Relevant bibliographies by topics / False positive attacks

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'False positive attacks'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "False positive attacks"

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Su, Man Nam, and Ho Cho Tae. "A METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL ENROUTE FILTERING BASED WSNS." Advances in Vision Computing: An International Journal (AVC) 3, no. 3 (2016): 09–16. https://doi.org/10.5281/zenodo.3518809.

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In wireless sensor networks, adversaries compromise sensor nodes to damage the network though potential threats such as false positive and false negative attacks. The false positive attacks cause energy drain and false alarms, and false negative attacks generate information loss. To address the false positive attacks in the sensor network, a statistical en-route filtering (SEF) detects the false report in intermediate nodes. Even though the scheme detects the false report against the false positive attack, it is difficult to detect false MACs in a legitimate report against the false negative a
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Su, Man Nam, and Ho Cho Tae. "A METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL ENROUTE FILTERING BASED WSNS." Advances in Vision Computing: An International Journal (AVC) 3, no. 3 (2016): 09–16. https://doi.org/10.5281/zenodo.3689099.

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In wireless sensor networks, adversaries compromise sensor nodes to damage the network though potential threats such as false positive and false negative attacks. The false positive attacks cause energy drain and false alarms, and false negative attacks generate information loss. To address the false positive attacks in the sensor network, a statistical en-route filtering (SEF) detects the false report in intermediate nodes. Even though the scheme detects the false report against the false positive attack, it is difficult to detect false MACs in a legitimate report against the false negative a
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Shang, Yilun. "False Positive and False Negative Effects on Network Attacks." Journal of Statistical Physics 170, no. 1 (2017): 141–64. http://dx.doi.org/10.1007/s10955-017-1923-7.

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Wang, Chao, Yunxiao Sun, Wenting Wang, Hongri Liu, and Bailing Wang. "Hybrid Intrusion Detection System Based on Combination of Random Forest and Autoencoder." Symmetry 15, no. 3 (2023): 568. http://dx.doi.org/10.3390/sym15030568.

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To cope with the rising threats posed by network attacks, machine learning-based intrusion detection systems (IDSs) have been intensively researched. However, there are several issues that need to be addressed. It is difficult to deal with unknown attacks that do not appear in the training set, and as a result, poor detection rates are produced for these unknown attacks. Furthermore, IDSs suffer from high false positive rate. As different models learn data characteristics from different perspectives, in this work we propose a hybrid IDS which leverages both random forest (RF) and autoencoder (
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Bhavani, Yerram, Vinjamuri Janaki, and Rangu Sridevi. "IP Traceback using Flow Based Classification." Recent Advances in Computer Science and Communications 13, no. 3 (2020): 482–90. http://dx.doi.org/10.2174/2213275912666190328200635.

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Background:Distributed Denial of Service (DDoS) attack is a major threat over the internet. The IP traceback mechanism defends against DDoS attacks by tracing the path traversed by attack packets. The existing traceback techniques proposed till now are found with few short comings. The victim required many number of packets to trace the attack path. The requirement of a large number of packets resulted in more number of combinations and more false positives.Methods:To generate a unique value for the IP address of the routers in the attack path Chinese Remainder theorem is applied. This helped
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Shahriar, Hossain, Hisham Haddad, and Vamshee Krishna Devendran. "Request and Response Analysis Framework for Mitigating Clickjacking Attacks." International Journal of Secure Software Engineering 6, no. 3 (2015): 1–25. http://dx.doi.org/10.4018/ijsse.2015070101.

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This paper addresses the detection of clickjacking attacks, which is an emerging web application security issue. The authors propose a web application request and response page analysis framework to detect clickjacking attacks. Their framework considers not only inspects visual features related to frame, JavaScript code pattern in details to match with known attack signatures. The proposed approach is able to detect advanced clickjacking attacks such as cursorjacking, double click, and history object-based attacks. The authors evaluate the proposed approach with a set of legitimate and malicio
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Pulls, Tobias, and Rasmus Dahlberg. "Website Fingerprinting with Website Oracles." Proceedings on Privacy Enhancing Technologies 2020, no. 1 (2020): 235–55. http://dx.doi.org/10.2478/popets-2020-0013.

