Detection and Prevention of Abuse in Online Social Networks

Adversaries leverage social networks to collect sensitive data about regular users and target them with abuse that includes fake news, cyberbullying, malware distribution, and propaganda. Such behavior is more effective when performed by the social network friends of victims. In two preliminary user studies we found that 71 out of 80 participants have at least 1 Facebook friend with whom (1) they never interact, either in Facebook or in real life, or whom they believe is (2) likely to abuse their posted photos or status updates, or (3) post offensive, false or malicious content. Such friend abuse is often considered to be outside the scope of online social network defenses. Several of our studies suggest that (1) perceived Facebook friend abuse as well as stranger friends are a significant problem; (2) users lack the knowledge or ability to address this problem themselves; and (3) when helped and educated, users are often willing to take defensive actions against abusive existing and pending friends, and strangers. Motivated by the rich, private information of users that is available to the Facebook friends, often the entry point of this vulnerability is the pending friends. In an exploratory study with a number of participants, we found that participants not only tend to accept invitations from perfect strangers but can even invent a narrative of common background to motivate their choice. Further, based on our conjecture that Facebook\u27s interface encourages users to accept pending friends, we develop new interfaces that seek to encourage users to explore the background of their pending friends and also to train them to avoid suspicious friends. The efficacy and implementation simplicity of the proposed modifications suggest that Facebook\u27s unwillingness to protect its users from abusive strangers is deliberate. This dissertation explores the friend abuse problem in online social networks like Facebook. We introduce two novel approaches to prevent friend abuse problem in Facebook. (1) First, we introduce AbuSniff which can detect already existing abusive friends in Facebook, and prevent the abusive friend from doing abuse by taking some protective actions against them. (2) Second, we introduce FLock to address the problem of abuse prevention during the time of friend invitation: by educating and training the Facebook users about the abusive friend from the list of pending friend invitations, and introducing new User Interface to help users reject the potentially abusive friend invitation, thus protecting the user from abuse in advance

AbuSniff: Automatic Detection and Defenses Against Abusive Facebook Friends

Adversaries leverage social network friend relationships to collect sensitive data from users and target them with abuse that includes fake news, cyberbullying, malware, and propaganda. Case in point, 71 out of 80 user study participants had at least 1 Facebook friend with whom they never interact, either in Facebook or in real life, or whom they believe is likely to abuse their posted photos or status updates, or post offensive, false or malicious content. We introduce AbuSniff, a system that identifies Facebook friends perceived as strangers or abusive, and protects the user by unfriending, unfollowing, or restricting the access to information for such friends. We develop a questionnaire to detect perceived strangers and friend abuse.We introduce mutual Facebook activity features and show that they can train supervised learning algorithms to predict questionnaire responses. We have evaluated AbuSniff through several user studies with a total of 263 participants from 25 countries. After answering the questionnaire, participants agreed to unfollow and restrict abusers in 91.6% and 90.9% of the cases respectively, and sandbox or unfriend non-abusive strangers in 92.45% of the cases. Without answering the questionnaire, participants agreed to take the AbuSniff suggested action against friends predicted to be strangers or abusive, in 78.2% of the cases. AbuSniff increased the participant self-reported willingness to reject invitations from strangers and abusers, their awareness of friend abuse implications and their perceived protection from friend abuse.Comment: 12TH INTERNATIONAL AAAI CONFERENCE ON WEB AND SOCIAL MEDIA (ICWSM-18), 10 page

A novel index-based decision support toolkit for safe reopening following a generalized lockdown in low and middle-income countries

While the effectiveness of lockdowns to reduce Coronavirus Disease-2019 (COVID-19) transmission is well established, uncertainties remain on the lifting principles of these restrictive interventions. World Health Organization recommends case positive rate of 5% or lower as a threshold for safe reopening. However, inadequate testing capacity limits the applicability of this recommendation, especially in the low-income and middle-income countries (LMICs). To develop a practical reopening strategy for LMICs, in this study, we first identify the optimal timing of safe reopening by exploring accessible epidemiological data of 24 countries during the initial COVID-19 surge. We find that a safe opening can occur two weeks after the crossover of daily infection and recovery rates while maintaining a negative trend in daily new cases. Epidemiologic SIRM model-based example simulation supports our findings. Finally, we develop an easily interpretable large-scale reopening (LSR) index, which is an evidence-based toolkit—to guide/inform reopening decision for LMICs

