Gotowe bibliografie tematyczne / False Positives and Static Topology

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Gotowa bibliografia na temat „False Positives and Static Topology”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "False Positives and Static Topology"

V., P. Krishna Anne*1 &. Dr. K. Rajasekhara Rao2. "ADVANCED IMPLEMENTATION OF ENHANCED AODV TO DETECT PASSIVE BASED INTRUSION DETECTION ATTACKS IN WIRELESS AD HOC NETWORKS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 168–76. https://doi.org/10.5281/zenodo.823076.

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Wireless networks are a combination of nodes or computers or devices which are communicate with each other in network communication. In wireless network communication security is an emerging challenge task. Some of the attacks occur in wireless ad hoc networks because of increase internal activities in data communication. AODV (Ad hoc On-Demand Distance Vector) is an aimed to detect intrusion detection attacks, implementation to detect intruder and provide solution to reduce packet delivery with respect to variative throughput based on data tranmission. To detect network valnerabilties in netw

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Yousefi-Azar, Mahmood, Len Hamey, Vijay Varadharajan, and Shiping Chen. "Byte2vec: Malware Representation and Feature Selection for Android." Computer Journal 63, no. 8 (2019): 1125–38. http://dx.doi.org/10.1093/comjnl/bxz121.

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Abstract Malware detection based on static features and without code disassembling is a challenging path of research. Obfuscation makes the static analysis of malware even more challenging. This paper extends static malware detection beyond byte level $n$-grams and detecting important strings. We propose a model (Byte2vec) with the capabilities of both binary file feature representation and feature selection for malware detection. Byte2vec embeds the semantic similarity of byte level codes into a feature vector (byte vector) and also into a context vector. The learned feature vectors of Byte2v

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Park, Jihyun, Jaeyoung Shin, and Byoungju Choi. "Reduction of False Positives for Runtime Errors in C/C++ Software: A Comparative Study." Electronics 12, no. 16 (2023): 3518. http://dx.doi.org/10.3390/electronics12163518.

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In software development, early defect detection using static analysis can be performed without executing the source code. However, defects are detected on a non-execution basis, thus resulting in a higher ratio of false positives. Recently, studies have been conducted to effectively perform static analyses using machine learning (ML) and deep learning (DL) technologies. This study examines the techniques for detecting runtime errors used in existing static analysis tools and the causes and rates of false positives. It analyzes the latest static analysis technologies that apply machine learning

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Sivaraman, Hariprasad. "Adaptive Thresholding in ML-Driven Alerting Systems for Reducing False Positives in Production Environment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem11938.

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Machine learning (ML)-driven alerting systems are essential for monitoring and ensuring stability in dynamic production environments. Traditional static thresholds often lead to excessive false positives, creating alert fatigue and reducing operational efficiency. This paper presents an adaptive thresholding model that dynamically adjusts alert thresholds based on real-time metrics, temporal trends, and historical data patterns. By integrating Long Short- Term Memory (LSTM) networks and autoencoders within an adaptive framework, this approach continuously learns and adapts to production data,

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Gunda Brahma Sagara. "Hybrid Deep Learning Framework for Real-Time Source Code Vulnerability Detection." Communications on Applied Nonlinear Analysis 32, no. 7s (2025): 889–900. https://doi.org/10.52783/cana.v32.3493.

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Source code vulnerabilities threaten software security, making detection essential in modern development. Traditional methods like static and dynamic analysis often fail due to high false positives and limited scalability. This work introduces a hybrid deep learning framework using CNNs, LSTMs, and code embeddings to detect vulnerabilities in real time. Incorporating Abstract Syntax Trees (ASTs) and Graph Neural Networks (GNNs), the system ensures structural representation and program semantics analysis. Integrated into CI/CD pipelines, the approach improves precision, recall, and F1-score (up

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Dong, Yukun, Mengying Wu, Shanchen Pang, et al. "Automated Program-Semantic Defect Repair and False-Positive Elimination without Side Effects." Symmetry 12, no. 12 (2020): 2076. http://dx.doi.org/10.3390/sym12122076.

