ATM Failure Prevention using AI

Complete ATM deposit transactions with jammed documents by capturing an image of the jammed item at the ATM and using it to finish the transaction. If a document gets jammed in an ATM, a servicer retrieves it and securely scans it. The image is transmitted to the ATM system along with partial transaction data. The system extracts more details from the image and completes the deposit. This avoids transaction interruption and document loss from jammed items.

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Stabilizing the plates in a media handling device like a bank ATM to prevent media jams and faults during deposit and dispense operations. The plates are moved vertically by mechanisms that ensure level positioning without tilting. This is achieved using threaded shafts with bearings, sidewalls with clamps, or geared elements with toothed rails. These mechanisms lock the plates at desired vertical positions during media operations to prevent tilting and media jamming.

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Existing smart contract honeypot detection approaches exhibit high false negatives and positives due to (i) their inability generate transaction sequences triggering order-dependent traps (ii) limited code coverage from traditional fuzzings random mutations. In this paper, we propose a hybrid fuzzing framework for based on taint analysis, SCH-Hunter. SCH-Hunter conducts source-code-level feature analysis of contracts extracts data dependency relationships between variables the generated Control Flow Graph construct specific fuzzing. A symbolic execution module is also introduced resolve complex conditional branches that alone fails penetrate, enabling constraint solving. Furthermore, real-time dynamic propagation monitoring implemented using techniques, leveraging flow information optimize seed mutation processes, thereby directing resources toward high-value regions. Finally, by integrating EVM (Ethereum Virtual Machine) instrumentation with effectively identifies detects security-sensitive operations, ultimately generating comprehensive report. Empirical results are as follows. ... Read More

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Enabling an ATM to continue processing transactions and communicating with a central server when it loses network connection. The ATM uses local storage to keep customer data updated. When the network is down, it processes transactions locally using offline protocols. To transmit the offline transactions, the ATM requests nearby devices like smartphones to act as intermediaries. These devices receive the offline transactions, encrypt them, and send them to the central server. This leverages edge devices as an alternative routing pathway when the ATM can't directly connect.

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This research article introduces a deep learning (DL) for identifying vulnerabilities in the smart contracts, leveraging an optimized DL method. The proposed method, termed LogT BiLSTM, combines bidirectional long short-term memory (BiLSTM) with logistic chaos Tasmanian devil optimization (LogT) enhancing detection of vulnerability. evaluation suggested approach is conducted using publicly available datasets. Initially, preprocessing steps involve removing duplicate data and imputing missing data. Subsequently, vulnerability process utilizes loss function achieved through LogT. Results indicate promising performance SC, highlighting efficacy LogT-BiLSTM approach.

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With the continuous development of technology, blockchain has been widely used in various fields by virtue its decentralization, data integrity, traceability, and anonymity. However, still faces many challenges, such as scalability security issues. Artificial intelligence, with powerful processing capability, pattern recognition ability, adaptive optimization algorithms, can improve transaction efficiency blockchain, enhance mechanism, optimize privacy protection strategy, thus effectively alleviating limitations terms security. Most existing related reviews explore application AI a whole but lack in-depth classification discussion on how empower core aspects blockchain. This paper explores artificial intelligence technologies addressing challenges systems, specifically scalability, security, protection. Instead claiming deep theoretical integration, we focus methods, machine learning learning, have adopted to consensus smart contract vulnerability detection, privacy-preserving mechanisms like federated differential privacy. Through comprehensive discussion, this provides structured ... Read More

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Blockchain technology has transformed modern digital ecosystems by enabling secure, transparent, and automated transactions through smart contracts. However, the increasing complexity of these contracts introduces significant challenges, including high computational costs, scalability limitations, difficulties in detecting anomalous behavior. In this study, we propose an AI-based optimization framework that enhances efficiency security blockchain The integrates Neural Architecture Search (NAS) to automatically design optimal Convolutional Network (CNN) architectures tailored data, effective anomaly detection. To address challenge limited labeled transfer learning is employed adapt pre-trained CNN models contract patterns, improving model generalization reducing training time. Furthermore, Model Compression techniques, filter pruning quantization, are applied minimize load, making suitable for deployment resource-constrained environments. Experimental results on Ethereum transaction datasets demonstrate proposed method achieves improvements detection accuracy compared conventional app... Read More

