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Journal articles on the topic 'False negative attacks'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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3

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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Nan, Haymarn Oo, Cahyadi Risdianto Aris, Chaw Ling Teck, and Htein Maw Aung. "Flooding Attack Detection and Mitigation in SDN with Modified Adaptive Threshold Algorithm." International Journal of Computer Networks & Communications (IJCNC) 12, no. 3 (2020): 75–95. https://doi.org/10.5281/zenodo.3946219.

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Flooding attack is a network attack that sends a large amount of traffic to the victim networks or services to cause denial-of-service. In Software-Defined Networking (SDN) environment, this attack might not only breach the hosts and services but also the SDN controller. Besides, it will also cause a disconnection of links between the controller and the switches. Thus, an effective detection and mitigation technique of flooding attacks is required. Statistical analysis techniques are widely used for the detection and mitigation of flooding attacks. However, the effectiveness of these technique

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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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Ajdani, Mahdi. "Deep Learning-Based Intrusion Detection Systems." International Journal of Information Security and Privacy 19, no. 1 (2025): 1–15. https://doi.org/10.4018/ijisp.383299.

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Given the increasing growth of cyber-attacks, the need for intrusion detection systems (IDS) with higher accuracy and efficiency is critical. This paper presents a novel approach using Generative Adversarial Networks (GANs) for intrusion detection. The proposed model leverages deep learning to extract complex features and uses GANs to generate synthetic data, improving IDS accuracy and efficiency. This approach reduces false positive and negative rates while increasing the accuracy of detecting unknown attacks. Experimental results on the NSL-KDD and CICIDS2017 datasets show 98.2% accuracy, a

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Akram, Muhammad, Muhammad Ashraf, and Tae Ho Cho. "Enhancing the Statistical Filtering Scheme to Detect False Negative Attacks in Sensor Networks." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 52. http://dx.doi.org/10.30537/sjcms.v1i1.7.

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In this paper, we present a technique that detects both false positive and false negative attacks in statistical filtering-based wireless sensor networks. In statistical filtering scheme, legitimate reports are repeatedly verified en route before they reach the base station, which causes heavy energy consumption. While the original statistical filtering scheme detects only false reports, our proposed method promises to detect both attacks.

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Dalvi, Sailee, Gilad Gressel, and Dr Krishnashree Achuthan. "Tuning the False Positive Rate / False Negative Rate with Phishing Detection Models." International Journal of Engineering and Advanced Technology 9, no. 1s5 (2019): 7–13. http://dx.doi.org/10.35940/ijeat.a1002.1291s519.

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Phishing attacks have risen by 209% in the last 10 years according to the Anti Phishing Working Group (APWG) statistics [19]. Machine learning is commonly used to detect phishing attacks. Researchers have traditionally judged phishing detection models with either accuracy or F1-scores, however in this paper we argue that a single metric alone will never correlate to a successful deployment of machine learning phishing detection model. This is because every machine learning model will have an inherent trade-off between it’s False Positive Rate (FPR) and False Negative Rate (FNR). Tuning the tra

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9

Oluwole Ogini, Nicholas, Wilfred Adigwe, and Noah Oghenefego Ogwara. "Distributed Denial of Service Attack Detection and Prevention Model for IoT based Computing Environment using Ensemble Machine Learning Approach." International Journal of Network Security & Its Applications 14, no. 4 (2022): 39–53. http://dx.doi.org/10.5121/ijnsa.2022.14403.

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Defending against Distributed Denial of Service (DDoS) in the Internet of Things (IoT) computing environment is a challenging task. DDoS attacks are type of collective attack in which attackers work together to compromise internet security and services. The resource-constrained devices used in IoT deployments have made it even easier for an attacker to break, because of the vast number of vulnerable IoT devices with significant compute power. This paper proposed an ensemble machine learning (ML) model using the bagging technique to detect and prevent DDoS attacks in the IoT computing environme

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Nicholas, Oluwole Ogini, Adigwe Wilfred, and Oghenefego Ogwara Noah. "DISTRIBUTED DENIAL OF SERVICE ATTACK DETECTION AND PREVENTION MODEL FOR IOTBASED COMPUTING ENVIRONMENT USING ENSEMBLE MACHINE LEARNING APPROACH." International Journal of Network Security & Its Applications (IJNSA) 14, no. 4 (2022): 39–53. https://doi.org/10.5281/zenodo.7002129.