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AbstractWebsite Fingerprinting (WF) attacks are a subset of traffic analysis attacks where a local passive attacker attempts to infer which websites a target victim is visiting over an encrypted tunnel, such as the anonymity network Tor. We introduce the security notion of a Website Oracle (WO) that gives a WF attacker the capability to determine whether a particular monitored website was among the websites visited by Tor clients at the time of a victim’s trace. Our simulations show that combining a WO with a WF attack—which we refer to as a WF+WO attack—significantly reduces false positives f
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Zeinalpour, Alireza, and Hassan A. Ahmed. "Addressing the Effectiveness of DDoS-Attack Detection Methods Based on the Clustering Method Using an Ensemble Method." Electronics 11, no. 17 (2022): 2736. http://dx.doi.org/10.3390/electronics11172736.

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The curse of dimensionality, due to lots of network-traffic attributes, has a negative impact on machine learning algorithms in detecting distributed denial of service (DDoS) attacks. This study investigated whether adding the filter and wrapper methods, preceded by combined clustering algorithms using the Vote classifier method, was effective in lowering the false-positive rates of DDoS-attack detection methods. We examined this process to address the curse of dimensionality of machine learning algorithms in detecting DDoS attacks. The results of this study, using ANOVA statistical analyses,
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Wang, Wentao, Xuan Ke, and Lingxia Wang. "A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller." Future Internet 10, no. 9 (2018): 83. http://dx.doi.org/10.3390/fi10090083.

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A data center network is vulnerable to suffer from concealed low-rate distributed denial of service (L-DDoS) attacks because its data flow has the characteristics of data flow delay, diversity, and synchronization. Several studies have proposed addressing the detection of L-DDoS attacks, most of them are only detect L-DDoS attacks at a fixed rate. These methods cause low true positive and high false positive in detecting multi-rate L-DDoS attacks. Software defined network (SDN) is a new network architecture that can centrally control the network. We use an SDN controller to collect and analyze
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Jeong, Yeonseon, Hyunghoon Kim, and Hyo Jin Jo. "ASD: ARP Spoofing Detector Using OpenWrt." Security and Communication Networks 2022 (March 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/2196998.

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The address resolution protocol (ARP) is one of the most important communication protocols in a local area network (LAN). However, since there is no authentication procedure, the ARP is vulnerable to cyberattack such as ARP spoofing. Since ARP spoofing can be connected to critical attacks, including a man-in-the-middle (MITM) attack, detecting ARP spoofing initially without returning false-positive alarms is important. In general, however, existing works for ARP spoofing are unable to distinguish between ARP spoofing and connections from virtual machine (VM) guests, which results in false-posi
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Dissertations / Theses on the topic "False positive attacks"

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Shittu, Riyanat O. "Mining intrusion detection alert logs to minimise false positives & gain attack insight." Thesis, City University London, 2016. http://openaccess.city.ac.uk/14592/.

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Utilising Intrusion Detection System (IDS) logs in security event analysis is crucial in the process of assessing, measuring and understanding the security state of a computer network, often defined by its current exposure and resilience to network attacks. Thus, the study of understanding network attacks through event analysis is a fast growing emerging area. In comparison to its first appearance a decade ago, the complexities involved in achieving effective security event analysis have significantly increased. With such increased complexities, advances in security event analytical techniques
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Books on the topic "False positive attacks"

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Szmukler, George. The conventional grounds for involuntary treatment are highly problematic. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198801047.003.0003.

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Substantial problems attach to both of the fundamental criteria that need to be met for involuntary treatment in conventional mental health legislation—the presence of a ‘mental disorder’ and a risk of harm to self or others. The boundaries of ‘mental disorder’ are of necessity loosely drawn, with substantial blurring at the edges and contested views about where these should lie. ‘Values’—for example, when does ‘sadness’ become a ‘depressive illness’—play a significant role in determining when a diagnosis of a ‘disorder’ is warranted. Precision in the assessment of ‘risk’ is poor, especially f
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Book chapters on the topic "False positive attacks"

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Simone, Antonino, and Boris Škorić. "Asymptotically False-Positive-Maximizing Attack on Non-binary Tardos Codes." In Information Hiding. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24178-9_2.

