page that may be an adware or an irrelevant content for the user. As a consequence the search engines earn a bad name in quality information retrieval and moreover the users are too dissatisfied. Also, there is sever wastage of expensive network resources like bandwidth. Detecting these malicious redirections is important for quality information retrieval from web. But detecting such redirections is a very tedious task due to the genuine usage of redirections to provide load balancing and URL shortening features. Also, the conventional methods of spam detection such as blacklists, whitelists are not very successful as they need to be updated every time. Many factors have been discussed in previous work that facilitate redirection. To design a more robust and reliable approach, this paper presents some new factors that facilitate redirection spam detection. We also explored the operational profile of each identified factor along with the criteria for its selection. Dr. Prabha Shreeraj Nair"Significant factors for Detecting Redirection Spam" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7019.pdf http://www.ijtsrd.com/engineering/computer-engineering/7019/significant-factors-for-detecting-redirection-spam/dr-prabha-shreeraj-nair
GOOGLE SEARCH ALGORITHM UPDATES AGAINST WEB SPAMieijjournal
With the search engines' increasing importance in people's life, there are more and more attempts to illegitimately influence page ranking by means of web spam. Web spam detection is becoming a major challenge for internet search providers. The Web contains a huge number of profit-seeking ventures that are attracted by the prospect of reaching millions of users at a very low cost. There is an economic incentive for manipulating search engine’s listings by creating otherwise useless pages that score high ranking in the search results. Such manipulation is widespread in the industry. There is a large gray area between the Ethical SEO and the Unethical SEO i.e. spam industry. SEO services range from making web pages indexed, to the creation of millions of fake web pages to deceive search engine ranking algorithms. Today's search engines need to adapt their ranking algorithms continuously to mitigate the effect of spamming tactics on their search results. Search engine companies keep their search ranking algorithms and ranking features secret to protect their ranking system from gaming by spam tactics. We tried to collect here all the Google's search algorithm updates from different sources which are against web spam.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A SURVEY ON WEB SPAM DETECTION METHODS: TAXONOMYIJNSA Journal
Web spam refers to some techniques, which try to manipulate search engine ranking algorithms in order to raise web page position in search engine results. In the best case, spammers encourage viewers to visit their sites, and provide undeserved advertisement gains to the page owner. In the worst case, they use malicious contents in their pages and try to install malware on the victim’s machine. Spammers use three kinds of spamming techniques to get higher score in ranking. These techniques are Link based techniques, hiding techniques and Content-based techniques. Existing spam pages cause distrust to search engine results. This not only wastes the time of visitors, but also wastes lots of search engine resources. Hence spam detection methods have been proposed as a solution for web spam in order to reduce negative effects of spam pages. Experimental results show that some of these techniques are
working well and can find spam pages more accurate than the others. This paper classifies web spam techniques and the related detection methods.
FEATURE SELECTION-MODEL-BASED CONTENT ANALYSIS FOR COMBATING WEB SPAM csandit
With the increasing growth of Internet and World Wide Web, information retrieval (IR) has
attracted much attention in recent years. Quick, accurate and quality information mining is the
core concern of successful search companies. Likewise, spammers try to manipulate IR system
to fulfil their stealthy needs. Spamdexing, (also known as web spamming) is one of the
spamming techniques of adversarial IR, allowing users to exploit ranking of specific documents
in search engine result page (SERP). Spammers take advantage of different features of web
indexing system for notorious motives. Suitable machine learning approaches can be useful in
analysis of spam patterns and automated detection of spam. This paper examines content based
features of web documents and discusses the potential of feature selection (FS) in upcoming
studies to combat web spam. The objective of feature selection is to select the salient features to
improve prediction performance and to understand the underlying data generation techniques.
A publically available web data set namely WEBSPAM - UK2007 is used for all evaluations.