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Defending against Distributed Denial of Service (DDoS) in the Internet of Things (IoT) computing environment is a challenging task. DDoS attacks are type of collective attack in which attackers work together to compromise internet security and services. The resource-constrained devices used in IoT deployments have made it even easier for an attacker to break, because of the vast number of vulnerable IoT devices with significant compute power. This paper proposed an ensemble machine learning (ML) model using the bagging technique to detect and prevent DDoS attacks in the IoT computing environme

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11

Shahriar, Hossain, and Hisham M. Haddad. "Client-Side Detection of Clickjacking Attacks." International Journal of Information Security and Privacy 9, no. 1 (2015): 1–25. http://dx.doi.org/10.4018/ijisp.2015010101.

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Clickjacking attacks are emerging threat for web application users where click operations performed by victims lead to security breaches such as compromising webcams and posting unintended messages. Effective client-side defense technique could prevent the possible victims. This paper presents a client side approach to detect clickjacking attacks. The authors' approach examines web page requests and responses; the proposed approach is designed to detect advanced attack types such as cursorjacking, double click, and history object-based attacks. They evaluate the proposed approach with a set of

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12

Perumal, Rajakumar, Tamilarasi Karuppiah, Uppiliraja Panneerselvam, Venkatesan Annamalai, and Prabu Kaliyaperumal. "Enhancing network security using unsupervised learning approach to combat zero-day attack." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 1284. http://dx.doi.org/10.11591/ijeecs.v36.i2.pp1284-1293.

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<span lang="EN-US">Machine learning (ML) and advanced neural network methodologies like deep learning (DL) techniques have been increasingly utilized in developing intrusion detection systems (IDS). However, the growing quantity and diversity of cyber-attacks pose a significant challenge for IDS solutions reliant on historical attack signatures. This highlights the industry's need for resilient IDSs that can identify zero-day attacks. Current studies focusing on outlier-based zero-day detection are hindered by elevated false-negative rates, thereby constraining their practical efficacy.

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Rajakumar, Perumal Tamilarasi Karuppiah Uppiliraja Panneerselvam Venkatesan Annamalai Prabu Kaliyaperumal. "Enhancing network security using unsupervised learning approach to combat zero-day attack." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 1284–93. https://doi.org/10.11591/ijeecs.v36.i2.pp1284-1293.

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Machine learning (ML) and advanced neural network methodologies like deep learning (DL) techniques have been increasingly utilized in developing intrusion detection systems (IDS). However, the growing quantity and diversity of cyber-attacks pose a significant challenge for IDS solutions reliant on historical attack signatures. This highlights the industry's need for resilient IDSs that can identify zero-day attacks. Current studies focusing on outlier-based zero-day detection are hindered by elevated false-negative rates, thereby constraining their practical efficacy. This paper suggests utili

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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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Macek, Nemanja, and Milan Milosavljevic. "Reducing U2R and R2L category false negative rates with support vector machines." Serbian Journal of Electrical Engineering 11, no. 1 (2014): 175–88. http://dx.doi.org/10.2298/sjee131007015m.

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The KDD Cup '99 is commonly used dataset for training and testing IDS machine learning algorithms. Some of the major downsides of the dataset are the distribution and the proportions of U2R and R2L instances, which represent the most dangerous attack types, as well as the existence of R2L attack instances identical to normal traffic. This enforces minor category detection complexity and causes problems while building a machine learning model capable of detecting these attacks with sufficiently low false negative rate. This paper presents a new support vector machine based intrusion detection s

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Slamet and Ibrahim Mohamed Abdelaziz Izzeldin. "An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 2 (2022): 1018~1025. https://doi.org/10.11591/eei.v11i2.3308.

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Currently, many companies use data mining for various implementations. One form of implementation is intrusion detection system (IDS). In IDS, the main problem for nuisance network administrators in detecting attacks is false alerts. Regardless of the methods implemented by this system, eliminating false alerts is still a huge problem. To describe data traffic passing through the network, a database of the network security layer (NSL) knowledge discovery in database (KDD) dataset is used. The massive traffic of data sent over the network contains excessive and duplicated amounts of information

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17

KUMAR, T. RAVI KIRAN, A. KAMAL NAYAN, B. UDAY KIRAN, S. PRANEETH REDDY, and K. SHIVA KUMAR. "A MULTI-LAYERED DEFENCE STRATEGY AGAINST DDOS ATTACKS." Journal of Nonlinear Analysis and Optimization 15, no. 02 (2024): 665–73. https://doi.org/10.36893/jnao.2024.v15i2.116.