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Wurzenberger, Markus, Max Landauer, Agron Bajraktari, and Florian Skopik. "Automatic Attack Pattern Mining for Generating Actionable CTI Applying Alert Aggregation." In Cybersecurity of Digital Service Chains. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04036-8_7.

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AbstractIntrusion Detection Systems (IDSs) monitor all kinds of IT infrastructures to automatically detect malicious activities related to cyber attacks. Unfortunately, especially anomaly-based IDS are known to produce large numbers of alerts, including false positives, that often become overwhelming for manual analysis. However, due to a fast changing threat landscape, quickly evolving attack techniques, and ever growing number of vulnerabilities, novel anomaly detection systems that enable detection of unknown attacks are indispensable. Therefore, to reduce the number of alerts that have to
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Cheng, Jie, Ru Zhang, Siyuan Tian, Bingjie Lin, Jiahui Wei, and Shulin Zhang. "Fusion of Traffic Data and Alert Log Based on Sensitive Information." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_9.

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AbstractAt present, the attack behavior that occurs in the network has gradually developed from a single-step, simple attack method to a complex multi-step attack method. Therefore, the researchers conducted a series of studies on this multi-step attack. Common methods usually use IDS to obtain network alert data as the data source, and then match a multi-step attack based on the correlation nature of the data. However, the false positives and omissions of the alert data based on IDS will lead to the failure of the resulting multi-step attack. Multi-source data is the basis of analysis and pre
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Hirakawa, Tetsuya, and Toyoo Takata. "The Trade-Off Between the False-Positive Ratio and the Attack Cost of Slow HTTP DoS." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57811-4_21.

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Hooper, Emmanuel. "Experimental Validation and Analysis of an Intelligent Detection and Response Strategy to False Positives and Network Attacks." In Intelligence and Security Informatics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760146_101.

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Burkart, Nadia, Maximilian Franz, and Marco F. Huber. "Explanation Framework for Intrusion Detection." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_9.

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AbstractMachine learning and deep learning are widely used in various applications to assist or even replace human reasoning. For instance, a machine learning based intrusion detection system (IDS) monitors a network for malicious activity or specific policy violations. We propose that IDSs should attach a sufficiently understandable report to each alert to allow the operator to review them more efficiently. This work aims at complementing an IDS by means of a framework to create explanations. The explanations support the human operator in understanding alerts and reveal potential false positi
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Junejo Khurum Nazir and Yau David. "Data Driven Physical Modelling For Intrusion Detection In Cyber Physical Systems." In Cryptology and Information Security Series. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-617-0-43.

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Cyber physical systems are critical to the infrastructure of a country. They are becoming more vulnerable to cyber attacks due to their use of off the shelf servers and industrial network protocols. Availability on World Wide Web for monitoring and reporting, has further aggravated their risk of being attacked. Once an attacker breaches the network security, he can affect the operations of the system which may even lead to a catastrophe. Mathematical and formal models try to detect the departure of the system from its expected behaviour but are difficult to build, and are sensitive to noise. F
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Thomas, Ciza, and N. Balakrishnan. "Usefulness of Sensor Fusion for Security Incident Analysis." In Situational Awareness in Computer Network Defense. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0104-8.ch010.

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Intrusion Detection Systems form an important component of network defense. Because of the heterogeneity of the attacks, it has not been possible to make a single Intrusion Detection System that is capable of detecting all types of attacks with acceptable levels of accuracy. In this chapter, the distinct advantage of sensor fusion over individual IDSs is proved. The detection rate and the false positive rate quantify the performance benefit obtained through the fixing of threshold bounds. Also, the more independent and distinct the attack space is for the individual IDSs, the better the fusion
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Laskov Pavel, Rieck Konrad, and Müller Klaus-Robert. "Machine Learning for Intrusion Detection." In NATO Science for Peace and Security Series - D: Information and Communication Security. IOS Press, 2008. https://doi.org/10.3233/978-1-58603-898-4-366.