Significant factors for Detecting Redirection Spamijtsrd
Redirection spam refers to a technique where a genuine search user is befooled and made to pass through a chain of redirections and ultimately presented with a compromised web page that may be an adware or an irrelevant content for the user. As a consequence the search engines earn a bad name in quality information retrieval and moreover the users are too dissatisfied. Also, there is sever wastage of expensive network resources like bandwidth. Detecting these malicious redirections is important for quality information retrieval from web. But detecting such redirections is a very tedious task due to the genuine usage of redirections to provide load balancing and URL shortening features. Also, the conventional methods of spam detection such as blacklists, whitelists are not very successful as they need to be updated every time. Many factors have been discussed in previous work that facilitate redirection. To design a more robust and reliable approach, this paper presents some new factors that facilitate redirection spam detection. We also explored the operational profile of each identified factor along with the criteria for its selection. Dr. Prabha Shreeraj Nair"Significant factors for Detecting Redirection Spam" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7019.pdf http://www.ijtsrd.com/engineering/computer-engineering/7019/significant-factors-for-detecting-redirection-spam/dr-prabha-shreeraj-nair
GOOGLE SEARCH ALGORITHM UPDATES AGAINST WEB SPAMieijjournal
With the search engines' increasing importance in people's life, there are more and more attempts to illegitimately influence page ranking by means of web spam. Web spam detection is becoming a major challenge for internet search providers. The Web contains a huge number of profit-seeking ventures that are attracted by the prospect of reaching millions of users at a very low cost. There is an economic incentive for manipulating search engine’s listings by creating otherwise useless pages that score high ranking in the search results. Such manipulation is widespread in the industry. There is a large gray area between the Ethical SEO and the Unethical SEO i.e. spam industry. SEO services range from making web pages indexed, to the creation of millions of fake web pages to deceive search engine ranking algorithms. Today's search engines need to adapt their ranking algorithms continuously to mitigate the effect of spamming tactics on their search results. Search engine companies keep their search ranking algorithms and ranking features secret to protect their ranking system from gaming by spam tactics. We tried to collect here all the Google's search algorithm updates from different sources which are against web spam.
This is a sample referral source definition policy that I created for the University of New Haven. This policy was referenced in my presentation, "Sizing Up a Monumental Task: Building your Recruitment Funnel and Measuring the Flow," at the 2011 NAGAP Annual Conference.
Can domain intelligence help healthcare service providers combat data breachesWhoisXML API
The Anthem breach reported in February 2015, which exposed around 78.8 million customer records, is one of the biggest #databreach in the health care industry. This presentation outlines the case facts and prevention techniques to safeguard against such data compromise.
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Integrated heterogeneous social networking siteseSAT Journals
Abstract The growth of Social Network Sites (SNSs) have opened the opportunity for researchers to explore a large amount of social and behavioral data. The heterogeneity of SNSs offer more seamless services across SNSs and also offers integrated SNSs. The aim of the system is to integrate the heterogeneous SNSs. The P2P-iSN allows users from heterogeneous SNSs to communicate without involving the SNS they have registered with. In proposed system architecture, Global Relationship Model (GRM) is used to capture the relationship strength, and i-Search, to find the optimal social path with less time between any two users who are meaningfully connected in heterogeneous SNSs. With the P2P-iSN, SNS developers can design more effective user-centric SNS application. All SNS user uses different services of different SNS on a single platform, and maintain relationship globally. Keywords—SNS , GRM , OpenID , GlobalID, i-search
Spams are unwanted and also undesirable emails which are mass sent to the numerous victims. Further
penetration of spams into electronic processors and communication equipments such as computers and
mobiles as well as lack of control on the information shared on the internet and other communication
networks and also inefficiency of the spam detecting methods developed for Persian contexts are among the
main challenging issues of the Persian subscribers. This paper presents a novel and efficient method for
thematic identification of Persian spams. The proposed method is capable of identifying the Persian, spams
and also “Penglish” spams. “Penglish” is made up of two words Persian and English and demonstrates a
Persian text which is written by English alphabetic letters. Based on the experimental analysis of the 10000
spams of different type the efficiency of the proposed method is evaluated to be more than 98%. The
presented method is also capable of updating its databases taking the advantage of the feedbacks received
from the users.
Journeying themes of data mining in felon and law enforcement agency profilingeSAT Journals
Abstract Crimes are social nuisance and cost our society badly in several ways. Duties of federal agencies like federal and central bureau of investigation (FBI and CBI), police is to maintain law and order into countries crimes analysis and visualization mainly poor in the Indian police force. The reason behind that is poor and unclean data, missing electronic data is a common phenomenon in the developing countries like India. Police often have vast amount of least utilized crime data in the form of FIR (First Information Report) and subsequent inquiries reports of the particular case registered. If we analyzed reports could give us useful information to help in predicting the crime like crime committing trends, hidden information. To boost the efforts of police force we can use advance technology aspects of data mining for finding information and for visualizing in effective manner we apply GIS. In this paper if crime data is stored in computerized format then we will analyzed how data mining agencies in tracking the activities of criminals. But in developing country like India the major problem is unavailability of criminal data. Criminal or suspect data is not well documented. Once we have data in electronic techniques we are able to get analyzed data and statics more accurate when compare to manual system. In this paper were discuss the loo holes of existing system in India and purpose a new practical model that will provide us with efficiency, accuracy, drop in crime rate , suspect reorganization and provide us better society to live. It is possible with mathematical prediction and data mining techniques for analyzing and visualization. Keywords: Knowledge discovery in database, crime, police, data mining prediction, GIS, hotspot, spatial analysis, finite differences
Case scenario a membership organization/tutorialoutletLunness
FOR MORE CLASSES VISIT
tutorialoutletdotcom
Case Scenario:
A membership organization for state government officials has asked your cybersecurity consulting firm to partner with it in developing a one-day workshop on workforce development needs for cybersecurity workers.