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The alarms of the program-semantic defect-detection report based on static analysis include defects and false positives. The repair of defects and the elimination of false positives are time-consuming and laborious, and new defects may be introduced in the process. To solve these problems, the safe constraints interval of related variables and methods are proposed for the semantic defects in the program, and proposes a functionally equivalent no-side-effect program-semantic defect repair and false-positive elimination strategy based on the test-equivalence theory. This paper realizes the autom

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Mateo Tudela, Francesc, Juan-Ramón Bermejo Higuera, Javier Bermejo Higuera, Juan-Antonio Sicilia Montalvo, and Michael I. Argyros. "On Combining Static, Dynamic and Interactive Analysis Security Testing Tools to Improve OWASP Top Ten Security Vulnerability Detection in Web Applications." Applied Sciences 10, no. 24 (2020): 9119. http://dx.doi.org/10.3390/app10249119.

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The design of the techniques and algorithms used by the static, dynamic and interactive security testing tools differ. Therefore, each tool detects to a greater or lesser extent each type of vulnerability for which they are designed for. In addition, their different designs mean that they have different percentages of false positives. In order to take advantage of the possible synergies that different analysis tools types may have, this paper combines several static, dynamic and interactive analysis security testing tools—static white box security analysis (SAST), dynamic black box security an

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Tiganov, Daniil, Lisa Nguyen Quang Do, and Karim Ali. "Designing UIs for Static Analysis Tools." Queue 19, no. 4 (2021): 97–118. http://dx.doi.org/10.1145/3487019.3487026.

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Static-analysis tools suffer from usability issues such as a high rate of false positives, lack of responsiveness, and unclear warning descriptions and classifications. Here, we explore the effect of applying user-centered approach and design guidelines to SWAN, a security-focused static-analysis tool for the Swift programming language. SWAN is an interesting case study for exploring static-analysis tool usability because of its large target audience, its potential to integrate easily into developers' workflows, and its independence from existing analysis platforms.

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LI, MIN, JIAN-XIN WANG, HUAN WANG, and YI PAN. "IDENTIFICATION OF ESSENTIAL PROTEINS FROM WEIGHTED PROTEIN–PROTEIN INTERACTION NETWORKS." Journal of Bioinformatics and Computational Biology 11, no. 03 (2013): 1341002. http://dx.doi.org/10.1142/s0219720013410023.

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Identifying essential proteins is very important for understanding the minimal requirements of cellular survival and development. Fast growth in the amount of available protein–protein interactions has produced unprecedented opportunities for detecting protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. Unfortunately, the protein–protein interactions produced by high-throughput experiments generally have high false positives. Moreover, most of centrality measures based on network topology are sensit

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Amit Singh, Et al. "?Implementation of Security Protocol for Intrusion Detection Systems in Wireless Sensor Networks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2024): 3240–43. http://dx.doi.org/10.17762/ijritcc.v11i9.9515.

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Sensor networks consist of compact sensors and actuators capable of monitoring physical conditions. Wireless Sensor Networks (WSNs) with limited power and dynamic topology require effective security mechanisms. Insider attacks pose a greater challenge than outsider attacks. This work proposes an Intrusion Detection approach in WSNs to detect attacks, emphasizing experimental results, parameter analysis, and Performance Evaluation based on accuracy and minimizing false positives.

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Rozprawy doktorskie na temat "False Positives and Static Topology"

Holmberg, Anna. "Jämförelse av statiska kodanalysverktyg : En fallstudie om statiska kodanalysverktygs förmåga att hitta sårbarheter i kod." Thesis, Högskolan Dalarna, Mikrodataanalys, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-35593.

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Security deficiencies that occur in web applications can have major consequences. PHP is a language that is often used for web applications and it places high demands on how the language is used to ensure it is safe. There are several features in PHP that should be handled with care to avoid security flaws. Static code analysis can help find vulnerabilities in code, but there are some drawbacks that can occur with static code analysis tools. One disadvantage is false positives which means that the tool reports vulnerabilities that do not exist. There are also false negatives which means the to

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Alikhashashneh, Enas A. "Using Machine Learning Techniques to Improve Static Code Analysis Tools Usefulness." Thesis, 2019. http://hdl.handle.net/1805/19942.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>This dissertation proposes an approach to reduce the cost of manual inspections for as large a number of false positive warnings that are being reported by Static Code Analysis (SCA) tools as much as possible using Machine Learning (ML) techniques. The proposed approach neither assume to use the particular SCA tools nor depends on the specific programming language used to write the target source code or the application. To reduce the number of false positive warnings we first evaluated a number of SCA tools in terms of software engin

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(7013450), Enas Ahmad Alikhashashneh. "USING MACHINE LEARNING TECHNIQUES TO IMPROVE STATIC CODE ANALYSIS TOOLS USEFULNESS." Thesis, 2019.

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<p>This dissertation proposes an approach to reduce the cost of manual inspections for as large a number of false positive warnings that are being reported by Static Code Analysis (SCA) tools as much as possible using Machine Learning (ML) techniques. The proposed approach neither assume to use the particular SCA tools nor depends on the specific programming language used to write the target source code or the application. To reduce the number of false positive warnings we first evaluated a number of SCA tools in terms of software engineering metrics using a highlighted synthetic source code n

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Części książek na temat "False Positives and Static Topology"

Chen, Chen, Kai Lu, Xiaoping Wang, Xu Zhou, and Li Fang. "Pruning False Positives of Static Data-Race Detection via Thread Specialization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45293-2_6.

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Chimdyalwar, Bharti, Priyanka Darke, Anooj Chavda, Sagar Vaghani, and Avriti Chauhan. "Eliminating Static Analysis False Positives Using Loop Abstraction and Bounded Model Checking." In FM 2015: Formal Methods. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19249-9_35.

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Dacík, Tomáš, and Tomáš Vojnar. "RacerF: Data Race Detection with Frama-C (Competition Contribution)." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90660-2_20.

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Abstract RacerF is a static analyser for detection of data races in multithreaded C programs implemented as a plugin of the Frama-C platform. The approach behind RacerF is mostly heuristic and relies on analysis of the sequential behaviour of particular threads whose results are generalised using a combination of under- and over-approximating techniques to allow analysis of the multithreading behaviour. In particular, in SV-COMP’25, RacerF relies on the Frama-C’s abstract interpreter EVA to perform the analysis of the sequential behaviour. Although RacerF does not provide any formal guarantees

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Pistoia, Marco, Omer Tripp, and David Lubensky. "Combining Static Code Analysis and Machine Learning for Automatic Detection of Security Vulnerabilities in Mobile Apps." In Application Development and Design. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3422-8.ch047.

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Mobile devices have revolutionized many aspects of our lives. Without realizing it, we often run on them programs that access and transmit private information over the network. Integrity concerns arise when mobile applications use untrusted data as input to security-sensitive computations. Program-analysis tools for integrity and confidentiality enforcement have become a necessity. Static-analysis tools are particularly attractive because they do not require installing and executing the program, and have the potential of never missing any vulnerability. Nevertheless, such tools often have high

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Pistoia, Marco, Omer Tripp, and David Lubensky. "Combining Static Code Analysis and Machine Learning for Automatic Detection of Security Vulnerabilities in Mobile Apps." In Mobile Application Development, Usability, and Security. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0945-5.ch004.

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Vashishth, Tarun Kumar, Alekh Chaudhary, Vikas Sharma, et al. "Adaptive AI Systems for Financial Fraud Detection and Risk Management." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-1200-2.ch021.

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This chapter explores the transformative role of adaptive AI systems in financial fraud detection and risk management. Leveraging machine learning and deep learning techniques, these systems dynamically analyze vast amounts of financial data to identify fraudulent activities and assess risks in real time. Unlike static rule-based methods, adaptive AI continuously evolves by learning from new data and adapting to emerging fraud tactics, thereby enhancing detection accuracy and reducing false positives. The chapter highlights key algorithms, such as neural networks and anomaly detection models t

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Lau, Matthew, Fahad Alsaeed, Kayla Thames, et al. "Physics-Assisted Explainable Anomaly Detection in Power Systems." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241073.

