In 2023, half of the social engineering attacks worldwide were scams, making it the most common type of cyberattack in this category. Phishing ranked second, with **** percent of the attacks, while business e-mail compromise (BEC) made up nearly ** percent of the total spear-phishing attacks.

Introduction

Social Engineering Statistics: Social Engineering refers to the use of psychological tricks by perpetrators to obtain critical and sensitive information from the victims. Although the methods have changed with technology, the purpose of deceit to obtain information remains a significant threat to the general population.

In this blog, we will review social engineering statistics to provide a holistic overview of the dangerous aspects of negative factors involving social engineering. By venturing into this topic, one can avoid getting compromised and be aware of measures to prevent cybersecurity attacks.

******** and*************************************n was the most common social engineering method used by cybercriminals in Poland in 2023.

In 2023, business e-mail compromise (BEC) scams were the most common type of social engineering attacks using Gmail.com. Roughly **** percent of such cyberattacks detected on Gmail.com were identified as BEC scams. General scamming ranked second, with over ** percent, and phishing was identified in *** percent of social engineering attacks abusing Gmail.com.

In today's digitally-driven world, the Social Engineering Attack Defense Solution market is rapidly emerging as a vital component of cybersecurity strategies across various industries. Social engineering attacks, which exploit human psychology rather than technical vulnerabilities, have become increasingly sophistic

The Social Engineering Penetration Testing market plays a crucial role in today's cybersecurity landscape, where organizations increasingly recognize the significance of human factors in their security protocols. Social engineering attacks exploit psychological manipulation to trick individuals into divulging confid

The Social Engineering Audit market has emerged as a crucial element in safeguarding organizations against sophisticated cyber threats. In an era where human behaviors can significantly influence cybersecurity, a comprehensive social engineering audit assesses vulnerabilities created by employees and organizational

Financial overview and grant giving statistics of American Social and Engineering Society

According to surveys of working adults and IT professionals conducted in 2023, almost ***** in ** respondents reported having encountered vishing attacks. This represents a slight decrease from ** percent in the year prior. Vishing attacks are a type of social engineering attacks performed over phone calls or voice messages for phishing.

Between November 2022 and October 2023, the education saw 860 data breach cases caused by system intrusion. Basic web application attacks resulted in 161 data breaches in the finance sector. Social engineering attacks caused 158 data breaches in the construction sector.

Since 2017, cybercrime incidents have increased by *** percent in Poland. In 2023, most incidents were reported in the social engineering and publication categories. The most numerous category is social engineering. These threats relate to phishing campaigns, impersonation, and social engineering attacks targeting users of ICT systems with the aim of phishing and spearphishing for confidential information, infecting a computer with malware, or inducing a user to take specific actions. In this category, the most significant number of incidents involved website impersonation using the subject's image, often aimed at extorting funds, login credentials, or other sensitive data.

The attached figures will show how Facebook's algorithims are sensitive to negative SEO tactics.

Indie Review Magazine obtained the majority of their likes via paid advertising through Facebook.

They achieved ad performance in both international and US markets of 0.01 to 0.03 cents a like on average. However, the magazine is suffering from a very serious troll sitaution.

A violent stalker - who has also been sending employees death threats and threatening messages/etc on social media sites - began targeting the magazine's facebook page as soon as it obtained some success.

You can see from the graphs that the organic likes (the light purple) increased as the paid likes increased, but decreased disproportionatly to the paid likes.

Thus, a massive decline in the organic likes directly preceeded a steady decline in paid page likes, which was a direct result of plumuting ad performance (because the "ranking" algorithims at Facebook, disproportionately value page un-likes). As a result it is possible for negative SEO firms and/or individuals to game the system.

As a result of these negative SEO tactics the magazine has noted ad performance which ranges from 0.02 cents per like and swiftly changing to greater than 4 dollars a like. This is what they refer to as the "money dump".

Slowly over time, the stalker has built up greater than one thousand fake scripted accounts, that he usses to automatically unlike their page whenever a promising post is made.

We suspect and our data highly suggests that Facebook's content ranking algorithims are especially sensitive to this tactic, thus it is possible that there are many pages out there who are suffering from dismal ad performance which may be directly related to sitatuions out of their control.

Negative SEO tactics emloyed by a competitor or in the case of Indie Review Magazine, a stalker, can have a very significant negative impact on the performance of an advertising campaign.

Without acess to Facebook's ranking algorithims we cannot say for certain what is going on behind the scenes at Facenook, but it is obvious from our data that Facebook's algorithims are very suceptible to negative SEO tactics.

If you would like to contact me regarding this reserach, you may email me at Jamie@ITSmoleculardesign.com.

In 2023, software/API vulnerabilities accounted for 38.6 percent of initial access vectors in cyberattacks, up by around 10 percent compared to 28.2 percent in 2022. Previously compromised credentials represented 20.5 percent of attacks, while social engineering and phishing were responsible for 17 percent of cyberattacks in the examined year.