Social network has become so popular with overwhelming high rate of growth, due to this popularity the online social networks is facing the issues of spamming, which has leads to unsubstantial economic loss to this menace of spam and spammers activities. It has leads to uncontrollable dissemination of viruses and malwares, promotional ads, phishing, and scams. spam activities has enter a new dangerous dimension, the spammers have step up their games and tactics online social networks, it consumes large amounts of network bandwidth leading to less revenue and significant economic loss to both private and public sectors. From the previous scholars work on spammer classification taxonomy, various machine learning techniques have been extensively used to detect spam activities and spammers in online social networks. There are various classifier that are learn over content-based features extracted from the user's interactions and profiles to label them as spam/spammers or legitimate. But recently, new network structural bench mark features have been proposed for spammer detection task, but their importance using structural bench mark learning methods has not been extensively evaluated yet. In this research work, we evaluate the the metric performance of some structural bench mark learning methods using scientific and strategic approach based attributes extracted from an interaction network for the task of spammer detection in online social network.
A survey on identification of ranking fraud for mobile applicationseSAT Journals
Abstract Now a day, mobile App is an exceptionally prevalent and surely understood idea because of the quick progression in the portable innovation and cell phones. Because of the extensive number of versatile Apps, ranking fraud is the key test before the versatile App market. In this paper we are proposing a ranking fraud discovery framework for portable Apps. The proposed framework mines the leading sessions, for example, leading sessions of portable applications to precisely find the ranking fraud. Other than this, by displaying Apps ranking, rating and review practices utilizing measurable theories tests, we examine three sorts of confirmations, they are ranking based proofs, rating based proofs and review based confirmations. Proposed an aggregation method to combine all the proof for fraud detection. Keywords: Ranking Fraud, Fraud For Mobile, Identification Mobile Fraud
CERT STRATEGY TO DEAL WITH PHISHING ATTACKScsandit
Every day, internet thieves employ new ways to obtain personal identity people and get access
to their personal information. Phishing is a somehow complex method that has recently been
considered by internet thieves. First, the present study aims to explain phishing, and why an
organization should deal with it and its challenges of providing. In addition, different kinds of
this attack and classification of security approaches for organizational and lay users are
addressed in this article. Finally, the CERT strategy – which relies on three principles of
informing, supporting and helping- is presented to deal with phishing and studying some antiphishing.
This white paper examines the need for strong authentication and explores the return on investment that can be realized in order to help organizations move toward more effective security.
This is a sample referral source definition policy that I created for the University of New Haven. This policy was referenced in my presentation, "Sizing Up a Monumental Task: Building your Recruitment Funnel and Measuring the Flow," at the 2011 NAGAP Annual Conference.
Can domain intelligence help healthcare service providers combat data breachesWhoisXML API
The Anthem breach reported in February 2015, which exposed around 78.8 million customer records, is one of the biggest #databreach in the health care industry. This presentation outlines the case facts and prevention techniques to safeguard against such data compromise.
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Integrated heterogeneous social networking siteseSAT Journals
Abstract The growth of Social Network Sites (SNSs) have opened the opportunity for researchers to explore a large amount of social and behavioral data. The heterogeneity of SNSs offer more seamless services across SNSs and also offers integrated SNSs. The aim of the system is to integrate the heterogeneous SNSs. The P2P-iSN allows users from heterogeneous SNSs to communicate without involving the SNS they have registered with. In proposed system architecture, Global Relationship Model (GRM) is used to capture the relationship strength, and i-Search, to find the optimal social path with less time between any two users who are meaningfully connected in heterogeneous SNSs. With the P2P-iSN, SNS developers can design more effective user-centric SNS application. All SNS user uses different services of different SNS on a single platform, and maintain relationship globally. Keywords—SNS , GRM , OpenID , GlobalID, i-search
Spams are unwanted and also undesirable emails which are mass sent to the numerous victims. Further
penetration of spams into electronic processors and communication equipments such as computers and
mobiles as well as lack of control on the information shared on the internet and other communication
networks and also inefficiency of the spam detecting methods developed for Persian contexts are among the
main challenging issues of the Persian subscribers. This paper presents a novel and efficient method for
thematic identification of Persian spams. The proposed method is capable of identifying the Persian, spams
and also “Penglish” spams. “Penglish” is made up of two words Persian and English and demonstrates a
Persian text which is written by English alphabetic letters. Based on the experimental analysis of the 10000
spams of different type the efficiency of the proposed method is evaluated to be more than 98%. The
presented method is also capable of updating its databases taking the advantage of the feedbacks received
from the users.
