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Artificial Intelligence (AI) bots receive much attention and usage in industry manufacturing and even store cashier applications. Our research is to train AI bots to be software engineering assistants, specifically to detect biases and errors inside AI software applications. An example application is an AI machine learning system that sorts and classifies people according to various attributes, such as the algorithms involved in criminal sentencing, hiring, and admission practices. Biases, unfair decisions, and flaws in terms of the equity, diversity, and justice presence, in such systems could have severe consequences. As a Hispanic-Serving Institution, we are concerned about underrepresented groups and devoted an extended amount of our time to implementing “An Assure AI” (AAAI) Bot to detect biases and errors in AI applications. Our state-of-the-art AI Bot was developed based on our previous accumulated research in AI and Deep Learning (DL). The key differentiator is that we are taking a unique approach: instead of cleaning the input data, filtering it out and minimizing its biases, we trained our deep Neural Networks (NN) to detect and mitigate biases of existing AI models. The backend of our bot uses the Detection Transformer (DETR) framework, developed by Facebook,
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Capture the Bot: Using Adversarial Examples to Improve CAPTCHA Robustness to Bot Attacks
- Award ID(s):
- 1822094
- PAR ID:
- 10334467
- Date Published:
- Journal Name:
- IEEE Intelligent Systems
- Volume:
- 36
- Issue:
- 5
- ISSN:
- 1541-1672
- Page Range / eLocation ID:
- 104 to 112
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)As the web keeps increasing in size, the number of vulnerable and poorly-managed websites increases commensurately. Attackers rely on armies of malicious bots to discover these vulnerable websites, compromising their servers, and exfiltrating sensitive user data. It is therefore crucial for the security of the web to understand the population and behavior of malicious bots. In this paper, we report on the design, implementation, and results of Aristaeus, a system for deploying large numbers of honeysites, i.e., websites that exist for the sole purpose of attracting and recording bot traffic. Through a seven-month-long experiment with 100 dedicated honeysites, Aristaeus recorded 26.4 million requests sent by more than 287K unique IP addresses, with 76K of them belonging to clearly malicious bots. By analyzing the type of requests and payloads that these bots send, we discover that the average honeysite received more than 37K requests each month, with more than 50% of these requests attempting to brute-force credentials, fingerprint the deployed web applications, and exploit large numbers of different vulnerabilities. By comparing the declared identity of these bots with their TLS handshakes and HTTP headers, we uncover that more than 86.2% of bots claiming to be Mozilla Firefox and Google Chrome are lying about their identity and are instead built on HTTP libraries and command-line tools.more » « less
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Hilbert and Ackermann asked for a method to consistently extend incomplete theories to complete theories. Gödel essentially proved that any theory capable of encoding its own statements and their proofs contains statements that are true but not provable. Hilbert did not accept that Gödel’s construction answered his question, and in his late writings and lectures, Gödel agreed that it did not, since theories can be completed incrementally, by adding axioms to prove ever more true statements, as science normally does, with completeness as the vanishing point. This pragmatic view of validity is familiar not only to scientists who conjecture test hypotheses but also to real-estate agents and other dealers, who conjure claims, albeit invalid, as necessary to close a deal, confident that they will be able to conjure other claims, albeit invalid, sufficient to make the first claims valid. We study the underlying logical process and describe the trajectories leading to testable but unfalsifiable theories to which bots and other automated learners are likely to converge.more » « less
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Abstract Engineering living systems is a rapidly emerging discipline where the functional biohybrid robotics (or “Bio-bots”) are built by integrating of living cells with engineered scaffolds. Inspired by embryonic heart, we presented earlier the first example of a biohybrid valveless pump-bot, an impedance pump, capable of transporting fluids powered by engineered living muscle tissues. The pump consists of a soft tube attached to rigid boundaries at the ends, and a muscle ring that squeezes the tube cyclically at an off-center location. Cyclic contraction results in a net flow through the tube. We observed that muscle force occasionally buckles the tube in a random fashion, i.e., similar muscles do not buckle the tube consistently. In order to explain this anomaly, here we develop an analytical model to predict the deformation and stability of circular elastic tubes subjected to a uniform squeezing force due to a muscle ring (like a taught rubber band). The prediction from the model is validated by comparing with experiments and finite element analysis. The nonlinear model reveals that the circular elastic tube cannot buckle irrespective of muscle force. Buckling state can be reached and sustained by bending and folding the tube before applying the muscle ring. This imperfection may appear during assembly of the pump or from nonuniform thickness of the muscle ring. This study provides design guides for developing advanced biohybrid impedance pumps for diverse applications.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/MIS.2020.3036156