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The absence of standards and the diverse nature of the Internet of Things (IoT) have made security and privacy concerns more acute. Attacks such as distributed denial of service (DDoS) are becoming increasingly widespread in IoT, and the need for ways to stop them is growing. The use of newly formed Software-Defined Networking (SDN) significantly lowers the computational burden on IoT network nodes and makes it possible to perform more security measurements. This paper proposes an SDN-based, four module DDoS attack detection and mitigation framework for IoT networks called FMDADM. The proposed

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18

Slamet, Slamet, and Izzeldin Ibrahim Mohamed Abdelaziz. "An enhanced classification framework for intrusions detection system using intelligent exoplanet atmospheric retrieval algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 2 (2022): 1018–25. http://dx.doi.org/10.11591/eei.v11i2.3308.

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Currently, many companies use data mining for various implementations. One form of implementation is intrusion detection system (IDS). In IDS, the main problem for nuisance network administrators in detecting attacks is false alerts. Regardless of the methods implemented by this system, eliminating false alerts is still a huge problem. To describe data traffic passing through the network, a database of the network security layer (NSL) knowledge discovery in database (KDD) dataset is used. The massive traffic of data sent over the network contains excessive and duplicated amounts of information

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19

Hindy, Hanan, Robert Atkinson, Christos Tachtatzis, Jean-Noël Colin, Ethan Bayne, and Xavier Bellekens. "Utilising Deep Learning Techniques for Effective Zero-Day Attack Detection." Electronics 9, no. 10 (2020): 1684. http://dx.doi.org/10.3390/electronics9101684.

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Machine Learning (ML) and Deep Learning (DL) have been used for building Intrusion Detection Systems (IDS). The increase in both the number and sheer variety of new cyber-attacks poses a tremendous challenge for IDS solutions that rely on a database of historical attack signatures. Therefore, the industrial pull for robust IDSs that are capable of flagging zero-day attacks is growing. Current outlier-based zero-day detection research suffers from high false-negative rates, thus limiting their practical use and performance. This paper proposes an autoencoder implementation for detecting zero-da

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20

Lakshmi, Vimitha R. Vidhya. "VADIA-Verkle Tree-based Approach for Dealing Data Integrity Attacks in Opportunistic Mobile Social Networks." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 15, no. 1 (2024): 154–71. http://dx.doi.org/10.58346/jowua.2024.i1.011.

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Opportunistic Mobile Social Networks (OMSN) are prone to data integrity attacks that jeopardize the integrity of the routing data inside the network. Among the several techniques that cope with these attacks in OMSN, tree-based approaches have proven to be the most effective due to its ease of data verification and ensurance in data integrity. This paper evaluates two tree-based data structures, Merkle tree and Verkle tree in terms of their effectiveness in detecting and preventing such attacks. The evaluation considers tree-generation time and proof-checking time, and the results demonstrate

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Kulbir, Kaur Sandhu. "A NOVEL APPROACH FOR IMAGE IMPAINTING USING ADAPTIVE SVM." GLOBAL JOURNAL OF ADVANCED ENGINEERING TECHNOLOGIES AND SCIENCES 8, no. 2 (2021): 17–25. https://doi.org/10.5281/zenodo.4568089.

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Data security is critical for most businesses and even home computer users. As, all the sensitive and important information is manipulated online. Therefore, it is essential to protect the data from intruders. We need to find the best ways to protect our system from different types of attacks.Intrusions refer to the network attacks against vulnerable services, data-driven attacks on applications, host-based attacks like privilege escalation, unauthorized logins and access to sensitive files, or malware like viruses, worms and Trojan horses. The problem in current IDS is the high false positive

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Akinyelu, Andronicus A., and Aderemi O. Adewumi. "Classification of Phishing Email Using Random Forest Machine Learning Technique." Journal of Applied Mathematics 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/425731.