Journeying themes of data mining in felon and law enforcement agency profilingeSAT Journals
Abstract Crimes are social nuisance and cost our society badly in several ways. Duties of federal agencies like federal and central bureau of investigation (FBI and CBI), police is to maintain law and order into countries crimes analysis and visualization mainly poor in the Indian police force. The reason behind that is poor and unclean data, missing electronic data is a common phenomenon in the developing countries like India. Police often have vast amount of least utilized crime data in the form of FIR (First Information Report) and subsequent inquiries reports of the particular case registered. If we analyzed reports could give us useful information to help in predicting the crime like crime committing trends, hidden information. To boost the efforts of police force we can use advance technology aspects of data mining for finding information and for visualizing in effective manner we apply GIS. In this paper if crime data is stored in computerized format then we will analyzed how data mining agencies in tracking the activities of criminals. But in developing country like India the major problem is unavailability of criminal data. Criminal or suspect data is not well documented. Once we have data in electronic techniques we are able to get analyzed data and statics more accurate when compare to manual system. In this paper were discuss the loo holes of existing system in India and purpose a new practical model that will provide us with efficiency, accuracy, drop in crime rate , suspect reorganization and provide us better society to live. It is possible with mathematical prediction and data mining techniques for analyzing and visualization. Keywords: Knowledge discovery in database, crime, police, data mining prediction, GIS, hotspot, spatial analysis, finite differences
Case scenario a membership organization/tutorialoutletLunness
FOR MORE CLASSES VISIT
tutorialoutletdotcom
Case Scenario:
A membership organization for state government officials has asked your cybersecurity consulting firm to partner with it in developing a one-day workshop on workforce development needs for cybersecurity workers.
Social network has become so popular with overwhelming high rate of growth, due to this popularity the online social networks is facing the issues of spamming, which has leads to unsubstantial economic loss to this menace of spam and spammers activities. It has leads to uncontrollable dissemination of viruses and malwares, promotional ads, phishing, and scams. spam activities has enter a new dangerous dimension, the spammers have step up their games and tactics online social networks, it consumes large amounts of network bandwidth leading to less revenue and significant economic loss to both private and public sectors. From the previous scholars work on spammer classification taxonomy, various machine learning techniques have been extensively used to detect spam activities and spammers in online social networks. There are various classifier that are learn over content-based features extracted from the user's interactions and profiles to label them as spam/spammers or legitimate. But recently, new network structural bench mark features have been proposed for spammer detection task, but their importance using structural bench mark learning methods has not been extensively evaluated yet. In this research work, we evaluate the the metric performance of some structural bench mark learning methods using scientific and strategic approach based attributes extracted from an interaction network for the task of spammer detection in online social network.
A survey on identification of ranking fraud for mobile applicationseSAT Journals
Abstract Now a day, mobile App is an exceptionally prevalent and surely understood idea because of the quick progression in the portable innovation and cell phones. Because of the extensive number of versatile Apps, ranking fraud is the key test before the versatile App market. In this paper we are proposing a ranking fraud discovery framework for portable Apps. The proposed framework mines the leading sessions, for example, leading sessions of portable applications to precisely find the ranking fraud. Other than this, by displaying Apps ranking, rating and review practices utilizing measurable theories tests, we examine three sorts of confirmations, they are ranking based proofs, rating based proofs and review based confirmations. Proposed an aggregation method to combine all the proof for fraud detection. Keywords: Ranking Fraud, Fraud For Mobile, Identification Mobile Fraud
CERT STRATEGY TO DEAL WITH PHISHING ATTACKScsandit
Every day, internet thieves employ new ways to obtain personal identity people and get access
to their personal information. Phishing is a somehow complex method that has recently been
considered by internet thieves. First, the present study aims to explain phishing, and why an
organization should deal with it and its challenges of providing. In addition, different kinds of
this attack and classification of security approaches for organizational and lay users are
addressed in this article. Finally, the CERT strategy – which relies on three principles of
informing, supporting and helping- is presented to deal with phishing and studying some antiphishing.
This white paper examines the need for strong authentication and explores the return on investment that can be realized in order to help organizations move toward more effective security.