A novel index-based decision support toolkit for safe reopening following a generalized lockdown in low and middle-income countries

While the effectiveness of lockdowns to reduce Coronavirus Disease-2019 (COVID-19) transmission is well established, uncertainties remain on the lifting principles of these restrictive interventions. World Health Organization recommends case positive rate of 5% or lower as a threshold for safe reopening. However, inadequate testing capacity limits the applicability of this recommendation, especially in the low-income and middle-income countries (LMICs). To develop a practical reopening strategy for LMICs, in this study, we first identify the optimal timing of safe reopening by exploring accessible epidemiological data of 24 countries during the initial COVID-19 surge. We find that a safe opening can occur two weeks after the crossover of daily infection and recovery rates while maintaining a negative trend in daily new cases. Epidemiologic SIRM model-based example simulation supports our findings. Finally, we develop an easily interpretable large-scale reopening (LSR) index, which is an evidence-based toolkit-to guide/inform reopening decision for LMICs

Case Studies on X-Ray Imaging, MRI and Nuclear Imaging

The field of medical imaging is an essential aspect of the medical sciences, involving various forms of radiation to capture images of the internal tissues and organs of the body. These images provide vital information for clinical diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and nuclear imaging in detecting severe illnesses. However, manual evaluation and storage of these images can be a challenging and time-consuming process. To address this issue, artificial intelligence (AI)-based techniques, particularly deep learning (DL), have become increasingly popular for systematic feature extraction and classification from imaging modalities, thereby aiding doctors in making rapid and accurate diagnoses. In this review study, we will focus on how AI-based approaches, particularly the use of Convolutional Neural Networks (CNN), can assist in disease detection through medical imaging technology. CNN is a commonly used approach for image analysis due to its ability to extract features from raw input images, and as such, will be the primary area of discussion in this study. Therefore, we have considered CNN as our discussion area in this study to diagnose ailments using medical imaging technology.Comment: 14 pages, 3 figures, 4 tables; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

Active Learning on Medical Image

The development of medical science greatly depends on the increased utilization of machine learning algorithms. By incorporating machine learning, the medical imaging field can significantly improve in terms of the speed and accuracy of the diagnostic process. Computed tomography (CT), magnetic resonance imaging (MRI), X-ray imaging, ultrasound imaging, and positron emission tomography (PET) are the most commonly used types of imaging data in the diagnosis process, and machine learning can aid in detecting diseases at an early stage. However, training machine learning models with limited annotated medical image data poses a challenge. The majority of medical image datasets have limited data, which can impede the pattern-learning process of machine-learning algorithms. Additionally, the lack of labeled data is another critical issue for machine learning. In this context, active learning techniques can be employed to address the challenge of limited annotated medical image data. Active learning involves iteratively selecting the most informative samples from a large pool of unlabeled data for annotation by experts. By actively selecting the most relevant and informative samples, active learning reduces the reliance on large amounts of labeled data and maximizes the model's learning capacity with minimal human labeling effort. By incorporating active learning into the training process, medical imaging machine learning models can make more efficient use of the available labeled data, improving their accuracy and performance. This approach allows medical professionals to focus their efforts on annotating the most critical cases, while the machine learning model actively learns from these annotated samples to improve its diagnostic capabilities.Comment: 12 pages, 8 figures; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

Generative Adversarial Networks for Data Augmentation

One way to expand the available dataset for training AI models in the medical field is through the use of Generative Adversarial Networks (GANs) for data augmentation. GANs work by employing a generator network to create new data samples that are then assessed by a discriminator network to determine their similarity to real samples. The discriminator network is taught to differentiate between actual and synthetic samples, while the generator system is trained to generate data that closely resemble real ones. The process is repeated until the generator network can produce synthetic data that is indistinguishable from genuine data. GANs have been utilized in medical image analysis for various tasks, including data augmentation, image creation, and domain adaptation. They can generate synthetic samples that can be used to increase the available dataset, especially in cases where obtaining large amounts of genuine data is difficult or unethical. However, it is essential to note that the use of GANs in medical imaging is still an active area of research to ensure that the produced images are of high quality and suitable for use in clinical settings.Comment: 13 pages, 6 figures, 1 table; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging