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Journal articles on the topic 'False Positives and Static Topology'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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Ji, Yuchen, Ting Dai, Zhichao Zhou, Yutian Tang, and Jingzhu He. "Artemis: Toward Accurate Detection of Server-Side Request Forgeries through LLM-Assisted Inter-procedural Path-Sensitive Taint Analysis." Proceedings of the ACM on Programming Languages 9, OOPSLA1 (2025): 1349–77. https://doi.org/10.1145/3720488.

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Server-side request forgery (SSRF) vulnerabilities are inevitable in PHP web applications. Existing static tools in detecting vulnerabilities in PHP web applications neither contain SSRF-related features to enhance detection accuracy nor consider PHP’s dynamic type features. In this paper, we present Artemis, a static taint analysis tool for detecting SSRF vulnerabilities in PHP web applications. First, Artemis extracts both PHP built-in and third-party functions as candidate source and sink functions. Second, Artemis constructs both explicit and implicit call graphs to infer functions’ relati

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Oleksandr, Popov, Shmatko Nataliia, Budanov Pavlo, Pantielieieva Iryna, and Brovko Kostiantyn. "COSTEFFECTIVENESS IN MATHEMATICAL MODELLING OF THE POWER UNIT CONTROL." Eastern-European Journal of Enterprise Technologies 6, no. 3 (102) (2019): 39–48. https://doi.org/10.15587/1729-4061.2019.183422.

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The authors of the study have analysed the criteria for increasing cost-effectiveness in the operation of power-generating equipment of power units at TPPs and NPPs. The existing methods of calculating the cost-effectiveness disregard factors that lead to economic costs during shutdowns of the power unit and reduce the energy consumer load. A significant factor in increasing cost-effectiveness of the automated control systems at a power unit of a power plant is the compulsory checking to detect a low level of information reliability. It is proved that reliability of the power unit technologica

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Borodin, Alexey Evgenevich, Alexey Vyacheslavovich Goremykin, Sergey Pavlovitch Vartanov, and Andrey Andreevich Belevantsev. "Searching for tainted vulnerabilities in static analysis tool Svace." Proceedings of the Institute for System Programming of the RAS 33, no. 1 (2021): 7–32. http://dx.doi.org/10.15514/ispras-2021-33(1)-1.

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The paper is dedicated to search for taint-based errors in the source code of programs, i.e. errors caused by unsafe use of data obtained from external sources, which could potentially be modified by an attacker. The interprocedural static analyzer Svace was used as a basis. The analyzer searches both for defects in the program and searches for suspicious places where the logic of the program may be violated. The goal is to find as many errors as possible at an acceptable speed and a low level of false positives (< 20-35%). To find errors, Svace with help of modified compiler builds a low-l

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He, Weigang, Peng Di, Mengli Ming, et al. "Finding and Understanding Defects in Static Analyzers by Constructing Automated Oracles." Proceedings of the ACM on Software Engineering 1, FSE (2024): 1656–78. http://dx.doi.org/10.1145/3660781.

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Static analyzers are playing crucial roles in helping find programming mistakes and security vulnerabilities. The correctness of their analysis results is crucial for the usability in practice. Otherwise, the potential defects in these analyzers (, implementation errors, improper design choices) could affect the soundness (leading to false negatives) and precision (leading to false positives). However, finding the defects in off-the-shelf static analyzers is challenging because these analyzers usually lack clear and complete specifications, and the results of different analyzers may differ. To

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Tan, Yaodan, and Junfeng Tian. "A Method for Processing Static Analysis Alarms Based on Deep Learning." Applied Sciences 14, no. 13 (2024): 5542. http://dx.doi.org/10.3390/app14135542.

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Automatic static analysis tools (ASATs), also known as static analyzers, have demonstrated their significance and practicability in detecting software defects. ASATs assist developers to identify potential vulnerabilities, errors, and security hazards in source code without executing the software. As software systems grow in scale and complexity, ASATs are replacing manual security audits and becoming crucial for detecting issues in code. However, ASATs often generate numerous warnings with high false positive rates, while developers typically only take measures on a small portion of actionabl

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Marques, Filipe, Mafalda Ferreira, André Nascimento, et al. "Automated Exploit Generation for Node.js Packages." Proceedings of the ACM on Programming Languages 9, PLDI (2025): 1341–66. https://doi.org/10.1145/3729304.

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The Node.js ecosystem, with its growing popularity and increasing exposure to security vulnerabilities, has a pressing need for more effective security analysis tools. To reduce false positives, recent works on detecting vulnerabilities in Node.js packages have developed synthesis algorithms to generate proof-of-concept exploits. However, these tools focus mainly on vulnerabilities that can be triggered by a single direct call to an exported function of the analyzed package, failing to generate exploits that require more complex interactions. In this paper, we present Explode.js, the first too

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Cao, Buwen, Jiawei Luo, Cheng Liang, and Shulin Wang. "Identifying Protein Complexes by Combining Network Topology and Biological Characteristics." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 7666–75. http://dx.doi.org/10.1166/jctn.2016.6084.

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Protein–protein interaction (PPI) data derived from biological experiments include many false-positive interactions which are treated equally as other real physical interactions, thereby complicating the detection of real protein complexes from protein–protein interaction (PPI) networks. In this paper, a new weighting method, named as cwMINE (combined weight of module identification in networks), for detecting protein complexes efficiently in protein interaction networks is presented. cwMINE has a good combination between network topology and biological feature, which can solve false positives

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Mandel, Nicolas, Nils Kompe, Moritz Gerwin, and Floris Ernst. "KISS—Keep It Static SLAMMOT—The Cost of Integrating Moving Object Tracking into an EKF-SLAM Algorithm." Sensors 24, no. 17 (2024): 5764. http://dx.doi.org/10.3390/s24175764.

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The treatment of moving objects in simultaneous localization and mapping (SLAM) is a key challenge in contemporary robotics. In this paper, we propose an extension of the EKF-SLAM algorithm that incorporates moving objects into the estimation process, which we term KISS. We have extended the robotic vision toolbox to analyze the influence of moving objects in simulations. Two linear and one nonlinear motion models are used to represent the moving objects. The observation model remains the same for all objects. The proposed model is evaluated against an implementation of the state-of-the-art fo

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Jabbar, Haidar, and Samir Al-Janabi. "AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning." Journal of Cybersecurity and Privacy 5, no. 2 (2025): 26. https://doi.org/10.3390/jcp5020026.

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Phishing remains a persistent cybersecurity threat, often bypassing traditional detection methods due to evolving attack techniques. This study presents a Reinforcement Learning (RL)-based phishing detection framework, leveraging a Deep Q-Network (DQN) to enhance detection accuracy, reduce false positives, and improve classification performance. The model was trained and evaluated using a real-world dataset comprising 5000 emails (2500 phishing and 2500 benign) and externally validated against a synthetic phishing dataset of 1000 samples simulating unseen attacks. It achieved a 95% accuracy, 9

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Tian, Jie, Ming Yu Luo, and Meng Yang Chen. "A New Type of Hexagonal Fault-Tolerant Clustering Algorithm." Applied Mechanics and Materials 543-547 (March 2014): 1728–33. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1728.

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In view of the problem that present most clustering algorithms are given priority to save energy but ignoring fault tolerance, this paper puts forward a new kind of hexagonal fault-tolerant clustering algorithm (HFTC). By increasing the middle management node, which controls nodes rate of false positives and managements topology within the cluster, this algorithm completes nondestructive substitution between nodes. At the same time, the introduction of backup nodes, it also improves the networks fault tolerance. The simulation experiments show that HFTC can guarantee a high fault tolerance in

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Sandhya, Guduru. "Web3 Smart Contract Security: Slither Static Analysis, Echidna Fuzzing, and Certora Formal Verification." European Journal of Advances in Engineering and Technology 11, no. 5 (2024): 147–50. https://doi.org/10.5281/zenodo.15387253.

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Ensuring the security of smart contracts is essential in Web3 development, as vulnerabilities in Solidity-based agreements can result in significant financial and operational risks. This study explores automated security auditing techniques, focusing on Slither for static analysis, Echidna for property-based fuzz testing, and Certora for formal verification. By integrating these tools, the research enhances vulnerability detection and reduces reliance on manual audits. Additionally, the study assesses Maximum Extractable Value (MEV) risks and evaluates Flashbots SUAVE as a mitigation strategy.

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Brotzman, Robert, Danfeng Zhang, Mahmut Taylan Kandemir, and Gang Tan. "SpecSafe: detecting cache side channels in a speculative world." Proceedings of the ACM on Programming Languages 5, OOPSLA (2021): 1–28. http://dx.doi.org/10.1145/3485506.

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The high-profile Spectre attack and its variants have revealed that speculative execution may leave secret-dependent footprints in the cache, allowing an attacker to learn confidential data. However, existing static side-channel detectors either ignore speculative execution, leading to false negatives, or lack a precise cache model, leading to false positives. In this paper, somewhat surprisingly, we show that it is challenging to develop a speculation-aware static analysis with precise cache models: a combination of existing works does not necessarily catch all cache side channels. Motivated

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Bierhoff, Kevin. "Wildcards need witness protection." Proceedings of the ACM on Programming Languages 6, OOPSLA2 (2022): 373–94. http://dx.doi.org/10.1145/3563301.

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In this paper, we show that the unsoundness discovered by Amin and Tate (2016) in Java’s wildcards is avoidable, even in the absence of a nullness-aware type system. The key insight of this paper is that soundness in type systems that implicitly introduce existential types through subtyping hinges on still making sure there are suitable witness types when introducing existentially quantified type variables. To show that this approach is viable, this paper formalizes a core calculus and proves it sound. We used a static analysis based on our approach to look for potential issues in a vast corpu

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Shimchik, Nikita Vladimirovich, Valery Nikolayevich Ignatyev, and Andrey Andreevich Belevantsev. "Irbis: static taint analyzer for vulnerabilities detection in C/C++." Proceedings of the Institute for System Programming of the RAS 34, no. 6 (2022): 51–66. http://dx.doi.org/10.15514/ispras-2022-34(6)-4.

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Static taint analysis can be used to find various security weaknesses and vulnerabilities in programs by discovering dataflow paths from taint sources to taint sinks. In most cases the data is called ”tainted” if it was obtained from an untrusted source without proper sanitization. In this paper we present a static taint analyzer Irbis. It implements analysis based on IFDS (Interprocedural Finite Distributive Subset) dataflow problem, as well as various extensions aimed at improving accuracy and completeness of the analysis. It supports different definitions of tainted data, which enables it t

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Rehana Sultana Khan. "AI-Based Rate Limiting for Cloud Infrastructure: Implementation Guide." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 370–80. https://doi.org/10.32996/jcsts.2025.7.3.43.

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Rate limiting represents a critical security mechanism for protecting all applications from abuse while ensuring fair resource allocation. Traditional static threshold approaches face significant limitations in modern dynamic environments, frequently triggering false positives during legitimate traffic fluctuations while missing sophisticated attack patterns. AI-powered rate limiting addresses these limitations by analyzing traffic patterns across multiple dimensions and making intelligent throttling decisions based on learned behavior rather than predetermined rules. This comprehensive framew

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Yang, Gang, Chaojing Tang, and Xingtong Liu. "DualAC2NN: Revisiting and Alleviating Alert Fatigue from the Detection Perspective." Symmetry 14, no. 10 (2022): 2138. http://dx.doi.org/10.3390/sym14102138.

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The exponential expansion of Internet interconnectivity has led to a dramatic increase in cyber-attack alerts, which contain a considerable proportion of false positives. The overwhelming number of false positives cause tremendous resource consumption and delay responses to the really severe incidents, namely, alert fatigue. To cope with the challenge from alert fatigue, we focus on enhancing the capability of detectors to reduce the generation of false alerts from the detection perspective. The core idea of our work is to train a machine-learning-based detector to grasp the empirical intellig

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Atmaja, Devi Yulianty Surya, and Achmad Samsudin. "Are There Misconceptions in My Class? Misconception Analysis with T3-SF (Three Tier-Test on Static Fluid)." Tarbiyah : Jurnal Ilmiah Kependidikan 13, no. 2 (2024): 155–70. https://doi.org/10.18592/tarbiyah.v13i2.13518.

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This study aims to identify the misconceptions of 20 students in class XI MIPA in one of the Senior High Schools in Sumedang Regency, Indonesia. The selection of research subjects used a random sampling technique. This research used a descriptive qualitative research design with a case study approach. The data collection technique in this study used T3-SF (Three Tier-Test on Static Fluid) to identify misconceptions in the submaterial of Hydrostatic Pressure, Pascal's Law and Archimedes' Law. Students did ten questions on Google Forms and processed them using Microsoft Excel. Based on this stud

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Naseer, Atif, and Enrique Nava. "Counting of Underwater Static Objects Through an Efficient Temporal Technique." Journal of Marine Science and Engineering 13, no. 2 (2025): 205. https://doi.org/10.3390/jmse13020205.

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Counting marine species is a challenging task for biologists and marine experts. This paper presents an efficient temporal technique for counting underwater static objects. The proposed method employs deep learning techniques to detect objects over time and an efficient spatial–temporal algorithm to track them, allowing for accurate counting of objects within a given area. The technique is designed to handle various challenges that arise in underwater environments such as low visibility, object occlusion, and water turbulence. The approach is validated through experiments conducted on the surv

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Okebule, T., O. A. Adeyemo, K. A. Olatunji, and A. S. Awe. "Content Analyzer for Information Leakage Detedction and Prevention in Android Smart Devices: A Conceptual Approach." advances in multidisciplinary & scientific research journal publication 6, no. 1 (2020): 72–90. http://dx.doi.org/10.22624/aims/v6n1p7.

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The advent of android operating system introduced tools to keep track of users’ information activities and prevent information leakage which bridged the trust between application developers and consumers. Literature shows that several phenomena had been developed to prevent malicious applications from stealing personal sensitive information from smart phones but there is still the need for efficient solutions. This study proposes a conceptual approach for the development of a contentAnalyzer for information leakage detection and prevention on android-based devices. The concept will help to min

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Gupta, Brij B., Pooja Chaudhary, and Shashank Gupta. "Designing a XSS Defensive Framework for Web Servers Deployed in the Existing Smart City Infrastructure." Journal of Organizational and End User Computing 32, no. 4 (2020): 85–111. http://dx.doi.org/10.4018/joeuc.2020100105.

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Cross-site scripting is one of the notable exceptions effecting almost every web application. Hence, this article proposed a framework to negate the impact of the XSS attack on web servers deployed in one of the major applications of the Internet of Things (IoT) i.e. the smart city environment. The proposed framework implements 2 approaches: first, it executes vulnerable flow tracking for filtering injected malicious scripting code in dynamic web pages. Second, it accomplished trusted remark generation and validation for unveiling any suspicious activity in static web pages. Finally, the filte

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Anub, A., and S. Sreelekshmy. "Dynamic Threshold-Based Algorithm for Client-Based HTTP Proxy Attack Detection through Spatial and Temporal Behavior Pattern Analysis." Recent Trends in Androids and IOS Applications 6, no. 3 (2024): 48–53. https://doi.org/10.5281/zenodo.13626561.

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<em>This paper provides a unique approach to client- based HTTP proxy attack detection using a Dynamic Spatiotem- poral Behavior Analysis (DSTBA) algorithm. Traditional meth- ods often lack adaptability to sophisticated cyberattacks. DSTBA addresses this by dynamically adjusting detection thresholds based on real-time analysis of spatial (network node distribution and interaction) and temporal (request timing and frequency) behavior patterns. This integration with machine learning tech- niques enhances attack identification accuracy while minimizing false positives. DSTBA’s core strength

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Kalsey, Kiratpal Singh. "Vulnerability and Malware Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 113–18. http://dx.doi.org/10.22214/ijraset.2024.64807.

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The increasingly linked digital world oftoday has made cybersecurity a top priority. The rise in cyber dangers, notably vulnerabilities and malware, poses major risks to individuals, organizations, and governments. This review article offers a thorough analysis of the approachesand instruments currently in use for malware and vulnerability detection. It examines and contrasts several detection methods, such as machine learning techniques, static and dynamic analysis, and signature-based detection. The study also examines new developments in the sector, such as the application of big data analy

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Wadne, Vinod. "MOBILE BOTNET DETECTION: A Machine Learning Approach using SVM." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2292–96. https://doi.org/10.22214/ijraset.2025.67807.

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Mobile botnets have become a growing cybersecurity concern, leveraging infected Android devices for malicious activities such as DDoS attacks, phishing, and credential theft. Detecting these threats requires advanced techniques due to their evasive nature. This paper proposes a machine learning-based detection system utilizing Support Vector Machine (SVM) to classify applications as benign or malicious. The model extracts and analyses 342 static features from Android applications to identify anomalous behaviour. Evaluated in a real-world setting, the approach demonstrates high detection accura

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Wadne, Vinode. "Mobile BOTNET Detection: A Machine Learning Approach using SVM." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 1314–19. https://doi.org/10.22214/ijraset.2025.72385.

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Mobile botnets have become a growing cybersecurity concern, leveraging infected Android devices for malicious activities such as DDoS attacks, phishing, and credential theft. Detecting these threats requires advanced techniques due to their evasive nature. This paper proposes a machine learning-based detection system utilizing Support Vector Machine (SVM) to classify applications as benign or malicious. The model extracts and analyses 342 static features from Android applications to identify anomalous behaviour. Evaluated in a real-world setting, the approach demonstrates high detection accura

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Li, Jinfeng. "Vulnerabilities Mapping based on OWASP-SANS: A Survey for Static Application Security Testing (SAST)." Annals of Emerging Technologies in Computing 4, no. 3 (2020): 1–8. http://dx.doi.org/10.33166/aetic.2020.03.001.

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The delivery of a framework in place for secure application development is of real value for application development teams to integrate security into their development life cycle, especially when a mobile or web application moves past the scanning stage and focuses increasingly on the remediation or mitigation phase based on static application security testing (SAST). For the first time, to the author’s knowledge, the industry-standard Open Web Application Security Project (OWASP) top 10 vulnerabilities and CWE/SANS top 25 most dangerous software errors are synced up in a matrix with Checkmarx

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Elrowayati, Ali, and Ammar Fadeel. "SAST Tools and Manual Testing to Improve the Methodology of Vulnerability Detection in Web Applications." International Journal of Engineering & Information Technology (IJEIT) 12, no. 1 (2024): 79–83. http://dx.doi.org/10.36602/ijeit.v12i1.480.

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Statically analyzing code during development is a common process of the development process, using Static Application Security Testing tools. SAST analyzes code without its execution and is also very fast compared to dynamic means and therefore focuses on a certain program part. However, the results of static analysis tools are not always accurate, either missing vulnerabilities or reporting false positives. This paper considers an evaluation of several SAST tools and an analysis of student code samples with known vulnerabilities, comparing manual analysis with the results of SAST tools. The r

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Umar, Kabir, Abu Bakar Sultan, Hazura Zulzalil, Novia Admodisastro, and Mohd Taufik Abdullah. "Comparing Web Vulnerability Scanners with a New Method for SQL Injection Vulnerabilities Detection and Removal EPSQLiFix." International Journal of Engineering & Technology 7, no. 4.31 (2018): 40–45. http://dx.doi.org/10.14419/ijet.v7i4.31.23338.

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Web vulnerabilities have become a major threat to the security of information and services accessible via the internet. Dynamic analysis based Web Vulnerability Scanners (WVS) have been employed to facilitate detection of vulnerabilities, though, such scanners could not remove the detected vulnerabilities. Empirical evidences show that some existing static analysis techniques targeted both detection and removal of vulnerabilities. However, these techniques are not adequately effective â€“ they report considerably large number of false positives and do not achieve fully automatic vulnerabilitie

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Ayyarrappan, Mariappan. "AI for Automated Code Reviews and Quality Assurance." International Scientific Journal of Engineering and Management 03, no. 11 (2024): 1–9. https://doi.org/10.55041/isjem02159.

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Automated code reviews and continuous quality assurance are essential in modern software development. Yet, conventional static analysis tools often produce large volumes of warnings, failing to capture deeper structural or semantic flaws. With the rise of artificial intelligence (AI), novel solutions can now parse codebases and understand patterns beyond rule-based checks— reducing false positives, spotting anti-patterns, and offering guided refactoring suggestions. This paper discusses how AI techniques, such as language models and machine learning–based code analysis, can enhance automated c

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Onyedinma, Ebele G, Asogwa Doris C, and Onyenwe, Ikechukwu E. "Towards resilient malware detection: A hybrid framework leveraging static-dynamic features and ensemble models." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 634–39. https://doi.org/10.30574/wjaets.2025.15.3.0901.

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Malware continues to evolve in complexity, often evading traditional detection methods through obfuscation, polymorphism, and zero-day exploits. To address these challenges, this study proposes a Hybrid Malware Detection Framework that integrates signature-based detection, static analysis, dynamic behavioural monitoring, and ensemble machine learning. The framework extracts both static features such as metadata and API imports, and dynamic behaviour patterns like file system activity, process creation, and network access, which are processed into a unified vector for classification. Ensemble m

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Hu, Huimin, Yingying Wang, Julia Rubin, and Michael Pradel. "An Empirical Study of Suppressed Static Analysis Warnings." Proceedings of the ACM on Software Engineering 2, FSE (2025): 290–311. https://doi.org/10.1145/3715729.

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Scalable static analyzers are popular tools for finding incorrect, inefficient, insecure, and hard-to-maintain code early during the development process. Because not all warnings reported by a static analyzer are immediately useful to developers, many static analyzers provide a way to suppress warnings, e.g., in the form of special comments added into the code. Such suppressions are an important mechanism at the interface between static analyzers and software developers, but little is currently known about them. This paper presents the first in-depth empirical study of suppressions of static a

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Olabode, Olanrewaju O., and Adebola K. Ojo. "Enhancing the Detection of Debit Card Fraud Detection Using Logistic Regression and Random Forest Techniques." Journal of Advances in Mathematics and Computer Science 39, no. 10 (2024): 74–83. http://dx.doi.org/10.9734/jamcs/2024/v39i101936.

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Debit card fraud is one of the major financial crimes globally, causing a very great financial losses for financial institutions and individuals. The traditional mode of fraud detection systems often struggles to keep with the latest change in fraud patterns, due to the dynamism of the criminals resulting in high rates of false positives. This project proposes an improved system based on machine learning models to accurately and effectively identify fraudulent transactions. With machine learning models, fraudulent activities can be monitored and identified in real time. It is able to adapt to

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Sun, Hao, Chao Su, Yue Wang, and Qingkai Zeng. "Improving the Accuracy of Integer Signedness Error Detection Using Data Flow Analysis." International Journal of Software Engineering and Knowledge Engineering 25, no. 09n10 (2015): 1573–93. http://dx.doi.org/10.1142/s0218194015400331.

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Integer signedness errors can be exploited by adversaries to cause severe damages to computer systems. Despite the significant advances in automating the detection of integer signedness errors, accurately differentiating exploitable and harmful signedness errors from unharmful ones is an important challenge. In this paper, we present the design and implementation of SignFlow, an instrumentation-based integer signedness error detector to reduce the reports for unharmful signedness errors. SignFlow first utilizes static data flow analysis to identify unharmful integer sign conversions from the v

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Yenti, Sekar Olivia, Maison Maison, and Syaiful Syaiful. "Diagnosis Miskonsepsi Siswa MAN Insan Cendekia Jambi Pada Materi Fluida Statis dan Fluida Dinamis." Edu-Sains: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam 10, no. 1 (2022): 13–18. http://dx.doi.org/10.22437/jmpmipa.v10i1.20695.

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Students' efforts in studying physics often encounter obstacles due to the assumption that physics is a difficult subject to understand and teaching physics tends to be less interesting, causing misconceptions. This study aims to identify the misconceptions experienced by students in static fluid and dynamic fluid materials through four-tier (four-tier) diagnostics. This study uses a quantitative description method, with respondents as many as 30 students of class XI MIA MAN Insan Cendekia Jambi. The results showed that the average misconception experienced by students was classified in the lo

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Nandeesha H D, Prasanna B T. "Optimized Data Processing and Genetic Algorithm based Feature Selection Method to Detect URL Phishing Attacks Using Reinforcement Learning." Journal of Information Systems Engineering and Management 10, no. 18s (2025): 204–12. https://doi.org/10.52783/jisem.v10i18s.2904.

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Phishing is one of the major, continuously evolving cyber threats. Traditional approaches include Static Blacklist Filtering and Signature- Based Detection, suffering from a high rate of false positives and very limited adaptability to new phishing methods. To that end, RL-UPD proposes Dynamic Reinforcement Learning for Phishing Detection DRLPD and enhances data processing and feature selection through genetic algorithm while constantly updating detection parameters at runtime by reinforcement learning method. DRLPD learns new phishing trends, which raises the detection precision by 0.25%, cut

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Muske, Tukaram, and Alexander Serebrenik. "Survey of Approaches for Postprocessing of Static Analysis Alarms." ACM Computing Surveys 55, no. 3 (2023): 1–39. http://dx.doi.org/10.1145/3494521.

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Static analysis tools have showcased their importance and usefulness in automated detection of defects. However, the tools are known to generate a large number of alarms which are warning messages to the user. The large number of alarms and cost incurred by their manual inspection have been identified as two major reasons for underuse of the tools in practice. To address these concerns plentitude of studies propose postprocessing of alarms: processing the alarms after they are generated. These studies differ greatly in their approaches to postprocess alarms. A comprehensive overview of the pos

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Dr., Suman Thapaliya, and Ravi Chandra Gurung Mr. "Mitigating Insider Threats and Data Breaches: Enhancing Data Loss Prevention Systems with Behavioral Analytics And NLP." International Journal of Multidisciplinary and Innovative Research 02, no. 01 (2025): 22–27. https://doi.org/10.5281/zenodo.14634504.

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Insider threats and data breaches pose significant challenges to modern organizations, leading to substantial financial, reputational, and operational damage. Traditional Data Loss Prevention (DLP) systems, which rely on static rule-based mechanisms and keyword-based detection, often fail to address the complexities of evolving insider threats. Such systems struggle to detect subtle behavioral anomalies or obfuscated data exfiltration, leading to high false positives and overlooked malicious activities. This paper explores the integration of Behavioral Analytics and Natural Language Processing

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Ramaswamy, Mithilesh. "Early Detection of Hard-Coded Secrets in Software Development: A Multi-Method Approach Integrating Static Analysis, Entropy-Based Detection, and Machine Learning." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–6. https://doi.org/10.55041/isjem0411.

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The inadvertent inclusion of hard-coded secrets—such as API keys, passwords, and tokens—within source code poses significant security risks, potentially leading to unauthorized access and data breaches. Recent studies have highlighted the prevalence of this issue; for instance, GitGuardian's 2023 report revealed a 67% increase in detected hard-coded secrets compared to the previous year, with 10 million new secrets discovered in public GitHub commits in 2022. To address this escalating concern, our research proposes a comprehensive, multi-method approach that integrates static code analysis, e

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Maddula, Sai Sirisha. "Evaluating Current Techniques for Detecting Vulnerabilities in Ethereum Smart Contracts." Engineering International 11, no. 1 (2023): 59–72. http://dx.doi.org/10.18034/ei.v11i1.717.

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Ethereum intelligent contract security must be guaranteed since these decentralized apps oversee large-scale financial transactions independently. To strengthen the dependability and credibility of Ethereum smart contracts, this paper assesses existing methods for finding weaknesses in them. The primary goals are to evaluate how well hybrid approaches, formal verification, dynamic analysis, and static analysis find vulnerabilities. Methodologically, a thorough assessment of available resources and instruments was carried out to evaluate the advantages and disadvantages of each approach. Import

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Li, Kaixuan, Yue Xue, Sen Chen, et al. "Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We?" Proceedings of the ACM on Software Engineering 1, FSE (2024): 1447–70. http://dx.doi.org/10.1145/3660772.

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In recent years, the importance of smart contract security has been heightened by the increasing number of attacks against them. To address this issue, a multitude of static application security testing (SAST) tools have been proposed for detecting vulnerabilities in smart contracts. However, objectively comparing these tools to determine their effectiveness remains challenging. Existing studies often fall short due to the taxonomies and benchmarks only covering a coarse and potentially outdated set of vulnerability types, which leads to evaluations that are not entirely comprehensive and may

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Jin, Lin, Shuai Hao, Haining Wang, and Chase Cotton. "Understanding the Practices of Global Censorship through Accurate, End-to-End Measurements." ACM SIGMETRICS Performance Evaluation Review 50, no. 1 (2022): 17–18. http://dx.doi.org/10.1145/3547353.3522640.

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It is challenging to conduct a large scale Internet censorship measurement, as it involves triggering censors through artificial requests and identifying abnormalities from corresponding responses. Due to the lack of ground truth on the expected responses from legitimate services, previous studies typically require a heavy, unscalable manual inspection to identify false positives while still leaving false negatives undetected. In this paper, we propose Disguiser, a novel framework that enables end-to-end measurement to accurately detect the censorship activities and reveal the censor deploymen

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