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Detection of cyber-attacks in power systems is crucial for rapid corrective actions like isolation, disinfection and asset restoration. For real-time deployment, detection methods must not only be accurate and computationally efficient, but also interpretable for further action. While physics models can reliably detect cyber-attacks, diagnosing where and how assets were attacked is computationally demanding. To supplement detection models, we propose Physics-Assisted Statistics for Anomaly Localization (PASAL), a domain-informed data-driven method that directly identifies anomalous devices. PA

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Velaga, Venkata Sai Sandeep. "Hybrid Optimization-Based Algorithm for Adaptive Threat Detection in Cloud Environments." In CyberFusion: The Strategic Integration of Cybersecurity for Digital Transformation in Tech Environment. QTanalytics India, 2025. https://doi.org/10.48001/978-81-980647-2-1-5.

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In the rapidly changing field of cloud computing, traditional security approaches may not be enough to thwart new cyber threats that are now sophisticated and adaptive. This paper proposes a new hybrid optimization-based threat detection model based on adaptive machine learning models and multiple optimization methods, such as Genetic Algorithms and Particle Swarm Optimization (PSO). The proposed model exploits optimization to select relevant features and optimize detection parameters. The hybrid model has the ability to learn adaptive patterns against evolving attacks and dynamically adjust t

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Lowe, Devesh, and Mithilesh Kumar Dubey. "A Novel Framework for Java Based Data Race Detection using ECC-EVKM-BERT and SS-LSGRU with KL-FBIS." In Advancements in Intelligent Systems. Soft Computing Research Society, 2024. https://doi.org/10.56155/978-81-975670-3-2-2.

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When two or more threads visit a shared variable concurrently and at least one of those accesses is a write operation without the necessary synchronization mechanisms in place, it’s known as a data race in multi-threaded algorithms. Unpredictable behavior, such as damaged data, software crashes, and inconsistent outputs, may result from this. Since data races can appear inconsistently based on thread scheduling and time, they are infamously hard to debug. Data races can have serious consequences, such as unstable software that responds erratically to various stimuli. They jeopardize data integ

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Kumar, Krishna, Aishwaryaa L K, and Pradeep K K. "Reinforcement Learning for Automated Intrusion Detection and Adaptive Defense in Zero-Day Attack Scenarios." In Artificial Intelligence in Cybersecurity for Risk Assessment and Transparent Threat Detection Frameworks. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552029-09.

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The increasing sophistication of cyber threats, particularly zero-day attacks, necessitates the development of intelligent and adaptive security mechanisms capable of real-time threat detection and mitigation. Traditional intrusion detection and prevention systems (IDPS) rely on static rule sets and signature-based techniques, which are insufficient against novel and evolving attack vectors. Reinforcement Learning (RL) offers a promising approach by enabling autonomous agents to learn optimal defense strategies through continuous interaction with network environments. This chapter explores the

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Streszczenia konferencji na temat "False Positives and Static Topology"

Muske, Tukaram, and Uday P. Khedker. "Efficient elimination of false positives using static analysis." In 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE). IEEE, 2015. http://dx.doi.org/10.1109/issre.2015.7381820.

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Dimastrogiovanni, Carlo, and Nuno Laranjeiro. "Towards Understanding the Value of False Positives in Static Code Analysis." In 2016 Seventh Latin-American Symposium on Dependable Computing (LADC). IEEE, 2016. http://dx.doi.org/10.1109/ladc.2016.25.

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Yang, Yixin, Ming Wen, Xiang Gao, Yuting Zhang, and Hailong Sun. "Reducing False Positives of Static Bug Detectors Through Code Representation Learning." In 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2024. http://dx.doi.org/10.1109/saner60148.2024.00075.

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Nguyen, Thu Trang, Pattaravut Maleehuan, Toshiaki Aoki, Takashi Tomita, and Iori Yamada. "Reducing False Positives of Static Analysis for SEI CERT C Coding Standard." In 2019 IEEE/ACM Joint 7th International Workshop on Conducting Empirical Studies in Industry (CESI) and 6th International Workshop on Software Engineering Research and Industrial Practice (SER&IP). IEEE, 2019. http://dx.doi.org/10.1109/cesser-ip.2019.00015.

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Murali, Aniruddhan, Noble Mathews, Mahmoud Alfadel, Meiyappan Nagappan, and Meng Xu. "FuzzSlice: Pruning False Positives in Static Analysis Warnings through Function-Level Fuzzing." In ICSE '24: 46th IEEE/ACM International Conference on Software Engineering. ACM, 2024. http://dx.doi.org/10.1145/3597503.3623321.

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Park, Joonyoung, Inho Lim, and Sukyoung Ryu. "Battles with false positives in static analysis of JavaScript web applications in the wild." In ICSE '16: 38th International Conference on Software Engineering. ACM, 2016. http://dx.doi.org/10.1145/2889160.2889227.

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Marcilio, Diego, and Rodrigo Bonifácio. "Automatically Fixing Static Analysis Tools Violations." In XI Congresso Brasileiro de Software: Teoria e Prática. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/cbsoft_estendido.2020.14625.

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Static analysis tools analyze source code to find deviations, or violations, from recommended programming practices defined as rules. A warning is raised when a piece of code violates any rule. Even though these tools can help to identify defects, developers still face several barriers when using them. Among the challenges are the significant number of reported warnings, often caused by false-positives, and the need to devise fixes, a repetitive and error-prone process. In this work, we addressed these two difficulties in two stages: 1) we identified which kind of rules are mostly fixed by Jav

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Pinto Prieto, Daira, Ronald de Haan, and Aybüke Özgün. "A Belief Model for Conflicting and Uncertain Evidence: Connecting Dempster-Shafer Theory and the Topology of Evidence." In 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/54.

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One problem to solve in the context of information fusion, decision-making, and other artificial intelligence challenges is to compute justified beliefs based on evidence. In real-life examples, this evidence may be inconsistent, incomplete, or uncertain, making the problem of evidence fusion highly non-trivial. In this paper, we propose a new model for measuring degrees of beliefs based on possibly inconsistent, incomplete, and uncertain evidence, by combining tools from Dempster-Shafer Theory and Topological Models of Evidence. Our belief model is more general than the aforementioned approac

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Moraes, Amanda, Paulo Borba, and Léuson Da Silva. "Semantic conflict detection via dynamic analysis." In Simpósio Brasileiro de Linguagens de Programação. Sociedade Brasileira de Computação, 2024. http://dx.doi.org/10.5753/sblp.2024.3471.

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During collaborative software development, a semantic conflict may occur when the individual behavior expected by different developers is no longer preserved after merging their branches. While potential semantic conflicts are not captured via textual merge tools, different approaches have already been proposed based on static analysis or automated test generation to verify behavioral changes given a merge scenario. However, these approaches share some limitations regarding scalability and reporting false positives and negatives. Trying to address these limitations, in this work, we assess the

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Yalamarty, Sai Sharan, Kriti Singh, Mohammadreza Kamyab, Curtis Cheatham, Vladimir Crkvenjakov, and Kelly Flurry. "Early Detection of Well Control Kick Events by Applying Data Analytics on Real Time Drilling Data." In IADC/SPE International Drilling Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/208770-ms.

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Abstract Kick events are some of the most life-threatening and environmentally disastrous events during drilling. Identifying potential kick events in time is valuable. Several hybrid algorithms combining physics and data analytics have been developed to help identify potential kick events from trends in real-time drilling data. These algorithms encompass all drilling operations like drilling, tripping, circulating, and making connections. The goal is to enable management by exception in real-time monitoring of wells. Real-time drilling data acquired from the sensors on the rig and other stati

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