This fileset contains a series of screenshots taken from our facebook advertising account. A few days ago we noticed that some negative "SEO" tactics, for lack of a better term, were having a negative impact on the performance of ads and fan engagement on the facebook page that we've been building.

I developed a custom software package, which utilizes nueural networks I've developed, to identify a target demographic, and suggest advertising content for said target demographic.

After a short training period we were able to create advertisemsents on facebook that averaged a cost of 0.01 cents per like. We also had a fan page engagement of nearly 4 times that of major brands like Wal-Mart.

Shortly after we began to obtain success we started noticing problems with our page. Since we have a stalker issue, we determined that the issues with our page were likely related to him.

We assued this because we had a disproportinately high number of spammy, negative, and inapporpriate comments on our posts. Offline harassment of our staff by the stalker also increased significantly during this time.

Curiously, we believe that the incident with the stalker allowed us to ascertain some interesting observations about Facebook's algorithims, which I've outlined below.

We believe, after reseraching this issue, that Facebook's algorithims suffer from the following issues:

1. They are easily gamed. We think that Facebook's algorithims are hypersensitive to negative comments being made on a post, and conversely likely positive ones as well. If a post is hidden, the comments are negative, or if a user interacts with the post negatively in some way, then Facebook's algorithims will "punish" your page.

2. We think that a series of scripted fake bot accounts would easily cause the issues that we've been expriencing.

As you can see from the data provided, over 90% of our likes come from paid facebook advertisement, therefore we do not have a significant number of fake accounts on our page brought in by third party advertising because we didn't do any of that.

Moreover, we did not send any of our fans obtained via mailing lists, or offline contact to our facebook page, those fans participate with us via email and/or through our private Google+ community.

So it is safe to say that our problems have not been caused by purchasing a large amount of fake likes from any third party vendor.

In addition, because our likes were gained very quickly, at a rate of about 2.5k likes a day, we do not believe that we have suffered from changes in the general demographic of our Facebook fan base over time.

Yet almost immediately after we started expericing trolling issues with our page, we also noticed a dip in the number of fans our posts were shown to by Facebook, and the performance of our ads began to go down, even though the content on our page had not changed.

We attributed this to holes in Facebook's algorithims, and potentially to the excessive use of fake bot accounts by Facebook itself.

We cannot prove the latter satement, but there have been similar reports before. Reference - http://www.forbes.com/sites/davidthier/2012/08/01/facebook-investigating-claims-that-80-of-ad-clicks-come-from-bots/

This article from Forbes outlines how one startup company repoted that up to 80% of their Facebook likes were fake bot accounts even though they paid for advertising directly through Facebook.

Our reserach suggests that Facebook's advertising platform functions as follows: - An advertiser pays for likes with Facebook, and the quality of the content on their page is initially assessed by those who are liking the page, but once the page obtains a following, we believe that the quality of the content is assessed by how many people like the posts on the page directly after they are posted.

If a post gets hidden, marked as spammed, skipped over, whatever, then we beleive that Facebook kicks that post out of the newsfeeds. If this happens to a significant number of posts on the page, then we believe that Facebook places the page on an advertising black-list.

Once on this black-list ads will begin to perform poorly, and content will drop out of newsfeeds causing even the most active page to go silent.

We tested this by posting pictures of attractive blond women, which with our demographic would have normally obtained a large number of likes and we struggled to get even 10 likes at over 20k page likes when we would have previosuly obtained almost 100 likes without boosting at only 5k page likes.

Why this probably isn't seen more often: In most cases this probably takes a while to occur as pages become old and fans grow bored, but in our case, because we have a stalker trolling our page with what appears to be hundres of scripted bot accounts, the effect was seen immediately.

Our data suggests that it became a tug of war between our stalker's army of fake bot accounts (making spammy comments, hiding our posts from newsfeeds, etc) and the real fans that actually like our page (who were voting our conent up - i.e. liking it, etc).

If you look at the graph of page likes in the figures provided - you can see that the darker purple are the fans we obtained via facebook advertising, well over 90%. We believe that the light purple (the "organic" fans) is mostly comprised of our stalker's fake drone accounts. We have less than 20 family members and friends liking our page, when we began this experiment we asked them not to interact with our page or the content.

In conclusion: We still have a lot more work to do, but it is highly likely that many Facebook likes are either scripted bots, and/or that Facebook's "weighting" algorithims are very suceptible to gaming via negative "SEO" tactics. Conversely, they are likely sensitive to gaming via positive "SEO" tactics as well.