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Detection of unknown attacks in network traffic is gaining increasing importance as modern attacks are characterized by high variabilities and mutation rates. Traditional signature-based intrusion detection systems (IDS) are not able to detect unknown attacks due to failing availability of appropriate signatures. We present an alternative approach based on machine learning techniques which enable automatic construction of profiles for normal packet payloads and detection of deviations thereof. Experimental evaluation of our approach showed a remarkable detection accuracy at low false positive
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Korba, Abdelaziz Amara, and Mohamed Amine Ferrag. "A Secure Routing Scheme Against Malicious Nodes in Ad Hoc Networks." In Security and Privacy in Smart Sensor Networks. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5736-4.ch013.

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This chapter proposes a new cluster-based secure routing scheme to detect and prevent intrusions in ad hoc networks. The proposed scheme combines both specification and anomaly detection techniques to provide an accurate detection of wide range of routing attacks. The proposed secure scheme provides an adaptive response mechanism to isolate malicious nodes from the network. A key advantage of the proposed secure scheme is its capacity to prevent wormhole and rushing attacks and its real-time detection of both known and unknown attacks which violate specification. The simulation results show th
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Conference papers on the topic "False positive attacks"

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Xu, Jincan, Mu Ma, Peng Wang, and Yaowei Sun. "Secure Control for Switched Positive Systems Against False Data Injection Attacks." In 2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, 2024. http://dx.doi.org/10.1109/yac63405.2024.10598494.

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Pitre, Priya, Arya Gandhi, Vaishnavi Konde, Rahul Adhao, and Vinod Pachghare. "An Intrusion Detection System for Zero-Day Attacks to Reduce False Positive Rates." In 2022 International Conference for Advancement in Technology (ICONAT). IEEE, 2022. http://dx.doi.org/10.1109/iconat53423.2022.9726105.

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Chi, Xiaoxiao, Xuyun Zhang, Yan Wang, et al. "Shadow-Free Membership Inference Attacks: Recommender Systems Are More Vulnerable Than You Thought." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/639.

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Recommender systems have been successfully applied in many applications. Nonetheless, recent studies demonstrate that recommender systems are vulnerable to membership inference attacks (MIAs), leading to the leakage of users’ membership privacy. However, existing MIAs relying on shadow training suffer a large performance drop when the attacker lacks knowledge of the training data distribution and the model architecture of the target recommender system. To better understand the privacy risks of recommender systems, we propose shadow-free MIAs that directly leverage a user’s recommendations for
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Schuartz, Fabio Cesar, Anelise Munaretto, and Mauro Fonseca. "Uma Comparação entre os Sistemas de Detecção de Ameaças Distribuídas de Rede Baseado no Processamento de Dados em Fluxo e em Lotes." In XXIV Workshop de Gerência e Operação de Redes e Serviços. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/wgrs.2019.7681.

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With the advancement of technology, allowing a greater massification of devices connected to the Internet of Things, there is a huge increase in the communication that circulates through the network, resulting in a growing number of vulnerability exploitations detected every year. Thus, faster and more accurate systems are needed to efficiently detect distributed denial of service attacks and port scans. This paper proposes a system for on-line detection of distributed network threats using data stream processing. The results obtained by the proposed system are compared with the results obtain
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Hodkhasa, Sandip, and Huiping Guo. "Forward Chaining and Self-Embedding Watermarking for Tamper Detection in a Continuous Stream of Data." In 8th International Conference on Artificial Intelligence and Fuzzy Logic System (AIFZ 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121612.

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Watermarking is extensively used in various media for data transfer, content authentication and integrity. The continuous flow of data is always vulnerable to tamper. This research proposes a new watermarking scheme that detects tampering in a stream of data. The stream of data is dynamically divided into different sized groups using synchronization points. A computed watermark is embedded in each group by hashing the concatenating the current group and the next group. A secondary watermark is generated based on the current group that prevents tampering from any attacks in the current group. W
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Ghislain VLAVONOU, Davy, Isckyros Gangbo, Thierry Nsabimana, et al. "IDS with hybrid sampling technique: combination over and under-sampling technique and comparison with deep convolutional approach." In Intelligent Human Systems Integration (IHSI 2024) Integrating People and Intelligent Systems. AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004488.