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With the continuous development of blockchain technology, thousands smart contracts have been deployed on blockchain, and number contract vulnerabilities has increased significantly. In task vulnerability detection, fuzz testing methods are usually used for detection. Existing AFL-based inefficient in generating test cases that meet complex path constraints. This study addresses limitations traditional techniques detecting related to strictly constrained conditional branches Ethereum contracts. To overcome this challenge, we propose a hybrid framework combines static semantic analysis with adaptive dynamic lightweight heuristic seed selection mechanism prioritize path-sensitive mutations. Our method adopts semantic-aware operators guide targeted exploration protected execution paths while dynamically optimizing energy allocation among cases. Experimental evaluation benchmark shows compared baseline methods, proposed achieves significantly improved branch coverage accelerated especially critical security such as reentrancy arithmetic exceptions, without sacrificing detection accuracy.... Read More

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Blockchain technology has emerged as a transformative innovation, providing transparent, immutable, and decentralized platform that underpins critical applications across industries such cryptocurrencies, supply chain management, healthcare, finance. Despite their promise of enhanced security trust, the increasing sophistication cyberattacks exposed vulnerabilities within blockchain ecosystems, posing severe threats to integrity, reliability, adoption. This study presents comprehensive systematic review by categorizing analyzing potential threats, including network-level attacks, consensus-based exploits, smart contract vulnerabilities, user-centric risks. Furthermore, research evaluates existing countermeasures mitigation strategies examining effectiveness, scalability, adaptability diverse architectures use cases. The highlights need for context-aware solutions address unique requirements various proposes framework advancing proactive resilient designs. By bridging gaps in literature, this offers valuable insights academics, industry practitioners, policymakers, contributing ongoin... Read More

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Leveraging swarm intelligence to enable automated teller machines (ATMs) to continue operating and processing transactions when they lose network connectivity to their financial institution. The method involves using the ATMs themselves, as well as nearby ATMs within a predefined radius, to collectively compute and execute financial transactions. The ATMs share resources, encrypt data, and communicate using a local ad-hoc network. This allows the swarm to collectively process transactions and provide basic banking services even when individual ATMs cannot do so alone due to lack of resources.

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As a novel decentralized computing paradigm, blockchain is expected to disrupt the existing e-commerce architecture and process. Secure smart contracts are crucial foundation for based on blockchain. However, vulnerabilities in occur from time cause significant financial losses e-commerce. Some static verification methods have been developed guarantee security at design time, but they cannot support complex scenarios runtime. lightweight method, runtime potential method secure contracts. The manual instrument, which leads additional overheads gas consumption. To deal with this, we propose passive learning-based framework Firstly, by exploring Genetic algorithm evolve state merging automaton reorganizing order simultaneously split behaviors, learning model information (PL4ESC). It directly learns P2TA (priced probabilistic timed automaton) traces without any prior knowledge. Then, integrate PL4ESC open-source PAT (Process Analysis Toolkit) automatically verify of experiments show that better accuracy precision than state-of-the-art methods. improves 1 4 percent compared TAG RTI+. far ... Read More

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Smart contracts, closely intertwined with cryptocurrency transactions, have sparked widespread concerns about considerable financial losses of security issues. To counteract this, a variety tools been developed to identify vulnerability in smart contract. However, they fail overcome two challenges at the same time when faced contract bytecode: (i) strong interference caused by enormous non-relevant instructions; (ii) missing semantics bytecode due incomplete data and control flow dependencies. In this paper, we propose multi-teacher based detection method, namely Multi-Teacher Vulnerability Hunter (MTVHunter), which delivers effective denoising semantic under guidance. Specifically, first an instruction teacher eliminate noise abstract pattern further reflect embeddings. Secondly, design novel complementary neuron distillation, effectively extracts necessary from source code replenish bytecode. Particularly, proposed distillation accelerate filling turning knowledge transition into regression task. We conduct experiments on 229,178 real-world contracts that four types common vulnerab... Read More