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Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved p

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Alani, Mohammed M., and Ernesto Damiani. "XRecon: An Explainbale IoT Reconnaissance Attack Detection System Based on Ensemble Learning." Sensors 23, no. 11 (2023): 5298. http://dx.doi.org/10.3390/s23115298.

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IoT devices have grown in popularity in recent years. Statistics show that the number of online IoT devices exceeded 35 billion in 2022. This rapid growth in adoption made these devices an obvious target for malicious actors. Attacks such as botnets and malware injection usually start with a phase of reconnaissance to gather information about the target IoT device before exploitation. In this paper, we introduce a machine-learning-based detection system for reconnaissance attacks based on an explainable ensemble model. Our proposed system aims to detect scanning and reconnaissance activity of

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Priambodo, Dimas Febriyan, Achmad Husein Noor Faizi, Fika Dwi Rahmawati, Septia Ulfa Sunaringtyas, Jeckson Sidabutar, and Tiyas Yulita. "Collaborative Intrusion Detection System with Snort Machine Learning Plugin." JOIV : International Journal on Informatics Visualization 8, no. 3 (2024): 1230. http://dx.doi.org/10.62527/joiv.8.3.2018.

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The increasing prevalence of cybercrime and cyber-attacks underscores the imperative need for organizations to implement robust network security measures. Nevertheless, current Intrusion Detection Systems (IDS) often rely on single-sensor or multi-sensor in the same type of IDS, including Host-Based IDS (HIDS) or Network-Based IDS (NIDS), which inherently possess limited detection capabilities. To address this limitation, this research combines NIDS and HIDS components into a collaborative-IDS system, thus expanding the scope of intrusion detection and enhancing the efficacy of the established

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Cho, Tae Ho, and Su Man Nam. "MODELING AND SIMULATION OF THRESHOLD ANALYSIS FOR PVFS IN WIRELESS SENSOR NETWORKS." International Journal of Research -GRANTHAALAYAH 4, no. 8 (2016): 1–10. http://dx.doi.org/10.29121/granthaalayah.v4.i8.2016.2555.

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Wireless sensor networks (WSNs) suffer serious damage from false positive and negative attacks due to their hardware restrictions. The sensor network causes both unnecessary energy consumption and information loss through false reports and normal reports, which include false message authentication codes (MACs). A probabilistic voting-based filtering scheme (PVFS) effectively detects the two types of attacks through a pre-defined threshold, which is the number of detected false MACs in a report. Since the threshold significantly influences the ability to detect attacks, the sensor network shoul

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Man Nam, Su, and Youn Kyoung Seo. "A Method of Improving Energy Efficiency Through Geofencing and False Data Blocking In Context-Aware Architecture for Probabilistic Voting-Based Filtering Scheme of WSNs." International journal of Computer Networks & Communications 13, no. 3 (2021): 97–107. http://dx.doi.org/10.5121/ijcnc.2021.13306.

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In wireless sensor networks, sensor nodes have the disadvantage of being vulnerable to several attacks due to the use of wireless communication and constrained energy. Adversaries exploit vulnerable characteristics of these nodes to capture them and generate false positive and false negative attacks. These attacks result in false alarms in a base station and information loss in intermediate nodes. A context-aware architecture for a probabilistic voting-based filtering scheme (CAA-PVFS) identifies compromised nodes that cause the damage. Although this method immediately detects the compromised

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Nam, Su Man, and Tae Ho Cho. "Discrete event simulation–based energy efficient path determination scheme for probabilistic voting–based filtering scheme in sensor networks." International Journal of Distributed Sensor Networks 16, no. 8 (2020): 155014772094913. http://dx.doi.org/10.1177/1550147720949134.

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In wireless sensor networks, sensors are extremely vulnerable to false positive and false negative attacks due to their stringent energy and computational constraints. Several en-route filtering schemes mainly focus on saving energy through early detection of false data within a short distance against these attacks; however, they cannot immediately block the false data injected by compromised nodes. A security scheme uses context-aware architecture for a probabilistic voting–based filtering scheme to detect the compromised nodes and block the injection of false data, unlike security protocols.

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Karthik, M., and M. Krishnan. "Securing an Internet of Things from Distributed Denial of Service and Mirai Botnet Attacks Using a Novel Hybrid Detection and Mitigation Mechanism." International Journal of Intelligent Engineering and Systems 14, no. 1 (2021): 113–23. http://dx.doi.org/10.22266/ijies2021.0228.12.