Phishing URL Detection using LSTM Based Ensemble Learning ApproachesIJCNCJournal
Increasing incidents of phishing attacks tempt a significant challenge for cybersecurity personals. Phishing is a deceitful venture with an intention to steal confidential information of an organization or an individual. Many works have been performed to build anti-phishing solutions over the years, but attackers are coming with new manoeuvres from time to time. Many of the existing techniques are experimented based on limited set of URLs and dependent on other software to collect domain related information of the URLs. In this paper, with an aim to build a more accurate and effective phishing attack detection system, we used the concept of ensemble learning using Long Short-Term Memory (LSTM) models. We proposed ensemble of LSTM models using bagging approach and stacking approach. For performing classification using LSTM method, no separate feature extraction is done. Ensemble models are built integrating the predictions of multiple LSTM models. Performances of proposed ensemble LSTM methods are compared with five different machine learning classification methods. To implement these machine learning algorithms, different URL based lexical features are extracted. Mutual Information based feature selection algorithm is used to select more relevant features to perform classifications. Both the bagging and the stacking approaches of ensemble learning using LSTM models outperform other machine learning techniques. The results are compared with other anti-phishing solutions implemented using deep learning methods. Our approaches have proved to be the more accurate one with a low false positive rate of less than 0.15% performed comparatively on a larger dataset.
PHISHING URL DETECTION USING LSTM BASED ENSEMBLE LEARNING APPROACHESIJCNCJournal
Increasing incidents of phishing attacks tempt a significant challenge for cybersecurity personals. Phishing
is a deceitful venture with an intention to steal confidential information of an organization or an
individual. Many works have been performed to build anti-phishing solutions over the years, but attackers
are coming with new manoeuvres from time to time. Many of the existing techniques are experimented
based on limited set of URLs and dependent on other software to collect domain related information of the
URLs. In this paper, with an aim to build a more accurate and effective phishing attack detection system,
we used the concept of ensemble learning using Long Short-Term Memory (LSTM) models. We proposed
ensemble of LSTM models using bagging approach and stacking approach. For performing classification
using LSTM method, no separate feature extraction is done. Ensemble models are built integrating the
predictions of multiple LSTM models. Performances of proposed ensemble LSTM methods are compared
with five different machine learning classification methods. To implement these machine learning
algorithms, different URL based lexical features are extracted. Mutual Information based feature selection
algorithm is used to select more relevant features to perform classifications. Both the bagging and the
stacking approaches of ensemble learning using LSTM models outperform other machine learning
techniques. The results are compared with other anti-phishing solutions implemented using deep learning
methods. Our approaches have proved to be the more accurate one with a low false positive rate of less
than 0.15% performed comparatively on a larger dataset.
A COMPREHENSIVE SURVEY OF PHISHING ATTACKS AND DEFENCES: HUMAN FACTORS, TRAIN...IJNSA Journal
Phishing is a sort of cybercrime as well as a security risk that enables ('phishers') to trick, manipulate, and deceive users into divulging and revealing confidential and sensitive information. Typically, attackers aim to influence and manipulate victims' psyche and emotions. The growing threat of phishing has made it desirable to investigate, and significant research has been undertaken on this matter. This paper explores the human and emotional factors that have been reported in previous studies to be significant in phishing victimization. In addition, we compare what security organizations and researchers have highlighted and emphasised in terms of phishing types and categories as well as training in tackling the problem, in a literature review which takes into account all major credible and published sources.
Running head SOCIAL ENGINEERING1SOCIAL ENGINEERING 6As a .docxtodd521
Running head: SOCIAL ENGINEERING1
SOCIAL ENGINEERING 6
As a hacker, the most successful use case is to target human error as a potential link to data breaches. Social engineering threats target psychological manipulation and human error and manipulate them to identify sensitive data (Krombholz et al., 2015). It can convince users to breach security measures that an attacker can access. Poor cyber security culture is one of the best ways to launch an attack. Social engineering can be implemented in a variety of ways, including whaling, spearfishing, sight checking, and traditional fishing (Hartfield & Loukas, 2015). The reason I use phishing is that I can successfully use social networks, SMS, email and other forms of phishing to get information for personal purposes.
The best way to do business is with microfinance. Although security systems are complex, social engineering can guarantee successful fraud. The first thing to do is to manage employee phishing which could affect access to large amounts of data. A new employee is a good goal with a few safety precautions. For example, it checks the name and contact number of their name tags. After that, the next step is to develop an email equivalent to the main business email. Sending a computer email wherever you need to install a newly needed patch update is an appropriate way to launch an attack (Krombholz et al., 2015). This email is called "Security Update for Windows 7 / 8.1, 8, and 10". The employee replied to a false e-mail indicating his real e-mail address. To get the data successfully, you need a cloned website.
Another way is to search the names of employees working in the company's marketing department and access the latest project accounts. Duplicate stolen invoices can be emailed to employees, requiring them to enter a password to unlock the document. Credentials will help you access your corporate network before you know it. This fishing method is effective because the higher the probability of obtaining information, the higher the number of targets. All details are recorded and stored on the cloning site.