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The need for intelligent and adaptive cyber security solutions is critical due to the ever evolving complex nature of threats. This monograph reveals possibilities offered by generative AI models in cybersecurity, particularly areas threat simulation anomaly detection. In detail, it provides an overview present landscape describes how like GANs, VAEs, Transformers can be used perform sophisticated emulation, training data synthesis, real-time anomalous behavior detection, attack work discusses also explores design systems, methodologies, ethics model that define its trustworthiness governance. With aid interdisciplinary case studies synthesis approaches, underscores advanced potentials along with emerging vulnerabilities employing defense operations. I hope this becomes a starting point researchers, practitioners, strategists seeking understand leveraging AI. Keywords: Generative AI, Cybersecurity, Threat Simulation, Anomaly Detection, Transformers, Synthetic Data, Adversarial Attacks, Cyber Intelligence, Behavioral Analysis, Ethics, Real-Time Monitoring, Deep Learning, Defense

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Proactively detecting power supply degradation in ATMs before failure to prevent downtime and customer inconvenience. The method involves training a machine learning model using historical maintenance records to predict scores indicating likelihood of power supply degradation based on preprocessed ATM event data. The scores are reported to the ATM's enterprise, allowing proactive action like closely monitoring or replacing the power supply if scores exceed thresholds.

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Predicting equipment component failures using machine learning and sensors to enable proactive maintenance. The system receives sensor data from components like cash belts, displays, and buttons on equipment like ATMs. It uses convolutional neural networks (CNNs) and long short-term memory (LSTM) neural networks to build predictive models from sensor time series data. When new sensor readings are input, the models generate warning signals if component function is abnormal or about to degrade. This allows advanced preparation and replacement of components before failure.

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Automatically generating an AI predictive maintenance model for any machine using historical sensor data and failure logs, without needing data scientists or specialized knowledge. The method involves: 1. Generating a failure labeling model to identify failures from historical data and relabel similar signals. 2. Providing the labeling model output to failure classification and anomaly detection models to learn failure signals and detect abnormal behavior. 3. Ensembling the classification and anomaly outputs to predict machine failures. The method uses time series similarities to relabel failure signals and improve model training. It also allows connecting any machine with historical data and failure logs to generate a custom predictive maintenance model.

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Preventative maintenance and customer service platform that uses AI to predict future events and determine preventative solutions. It processes real-time and historical data to find future events, extracts features to prioritize solutions, and generates an intelligent assistant to perform them. This reduces service requests by addressing issues proactively instead of waiting for customer complaints.

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Automated self-learning ATM security system that enables an ATM to decide whether its security is under threat and learn to fight such threat in real time using means made available to the ATM when the threat arises. The system involves extracting data points from the ATM's transition flows between states and generating rules for each transition based on the extracted data. If a new transition's data doesn't match existing rules, it's flagged as potentially unsafe. This allows the ATM to learn from its own mistakes and adapt its security without external intervention or static rules.

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Optimizing ATM replenishment schedules using machine learning to analyze user data and generate more accurate forecasts. It retrieves unstructured user data like social media posts, generates user personas, then combines with structured transaction data and deep learning to create optimal replenishment schedules. The user personas provide insight into user behavior and consumption patterns beyond just transaction history. This allows predicting when an ATM will run out based on user characteristics and generating optimized schedules for replenishment.

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Prioritizing maintenance schedules for hardware devices like ATMs using predictive failure scores that are prioritized by business impacts. The method involves generating predictive failure scores for each device, setting windows for servicing, determining missed services, prioritizing devices by business impact factors, and generating schedules with lower priority devices having missed services. This maximizes business impact by focusing maintenance on devices with the highest predicted failures and the greatest business impact if unserviced.

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