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Internet of Things (IoT) has become more familiar in all applications and industrial fields such as medical, military, transportation, etc. It has some limitations because of the attack model in the transmission or communication channel. Moreover, one of the deadliest attacks is known as a Distributed Denial of Service Attack (DDoS). The Presence of DDoS in network layer cause huge damage in data transmission channel that ends in data loss or collapse. To address this issue the current research focused on an innovative detection and mitigation of Mirai and DDoS attack in IoT environment. Initi

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Khan, Inam Ullah, Asrin Abdollahi, Ryan Alturki, et al. "Intelligent Detection System Enabled Attack Probability Using Markov Chain in Aerial Networks." Wireless Communications and Mobile Computing 2021 (September 9, 2021): 1–9. http://dx.doi.org/10.1155/2021/1542657.

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The Internet of Things (IoT) plays an important role to connect people, data, processes, and things. From linked supply chains to big data produced by a large number of IoT devices to industrial control systems where cybersecurity has become a critical problem in IoT-powered systems. Denial of Service (DoS), distributed denial of service (DDoS), and ping of death attacks are significant threats to flying networks. This paper presents an intrusion detection system (IDS) based on attack probability using the Markov chain to detect flooding attacks. While the paper includes buffer queue length by

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AL-Adaileh, Mohammad A., Mohammed Anbar, Yung-Wey Chong, and Ahmed Al-Ani. "Proposed statistical-based approach for detecting distribute denial of service against the controller of software defined network (SADDCS)." MATEC Web of Conferences 218 (2018): 02012. http://dx.doi.org/10.1051/matecconf/201821802012.

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Software-defined networkings (SDNs) have grown rapidly in recent years be-cause of SDNs are widely used in managing large area networks and securing networks from Distributed Denial of Services (DDoS) attacks. SDNs allow net-works to be monitored and managed through centralized controller. Therefore, SDN controllers are considered as the brain of networks and are considerably vulnerable to DDoS attacks. Thus, SDN controller suffer from several challenges that exhaust network resources. For SDN controller, the main target of DDoS attacks is to prevent legitimate users from using a network resou

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Elshazly, Amr A., Islam Elgarhy, Ahmed T. Eltoukhy, et al. "False Data Injection Attacks on Reinforcement Learning-Based Charging Coordination in Smart Grids and a Countermeasure." Applied Sciences 14, no. 23 (2024): 10874. http://dx.doi.org/10.3390/app142310874.

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Reinforcement learning (RL) is proven effective in optimizing home battery charging coordination within smart grids. However, its vulnerability to adversarial behavior poses a significant challenge to the security and fairness of the charging process. In this study, we, first, craft five stealthy false data injection (FDI) attacks that under-report the state-of-charge (SoC) values to deceive the RL agent into prioritizing their charging requests, and then, we investigate the impact of these attacks on the charging coordination system. Our evaluations demonstrate that attackers can increase the

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Jawhar, Muna M. T., and Monica Mehrotra. "A Hybrid FCM Clustering- Neural Network Model for Intrusion Detection." Advanced Materials Research 403-408 (November 2011): 3519–27. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3519.

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Security has become an important issue for networks. Intrusion detection technology is an effective approach in dealing with the problems of network security. In this paper, we present an intrusion detection model based on hybrid fuzzy logic and neural network. The key idea is to take advantage of different classification abilities of fuzzy clustering and neural network for intrusion detection system. The new model has ability to recognize an attack, to differentiate one attack from another (i.e. classifying attacks), and the most important, to detect new attacks with high detection rate and l

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Aliyu, Ahmed Abubakar, Shamsuddeen Umaru Adamu, Abubakar Muazu Ahmed, and Ezekia Gilliard. "Blockchain-based Reputation System for Smart Farm Anomaly Detection and Prevention." Kasu Journal of Computer Science 1, no. 3 (2024): 593–606. http://dx.doi.org/10.47514/kjcs/2024.1.3.0014.

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While smart farming is becoming increasingly popular as a way to improve the efficiency and sustainability of agroecosystems and global food security, its vulnerability to cyber-attacks is rapidly increasing. A distributed reputation system can help detect and prevent malicious activities by tracking the reputation of IP addresses of devices, products, and entities in a smart farming system, thereby enhancing its security and trust. In this paper, we propose a blockchain-based reputation system for IP addresses in smart farms for real-time intrusion detection and prevention. The study uses the

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Akanji, Oluwatobi Shadrach, Opeyemi Aderiike Abisoye, and Mohammed Awwal Iliyasu. "MITIGATING SLOW HYPERTEXT TRANSFER PROTOCOL DISTRIBUTED DENIAL OF SERVICE ATTACKS IN SOFTWARE DEFINED NETWORKS." Journal of Information and Communication Technology 20, Number 3 (2021): 277–304. http://dx.doi.org/10.32890/jict2021.20.3.1.

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Distributed Denial of Service (DDoS) attacks has been one of the persistent forms of attacks on information technology infrastructure connected to public networks due to the ease of access to DDoS attack tools. Researchers have been able to develop several techniques to curb volumetric DDoS which overwhelms the target with a large number of request packets. However, compared to slow DDoS, limited number of research has been executed on mitigating slow DDoS. Attackers have resorted to slow DDoS because it mimics the behaviour of a slow legitimate client thereby causing service unavailability. T

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Zhou, Lu, Mingchao Liao, Cao Yuan, and Haoyu Zhang. "Low-Rate DDoS Attack Detection Using Expectation of Packet Size." Security and Communication Networks 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/3691629.

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Low-rate Distributed Denial-of-Service (low-rate DDoS) attacks are a new challenge to cyberspace, as the attackers send a large amount of attack packets similar to normal traffic, to throttle legitimate flows. In this paper, we propose a measurement—expectation of packet size—that is based on the distribution difference of the packet size to distinguish two typical low-rate DDoS attacks, the constant attack and the pulsing attack, from legitimate traffic. The experimental results, obtained using a series of real datasets with different times and different tolerance factors, are presented to de

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Abu Al-Haija, Qasem, and Saleh Zein-Sabatto. "An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks." Electronics 9, no. 12 (2020): 2152. http://dx.doi.org/10.3390/electronics9122152.

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With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, the Internet of Things (IoT) has earned wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack that tends to be treated as normal traffic through t

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Bold, Robert, Haider Al-Khateeb, and Nikolaos Ersotelos. "Reducing False Negatives in Ransomware Detection: A Critical Evaluation of Machine Learning Algorithms." Applied Sciences 12, no. 24 (2022): 12941. http://dx.doi.org/10.3390/app122412941.

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Technological achievement and cybercriminal methodology are two parallel growing paths; protocols such as Tor and i2p (designed to offer confidentiality and anonymity) are being utilised to run ransomware companies operating under a Ransomware as a Service (RaaS) model. RaaS enables criminals with a limited technical ability to launch ransomware attacks. Several recent high-profile cases, such as the Colonial Pipeline attack and JBS Foods, involved forcing companies to pay enormous amounts of ransom money, indicating the difficulty for organisations of recovering from these attacks using tradi

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Khoeurt, Sopha, Chakchai So-In, Pakarat Musikawan, and Phet Aimtongkham. "Multidirectional Trust-Based Security Mechanisms for Sinkhole Attack Detection in the RPL Routing Protocol for Internet of Things." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 14, no. 3 (2023): 48–76. http://dx.doi.org/10.58346/jowua.2023.i3.005.

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The Internet of Things (IoT) has gained popularity in recent years by connecting physical objects to the Internet, enabling innovative applications. To facilitate communication in low-power and lossy networks (LLNs), the IPv6-based routing protocol for LLNs (RPL) is widely used. However, RPL’s lack of specified security models makes it vulnerable to security threats, particularly sinkhole attacks. Existing sinkhole attack detection techniques suffer from high detection delays and false positives. To overcome these limitations, in our research we propose a multidirectional trust-based detection

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Tae, Ho Cho, Man Nam Su, and K. Shahzad Muhammad. "GAFS: GENETIC ALGORITHM-BASED FILTERING SCHEME FOR IMPROVING DETECTION POWER IN SENSOR NETWORKS." International Journal of Research – Granthaalayah 3, no. 12 (2017): 100–116. https://doi.org/10.5281/zenodo.848950.

