51,932 research outputs found
Phishing through social bots on Twitter
No full textThis work investigates how social bots can phish employees of organizations, and thus endanger corporate network security. Current literature mostly focuses on traditional phishing methods (through e-mail, phone calls, and USB sticks). We address the serious organizational threats and security risks caused by phishing through online social media, specifically through Twitter. This paper first provides a review of current work. It then describes our experimental development, in which we created and deployed eight social bots on Twitter, each associated with one specific subject. For a period of four weeks, each bot published tweets about its subject and followed people with similar interests. In the final two weeks, our experiment showed that 437 unique users could have been phished, 33 of which visited our website through the network of an organization. Without revealing any sensitive or real data, the paper analyses some findings of this experiment and addresses further plans for research in this area.</p
Differential Acquisition of m-Sequences using Recursive Soft Sequential Estimation
No full textIn this contribution a novel sequential estimation method is proposed for the acquisition of -sequences. This sequential estimation method exploits the principle of iterative soft-in-soft-out (SISO) decoding for enhancing the acquisition performance, and that of differential pre-processing for the sake of achieving an enhanced acquisition performance, when communicating over various communication environments. Hence the advocated acquisition arrangement is referred to as the Differential Recursive Soft Sequential Estimation (DRSSE) acquisition scheme. The DRSSE acquisition scheme exhibits a low complexity, which is similar to that of an -sequence generator, while achieving an acquisition time that is linearly dependent on the number of stages in the -sequence generator. A low acquisition time is achieved with the advent of the property that the proposed DRSSE scheme is capable of determining the real-time reliabilities associated with the decision concerning a set of, say , consecutive chips. This set of consecutive chips constitutes the sufficient initial condition for enabling the local -sequence generator to produce a synchronized local despreading -sequence replica. Owing to these attractive characteristics, the DRSSE acquisition scheme constitutes a promising initial synchronization scheme for acquisition of long -sequences, when communicating over various propagation environments
∑_(l+m=k,l,m≥0) ((α+l-1)¦l) ((β+m-1)¦m)=((α+β+k-1)¦k) and its application to negative binomial distribution
No full textWe prove here the following equation: ∑_(l+m=k,l,m≥0) ((α+l-1)¦l) ((β+m-1)¦m)=((α+β+k-1)¦k) and give its application to prove the reproductive property of the negative binomial distribution.
These finite sum equation involving binomial coefficients and proof of the reproductive property are not known as far as the author knows.論文(Article)departmental bulletin pape
De Maiestate / Praeside M. Jacobo Thomasio, Moralis Philosoph. P. P., publice disputabit Johannes Dunte, R. L. Author & Respon: ad diem 9. Septembr. H L. Q. C.
No full textDE MAIESTATE / PRAESIDE M. JACOBO THOMASIO, MORALIS PHILOSOPH. P. P., PUBLICE DISPUTABIT JOHANNES DUNTE, R. L. AUTHOR & RESPON: AD DIEM 9. SEPTEMBR. H L. Q. C.
De Maiestate / Praeside M. Jacobo Thomasio, Moralis Philosoph. P. P., publice disputabit Johannes Dunte, R. L. Author & Respon: ad diem 9. Septembr. H L. Q. C. (1)
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Beiträge (21
The Face of Deception: The Impact of AI-Generated Photos on Malicious Social Bots
No full text\ua9 2014 IEEE. In this research, we investigate the influence of utilizing artificial intelligence (AI)-generated photographs on malicious bots that engage in disinformation, fraud, reputation manipulation, and other types of malicious activity on social networks. Our research aims to compare the performance metrics of social bots that employ AI photos with those that use other types of photographs. To accomplish this, we analyzed a dataset with 13 748 measurements of 11 423 bots from the VK social network and identified 73 cases where bots employed generative adversarial network (GAN)-photos and 84 cases where bots employed diffusion or transformers photos. We conducted a qualitative comparison of these bots using metrics such as price, survival rate, quality, speed, and human trust. Our study findings indicate that bots that use AI-photos exhibit less danger and lower levels of sophistication compared to other types: AI-enhanced bots are less expensive, less popular on exchange platforms, of inferior quality, less likely to be operated by humans, and, as a consequence, faster and more susceptible to being blocked by social networks. We also did not observe any significant difference between GAN-based and diffusion/transformers-based bots, indicating that diffusion/transformers models did not contribute to increased bot sophistication compared to GAN models. Our contributions include a proposed methodology for evaluating the impact of photos on bot sophistication, along with a publicly available dataset for other researchers to study and analyze bots. Our research findings suggest a contradiction to theoretical expectations: in practice, bots using AI-generated photos pose less danger
The Believability Gene in Virtual Bots