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Digital is constantly evolving with the appearance of connected objects and on top of the popularization today of artificial intelligence. One of the direct inductions remains the excessive proliferation of various kinds of attacks in computer systems. Hackers exploit these vulnerabilities to break in and attack systems with increasingly complex attacks. The consequences of intrusions are destructive and ruinous for businesses and organizations such as electronic ransom ware, data alteration and loss, financial and brand image loss.It is important for those involved in computer systems to equi
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Hintersdorf, Dominik, Lukas Struppek, and Kristian Kersting. "To Trust or Not To Trust Prediction Scores for Membership Inference Attacks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/422.

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Membership inference attacks (MIAs) aim to determine whether a specific sample was used to train a predictive model. Knowing this may indeed lead to a privacy breach. Most MIAs, however, make use of the model's prediction scores - the probability of each output given some input - following the intuition that the trained model tends to behave differently on its training data. We argue that this is a fallacy for many modern deep network architectures. Consequently, MIAs will miserably fail since overconfidence leads to high false-positive rates not only on known domains but also on out-of-distri
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Sanz, Igor Jochem, and Otto Carlos Muniz Bandeira Duarte. "Graph-based Feature Enrichment for Online Intrusion Detection in Virtual Networks." In XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbrc_estendido.2019.7779.

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The ubiquitousness of Internet-of-Things devices paves the way for distributed network attacks at an unprecedented scale. Graph theory, strengthened by machine learning techniques, improves an automatic discovery of group behavior patterns of distributed network threats often omitted by traditional security systems. This dissertation proposes an intrusion detection system for online threat detection enriched by a graph-learning analysis. We develop a feature enrichment algorithm that infers metrics based on a graph analysis. By using different machine learning techniques, we evaluated our syst
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Nnaji, David, and Jeremy Daily. "Trucking Forward: Intrusion Detection for SAE J1708/J1587 Networks in Heavy-Duty Vehicles." In WCX SAE World Congress Experience. SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2805.

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<div class="section abstract"><div class="htmlview paragraph">Automotive researchers and industry experts have extensively documented vulnerabilities arising from unauthorized in-vehicle communication through academic research, industry investigations, sponsored events, and learnings from real-world attacks. While current cybersecurity endeavors in the heavy-duty (HD) vehicle space focus on securing conventional communication technologies such as the controller area network (CAN), there is a notable deficiency in defensive research concerning legacy technologies, particularly those
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Souza, Cristiano A. de, Carlos B. Westphall, Jean D. G. Valencio, Renato B. Machado, and Wesley dos R. Bezerra. "DNNET-Ensemble approach to detecting and identifying attacks in IoT environments." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbrc.2023.502.

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Special security techniques like intrusion detection mechanisms are indispensable in modern computer systems. It is important to detect and identify the attack in a category so that specific countermeasures for the threat category are solved. However, most existing multiclass detection approaches have some weaknesses, mainly related to detecting specific categories of attacks and problems with false positives. This article addresses this research problem and advances state-of-the-art, bringing contributions to a two-stage detection architecture called DNNET-Ensemble, combining binary and multi
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Reports on the topic "False positive attacks"

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Drury, J., S. Arias, T. Au-Yeung, et al. Public behaviour in response to perceived hostile threats: an evidence base and guide for practitioners and policymakers. University of Sussex, 2023. http://dx.doi.org/10.20919/vjvt7448.

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Background: Public behaviour and the new hostile threats • Civil contingencies planning and preparedness for hostile threats requires accurate and up to date knowledge about how the public might behave in relation to such incidents. Inaccurate understandings of public behaviour can lead to dangerous and counterproductive practices and policies. • There is consistent evidence across both hostile threats and other kinds of emergencies and disasters that significant numbers of those affected give each other support, cooperate, and otherwise interact socially within the incident itself. • In emerg
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