After a while, I sent an email to sign up for work services to improve my account security. Look for reliable sources, like the new email management center. If they agree, they can submit login information to my database. The most important information is your username, password, personal identification number (PIN), personal details, credit card details and most recent transaction details.
Targets can be listed through the antivirus you are using. It is important to use DNS spyware to ensure the success of e-phishing campaigns (Fernan et al., 2019). This process helps determine the protection of the malware involved. The next step is to install antivirus software on the virtual machine. This is done before sending an email. Although it has the same version as the antivirus software used, it is not reliable. Common free antivirus s.
Review of the machine learning methods in the classification of phishing attackjournalBEEI
The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail.
The Impact of Frequent Use Email When Creating Account at the Websites on the...ijcsit
This research aims to measure the impact of frequent use of emails when creating account at the websites
on the privacy and security of the user (a survey study conducted on a sample of email users' views). The
sample, 200 people of the Jordanian society, includes employees of commercial and communication
companies, banks, university students, employees and faculty members as well as computer centers at
universities. All have emails and are able to use the computer and internet. A questionnaire has been
prepared for this purpose aims to measure the variables of the study. SPSS program was used to analyze
the results. The study revealed the existence of a statistical significant impact of frequent use of email
account when creating an account at the Internet sites on the security and privacy of the user. The study
concluded a number of conclusions and recommendations
It seems like we've been hearing a lot about phishing in the news in recent years, and this threat hasn't abated yet. Why are attacks via phishing -and social engineering in general -so prevalent and so effective? This whitepaper examines the many different methods employed in phishing attacks and social engineering campaigns, and offers a solution-based approach to mitigating risk from these attack vectors.
Mary-Louise Fleming, Ph.D. CV
Work experience
Jan 2012 – current Head, School of Public Health and Social Work
July 2006 – Dec 2011 Head, School of Public Health
2003 – June 2006 Acting Head, School Public Health (July to November, 2003: February 2004 – June 2006)
2003 - 2006 Director of Academic Programs, School Public Health
1997 – 2000 Academic Adviser to Dean, Faculty of Health
1993 – 1997, 2001 Senior Lecturer School of Public Health
1988 - 1993 Acting Head, School of Public Health 4th April 1993 to 31 December 1993.
Head, Department of Community Studies, School of Health and Welfare Studies, Brisbane College of Advanced Education (1988-90) and Senior Lecturer in Health Education, Course Co-ordinator, Graduate Diploma in Health Science (Health Education)
1984- 1988 Lecturer in Health Education, School of Health and Welfare Studies, Brisbane College of Advanced Education (1987-88); Lecturer in Health Education, Department of Social Science, Division of Community Studies, Kelvin Grove Campus, Brisbane College of Advanced Education (1985-86).
1982 - 1983 Lecturer in Health Education, School of Education, Canberra College of Advanced Education, ACT.
Nov – Dec 1981 Research Assistant, Director, Central Executive, Brisbane Colleges of Advanced Education.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
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University Payroll Theft Scheme
1. TLP: GREEN
TLP: GREEN
Advisory
University Payroll Theft Scheme 18 August 2014
DISCLAIMER: This Bulletin is provided "as is" for informational purposes only. The Department of Homeland
Security (DHS) does not provide any warranties of any kind regarding any information contained within. DHS
does not endorse any commercial product or service referenced in this bulletin or otherwise. This document is
marked TLP: GREEN. Recipients may share TLP: GREEN information with peers within their sector or
community, but not via publicly accessible channels. For more information on the Traffic Light Protocol, see
http://www.us-cert.gov/tlp.
Summary
This advisory was prepared in collaboration with the National Cybersecurity and Communications
Integration Center (NCCIC), Multi-State Information Sharing and Analysis Center (MS-ISAC), and
Research and Education Networking Information Sharing and Analysis Center (REN-ISAC). The purpose
of this release is to provide relevant and actionable information for prevention and defense.
Universities and colleges have been targeted by spearphishing campaigns designed to steal user
credentials for many years. Stolen credentials are used for many purposes, including but not limited to
sending spam from compromised e-mail accounts, optimizing search engine results for black market
pharmaceutical web pages, gaining access to university-licensed resources, and hosting malware.
For roughly the past year, many of these campaigns have used harvested credentials to alter victim’s
direct deposit information. Targeted individuals include both faculty and administrators from various
departments. Several of the attacks appear to have specifically targeted individuals in university medical
and dental programs. These e-mails often, but not always, use subjects related to salary increases to lure
victims into clicking on malicious links. Subject lines have included:
Your Salary Review Documents
Important Salary Notification
Your Salary Raise Confirmation
connection from unexpected IP
RE: Mailbox has exceeded its storage limit.
The malicious links contained in these e-mails direct victims to webpages controlled by the attackers that
look nearly identical to their university’s legitimate login portals. Once the attackers harvest the user’s
credentials, the credentials are used to access the victim’s payroll information and re-route direct deposits
to an account they control. However, in at least one case, insurance policy information was also changed.
The number of personnel targeted in these attacks also varies but reports indicate targets range anywhere
from 15 to several hundred.
Documented Attacks
In December 2013, a University of Western Michigan employee had their direct deposit information
altered following a successful phishing attack. The victim claims that his single sign on (SSO) credentials
National Cybersecurity and
Communications Integration Center
2. TLP: GREEN
TLP: GREEN
were used to access his e-mail and delete the university’s automatically generated e-mail notification.
Open source reporting highlights several other universities that have been impacted by similar campaigns.
These include: Boston University in early January 2014, Texas A&M in July 2013, the University of
Iowa in November of 2013, and the University of Michigan in August of 2013.1,2,3,4
While only a few
cases have been reported in the media, estimates of impacted universities and colleges are much larger.
To address these attacks, several entities have released public and private alerts on this activity throughout
the past year to include the MS-ISAC, the REN-ISAC, the FBI and The Internet Crime Complaint Center
(IC3) among others.5
Attack Details
While it is possible that the attackers behind these campaigns have been launching similar attacks on
other sectors, there are several reasons why higher educational institutions would be an attractive target.
Most universities and colleges provide access to a wide variety of information regarding their technology
environment in an effort to support their global research and education missions. Access to this
information allows attackers to more easily conduct reconnaissance prior to an attack. E-mail contact
information for faculty and staff can also be easily accessed and used by attackers to create target lists for
their campaigns.
In at least one case, a university confirmed that the list of phishing recipients was generated using a
scrape of email addresses on the public campus website. The university assessed that this was likely the
reason faculty, rather than staff, were the primary targets as this University’s website contains faculty
profiles with email addresses, but few staff email addresses.
Though attempting to harvest credentials through phishing is a common practice, certain aspects of this
campaign indicate that attackers are specifically targeting university and college personnel and
conducting some level of reconnaissance prior to their attack. Though it is unknown if these attacks are
being conducted by a single group or multiple groups, the tactics, techniques, and procedures (TTPs) used
in many of the attacks share very similar characteristics.
Some TTPs Include: Examples of URLs created to resemble legitimate URLs
include:
Altering direct deposit account information
Spoofed to appear as if message came from the
appropriate department, e.g. HR for “salary increase”
lures or IT department if “mailbox exceeded”
Spoofed login screens that are a close replica of
legitimate login screen
Targeting of faculty and staff
Using university images within e-mails text
Spoofed institutional-specific prompts for additional
credential information, e.g., PINS, bank account
numbers.
URLs mimicking legitimate (and accessible) portal
URLs
Use of the “salary increase” approach seems to
coincide with end of the fiscal year.
hxxp://*.ru/www.<domain>.edu/Login.htm
hxxp://*.it/www.<domain>.edu/Login.htm
hxxp://*.com/<domain>/<domain>.edu.htm
hxxp://*.ru/www.<domain>.edu/IPalertsystem/WebLogin.htm
hxxp://*.it/www.<domain>.edu/IPalertsystem/WebLogin.htm
hxxp://*.ru/www.<domain>.edu/<domain>/logon.aspx.htm
hxxp://.../energynews/support/<domain>/<domain>.edu.htm
hxxp://*.ru/www.usu.edu/Login.htm
“<domain>” refers to the affected institution, for example:
Host information, represented by “*” in e.g. *.ru, changes over
time and has been seen to include the use of legitimate domains that
are either compromised or misused for the attacker’s purpose.
3. TLP: GREEN
TLP: GREEN
The phishing e-mails have contained official institutional images; often via an HTML image link direct to
the resource.
Examples of spoofed sender e-mail addresses include:
HRresources @<domain>.edu
employeebenefits @<domain>.edu
In addition, actors appear to be aware of institution-
specific requirements for updating banking
information. For example, if the university requires a
Personal Identification Number (PIN) or bank account
number to change existing settings, the spoofed login
screens may prompt users for this information.
The timing of some of these attacks has also been
observed coinciding with the end of the fiscal year for
many universities and colleges (July 1). This is often
when institutions give their employees salary
increases,6
and may help add to the perceived
legitimacy of the e-mails. These factors all contribute
to a relatively well-planned campaign with an increased
likelihood of deceiving unsuspecting users into
divulging their personal information.