No full textInternational audienceVideo game development is not only one of the most profitable entertainment industries but also represents a very interesting field of research. Particularly in the area of Artificial Intelligence (AI), it provides many interesting (and hard to tackle) challenges. One of them consists in creating artificial bots (i.e., game characters not controlled by a human) that mimic human behavior or, at least, show a believable behavior. This paper deals with this issue by describing two very different approaches that were proposed for creating believable bots and applied, with some degree of success, in the context of a first-person shooter (FPS) game. One approach is based on the idea of imitating human player behavior, and the other one consists of automatically creating bots via an interactive evolutionary algorithm. The paper analyzes the performance of these proposals and introduces different forms of hybridizing both approaches for future applications
Erratum to: Effect of moderate red wine intake on cardiac prognosis after recent acute myocardial infarction of subjects with Type 2 diabetes mellitus (Diabetic Medicine, (2006), 23, 9, (974-981), 10.1111/j.1464-5491.2006.01886.x)
No full textIn an article by Marfella et al, the author name C. Saron is incorrect and should be listed as C. Sardu. Therefore the correct author list is: R. Marfella, F. Cacciapuoti, M. Siniscalchi, F. C. Sasso, F. Marchese, F. Cinone, E. Musacchio, M. A. Marfella, L. Ruggiero, G. Chiorazzo, D. Liberti, G. Chiorazzo, G. F. Nicoletti, C. Sardu, F. D'Andrea, C. Ammendola, M. Verza and L. Coppola.In an article by Marfella et al, the author name C. Saron is incorrect and should be listed as C. Sardu. Therefore the correct author list is: R. Marfella, F. Cacciapuoti, M. Siniscalchi, F. C. Sasso, F. Marchese, F. Cinone, E. Musacchio, M. A. Marfella, L. Ruggiero, G. Chiorazzo, D. Liberti, G. Chiorazzo, G. F. Nicoletti, C. Sardu, F. D'Andrea, C. Ammendola, M. Verza and L. Coppola
Even good bots fight: The case of Wikipedia.
No full textIn recent years, there has been a huge increase in the number of bots online, varying from Web crawlers for search engines, to chatbots for online customer service, spambots on social media, and content-editing bots in online collaboration communities. The online world has turned into an ecosystem of bots. However, our knowledge of how these automated agents are interacting with each other is rather poor. Bots are predictable automatons that do not have the capacity for emotions, meaning-making, creativity, and sociality and it is hence natural to expect interactions between bots to be relatively predictable and uneventful. In this article, we analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other's edits over the period 2001-2010, model how pairs of bots interact over time, and identify different types of interaction trajectories. We find that, although Wikipedia bots are intended to support the encyclopedia, they often undo each other's edits and these sterile "fights" may sometimes continue for years. Unlike humans on Wikipedia, bots' interactions tend to occur over longer periods of time and to be more reciprocated. Yet, just like humans, bots in different cultural environments may behave differently. Our research suggests that even relatively "dumb" bots may give rise to complex interactions, and this carries important implications for Artificial Intelligence research. Understanding what affects bot-bot interactions is crucial for managing social media well, providing adequate cyber-security, and designing well functioning autonomous vehicles
Comparative Analysis of Machine Learning Algorithms for the Classification of Twitter Bots
No full textSocial media platforms have become risky for actual users due to the rise in the number of bots. The security mechanisms put in place to help identify and categorize bots accounts from legitimate human accounts have significant drawbacks, such as the misclassification of accounts because of behavioral change. In general, studies on Twitter bots identification demonstrate that bots can be useful while also having a negative impact on users by broadcasting misleading news, spamming, or posing as a phony follower to boost an account's popularity. This study employed Logistic Regression, Catboost, and Random Forest algorithms to develop Twitter bots classification systems, capable of distinguishing between useful and harmful bots accounts in order to limit their impact on users and the Twitter community. The feasibility of the algorithms was tested on Twitter spam bots dataset gotten from Kaggle, containing eight(8) features, which were reduced to two (2) using decision tree. The selected features were further utilized to develop bots classification systems. Comparative analysis of the results showed that Random forest classifier recorded best performance when evaluated on training set, while the Logistic recorded highest performance in terms of accuracy, precision, and F1 Score achieving 83%, 78%, and 81%, respectively when evaluated on test set. The classification systems can help identify and mitigate the impact of harmful bots on Twitter, such as those used for spamming or disseminating fake news. The study has demonstrated the effectiveness of machine learning algorithms in classifying Twitter bots and provided a potential solution for improving online social media platforms
A survey of bots used for distributed denial of service attacks
No full textIn recent years, we have seen the arrival of Distributed Denial-of-Service (DDoS) open-source bot-based attack tools facilitating easy code enhancement, and so resulting in attack tools becoming more powerful. Developing new techniques for detecting and responding to the latest DDoS attacks often entails using attack traces to determine attack signatures and to test the techniques. However, obtaining actual attack traces is difficult, because the high-profile organizations that are typically attacked will not release monitored data as it may contain sensitive information. In this paper, we present a detailed study of the source code of the popular DDoS attack bots, Agobot, SDBot, RBot and Spybot to provide an in-depth understanding of the attacks in order to facilitate the design of more effective and efficient detection and mitigation techniques.Accepted versio
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