Of course we cannot say for certain where the Facebook accounts that like a page come from without acess to their internal systems, but the evidence does strongly suggest that Facebook might be plagued with a large quantity of bot accounts, and that their algorithim has to be sensitive to actions from live users, so that the quality of the content can be easily ascertained. Otherwise it would be pretty easy for an advertiser to game Facebook's system by paying for, and getting, a large quantity of likes for content that is not appealing to any significant group of people.

Again we have to reiterate that we have no solid proof of this, but our data strongly suggests that this is the case.

We have reported the issues to Facebook, but interestingly, after we made it clear that we were going to analyze and investigate the issues with our page, we have been suddenly and incessently plagued with a never ending stream of "technical difficulties" related to our advertising account.

If you'd like to collaborate on this project, please feel free to email me at Jamie@ITSmoleculardesign.com.

Genetic engineering of animals has been proposed to address societal problems, but public acceptance of the use of this technology is unclear. Previous work has shown that the source of information proposing the technology (e.g. companies, universities), the term used to describe the technology (e.g. genome editing, genetic modification), and the genetic engineering application (e.g. different food products) affects technology acceptance. We conducted three mixed-method surveys and used a causal trust-acceptability model to understand social acceptance of genetic engineering (GE) by investigating 1) the source of information proposing the technology, 2) the term used to describe the technology, and 3) the GE application for farm animals proposed. Quantitative analysis showed that the source of information and technology term had little to no effect on social acceptance. Further, participants expressed their understanding of technology using a range of terms interchangeably, all describing technology used to change an organism’s DNA. Applications involving animals were perceived as less beneficial than a plant application, and an application for increased cattle muscle growth was perceived as more risky than a plant application. We used structural equation modelling and confirmed model fit for each survey. In each survey, perceptions of benefit had the greatest effect on acceptance. Following our hypothesized model, social trust had an indirect influence on acceptance through similar effects of perceived benefit and risk. When assessing the acceptability of applications participants considered impacts on plants, animals, and people, trust in actors and technologies, and weighed benefits and drawbacks of GE. Future work should consider how to best measure acceptability of GE for animals, consider contextual factors and consider the use of inductive frameworks.

Companion DATA

Title: Using social media and personality traits to assess software developers' emotional polarity

Authors: Leo Moreira Silva Marília Gurgel Castro Miriam Bernardino Silva Milena Santos Uirá Kulesza Margarida Lima Henrique Madeira

Journal: PeerJ Computer Science

Github: https://github.com/leosilva/peerj_computer_science_2022

The folders contain:

Experiment_Protocol.pdf: document that present the protocol regarding recruitment protocol, data collection of public posts from Twitter, criteria for manual analysis, and the assessment of Big Five factors from participants and psychologists. English version.

/analysis analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications

/dataset alldata.json: contains the dataset used in the paper

/ethics_committee committee_response_english_version.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. English version. committee_response_original_portuguese_version: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. Portuguese version. committee_submission_form_english_version.pdf: the project submitted to the committee. English version. committee_submission_form_original_portuguese_version.pdf: the project submitted to the committee. Portuguese version. consent_form_english_version.pdf: declaration of free and informed consent fulfilled by participants. English version. consent_form_original_portuguese_version.pdf: declaration of free and informed consent fulfilled by participants. Portuguese version. data_protection_declaration_english_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. English version. data_protection_declaration_original_portuguese_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. Portuguese version.

/notebooks General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period

/surveys Demographic_Survey_english_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. English version. Demographic_Survey_portuguese_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. Portuguese version. Demographic_Survey_answers.xlsx: participants' demographic survey answers ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits. ibf_en.doc: translation in English of the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits. ibf_answers.xlsx: participantes' and psychologists' answers for BFI

We have removed from dataset any sensible data to protect participants' privacy and anonymity. We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.

Explore the progression of average salaries for graduates in Social And Engineering Systems And Statistics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Social And Engineering Systems And Statistics relative to other fields. This data is essential for students assessing the return on investment of their education in Social And Engineering Systems And Statistics, providing a clear picture of financial prospects post-graduation.

The Computer Engineering market is a dynamic and rapidly evolving sector that plays a pivotal role in driving technological advancements across multiple industries. Encompassing the design, development, and optimization of computer systems and components, this field integrates principles from both electrical enginee

Reproduction Material for: Reproducible Privacy: Using Differential Privacy to Share Sensitive Research Data

Companion DATA of the paper "Using social media and personality traits to assess software developers’ emotions" submitted to the IEEE Access journal, 2022.

The folders contain:

/analysis analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications

/dataset alldata.json: contains the dataset used in the paper

/notebooks General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results

Statistics - Polynomial Regression: notebook file with the polynomial regression results
Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis
Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period

/surveys Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts Demographic_Survey_answers.xlsx: participants' demographic survey answers ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits ibf_answers.xlsx: participantes' and psychologists' answers for BFI

We have removed from dataset any sensible data to protect participants' privacy and anonymity. We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.