While malicious IPs and IP ranges change over time and may or may not be useful as long-term indicators
of compromise (IoC), below are IP ranges observed to be associated as the sources of some of these
attacks:
41. 190.2.0/24
41. 190.3.0/24
108. 61.0.0/16 range
Prevention Techniques
In an effort to defend against this threat, some institutions set up e-mail alerts which were sent to
individuals when payroll information was altered. Following this, some universities reported victims
were successfully alerted of malicious activity right away. However, in some instances, attackers were
able to access victim e-mail accounts and delete alert messages prior to the victim reading them. This is
possibly due to the use of single sign on SSO.
Single sign on is a form of user authentication that allows a user to access multiple applications with a
single set of login credentials. Some institutions require the use of additional forms of identification when
accessing higher risk data like payroll information. However, as mentioned earlier, actors seem to be
aware of these additional forms of authentication and are integrating them into their phishing e-mails.
As the use of SSO allows attackers to access multiple accounts with a single set of credentials, actors can
implement phishing themes that may have nothing to do with salary increases, yet still result in direct
deposit alteration.
Subject: Important Salary Notification
As part of <institution name> standard practice to
offer salary increases once a year after an annual
review, the Human Resources reviewed you for a
salary raise on your next paycheck
Click below to confirm and access your salary
revision documents:
Click Here to access the documents
Sincerely,
Human Resources
Example of the text contained within the e-mail
4. TLP: GREEN
TLP: GREEN
While educating end users about the dangers of phishing and providing real world examples of what
indicators to look for is always a great practice for creating a human line of defense, universities and
colleges can institute additional measures to help defend their employees from these attacks. It is
important to take into consideration that adding some controls may result in increased costs that entities
may or may not be willing to absorb. Suggested defensive measures include:
Remove self-service direct deposit capabilities, when practical.
Add two-factor authentication or VPN requirements. This may not completely stop attacks but it
makes the attacker’s work more difficult.
Send e-mail alerts to users when direct deposit information has been changed. As mentioned
previously, this can be circumvented in some cases.
Redact sensitive information available to the user in the online system. This will only stop the
further loss of personal information such as SSN.
Require users to enter additional information, like an account number or pin. Attackers who know
this can adjust their phishing messages to adapt.
Implement back-end systems that inspect for suspicious direct deposit account changes, such as
those from unusual geographic locations, duplicate DD account numbers, unusual destination
account routing numbers.
Contact information sharing communities such as the ISAC serving your sector to stay informed
concerning new attacks and to share your own experience.
Be cautious about sending emails to users that link them directly to login pages, as it could have
the unintended consequence of training users to be susceptible to phishing.
Users should be cautious when accessing e-mail and never send account information to others. In
addition, users should use caution and double-check the URL when viewing e-mails that prompt
an individual to login to verify it belongs to their institution.
Further recommendations on phishing defense can be found on the US-CERT website.7
Points of Contact
National Cybersecurity and Communications Integration Center (NCCIC)8
The mission of the NCCIC is to operate at the intersection of the private sector, civilian, law enforcement,
intelligence, and defense communities, applying unique analytic perspectives, ensuring shared situational
awareness, and orchestrating synchronized response efforts while protecting the Constitutional and
privacy rights of Americans in both the cybersecurity and communications domains.
NCCIC@hq.dhs.gov
(888) 282-0870
Research and Education Networking Information Sharing and Analysis Center (REN-ISAC)9
The mission of the REN-ISAC mission is to aid and promote cybersecurity operational protection and
response within the research and higher education (R&E) communities.
SOC@REN-ISAC.net
24x7 Watch Desk (317) 278-6630
The Multi-State Information Sharing and Analysis Center (MS-ISAC)10
The mission of the MS-ISAC is to improve the overall cyber security posture of state, local, territorial and
tribal governments. Public educational institutions (both K-12 and higher education) are part of this
mission space, under the umbrella of state and local governments.
SOC@msisac.org
(866) 787-4722 | (518) 266-3488
5. TLP: GREEN
TLP: GREEN
References
*NOTE: Webpages may change or become unavailable over time.
1
http://cis.tamu.edu/news/2013/07/Nine_A_M_accounts_compromised_direct_deposit_altered.php
2
http://www.boston.com/yourcampus/news/boston_university/2014/01/bu_employee_direct_deposit_pay_stolen_through_alleged_internet_scam.
html
3
http://www.annarbor.com/news/university-of-michigan-spear-phishing/
4
https://it.usu.edu/computer-security/computer-security-threats/articleID=25294
5
Public Service Announcement, Prepared by the Internet Crime Complaint Center (IC3); http://www.ic3.gov/media/2014/140505.aspx
6
http://wiki.fool.com/When_Is_the_Fiscal_Year%3F
7
https://www.us-cert.gov/ncas/tips/ST04-014
8
http://www.dhs.gov/about-national-cybersecurity-communications-integration-center
9
http://www.ren-isac.net/
10
http://msisac.cisecurity.org/