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Wireless sensor networks (WSNs) have stringent energy and computational requirements. Security has become very crucial issue with the widespread acceptance of the WSNs in numerous decision-critical and hostile environments. Since sensor nodes are left unattended, they can be compromised by adversaries to launch various application layer attacks. Effective countermeasures against these attacks can lead to improved security. A probabilistic votingbased filtering scheme (PVFS) uses probabilistic filtering based on the distance to counter attacks of fabricated reports with false votes and real rep

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Aineyoona, Patrick. "A MACHINE LEARNING ALGORITHM WITH SELF-UPDATE PARAMETER CALIBRATION TO IMPROVE INTRUSION DETECTION OF DDOS IN COMMUNICATION NETWORKS." International Journal of Engineering Applied Sciences and Technology 6, no. 6 (2021): 72–79. http://dx.doi.org/10.33564/ijeast.2021.v06i06.008.

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Currently DDoS attack has become one of the most common network attacks worldwide. This is largely due to the fact that we live in the age of the Internet of Things, with the rapid development of computer and communication technology evolving into big, complex and distributed systems that are exposed to several kinds of attacks in addition to new threats. In order to detect intruders in an efficient and timely manner, a real time detection mechanism, proficient in dealing with a variety of forms of attacks is highly important. However, due to the uniformity and evolution of DDoS attack modes a

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Hamel, Jennifer A., and Reginald B. Cocroft. "Negative feedback from maternal signals reduces false alarms by collectively signalling offspring." Proceedings of the Royal Society B: Biological Sciences 279, no. 1743 (2012): 3820–26. http://dx.doi.org/10.1098/rspb.2012.1181.

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Within animal groups, individuals can learn of a predator's approach by attending to the behaviour of others. This use of social information increases an individual's perceptual range, but can also lead to the propagation of false alarms. Error copying is especially likely in species that signal collectively, because the coordination required for collective displays relies heavily on social information. Recent evidence suggests that collective behaviour in animals is, in part, regulated by negative feedback. Negative feedback may reduce false alarms by collectively signalling animals, but this

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P, Calduwel Newton, and Jabez Samuel F. "Secured Technique to Detect and Avoid Malicious Nodes in Internet of Things." Indian Journal of Science and Technology 15, no. 44 (2022): 2363–74. https://doi.org/10.17485/IJST/v15i44.1387.

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Abstract <strong>Objectives:</strong> To detect rank attacks during topology establishment and updated the RPL Destination Oriented Directed Acyclic Graph (DODAG) formation algorithm. The algorithm’s distributed module runs across all participating nodes, while the centralized module runs in the sink. <strong>Methods:</strong> The integrity and authenticity of control messages transmitted among two nodes and the sink are verified using a lightweight Hashed Message Authentication Code - Light-weight One-way Cryptographic Hash Algorithm (HMAC-LOCHA). The Secured Technique to

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Ramdane, Chikh, and Salim Chikhi. "A New Negative Selection Algorithm for Adaptive Network Intrusion Detection System." International Journal of Information Security and Privacy 8, no. 4 (2014): 1–25. http://dx.doi.org/10.4018/ijisp.2014100101.

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Negative Selection Algorithm (NSA) is one of the widely used techniques for Intrusion Detection Systems (IDS) designing. In this paper, the proposed is an IDS based on a new model of NSA namely HNSA-IDSA (Hybrid NSA for Intrusion Detection System Adaptation). The proposed system can detect unknown attacks; moreover can be adapted automatically when new profiles' changes of the system are detected. To determine the efficiency of the proposed approach, the standard KDD99 dataset was used for performing experiments. The obtained results show that the authors' mechanism outperforms some literature

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Alamri, Emtethal K., Abdullah M. Alnajim, and Suliman A. Alsuhibany. "Investigation of Using CAPTCHA Keystroke Dynamics to Enhance the Prevention of Phishing Attacks." Future Internet 14, no. 3 (2022): 82. http://dx.doi.org/10.3390/fi14030082.

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Phishing is a cybercrime that is increasing exponentially day by day. In phishing, a phisher employs social engineering and technology to misdirect victims towards revealing their personal information, which can then be exploited. Despite ongoing research to find effective anti-phishing solutions, phishing remains a serious security problem for Internet users. In this paper, an investigation of using CAPTCHA keystroke dynamics to enhance the prevention of phishing attacks was presented. A controlled laboratory experiment was conducted, with the results indicating the proposed approach as highl

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Ghugar, Umashankar, Jayaram Pradhan, Sourav Kumar Bhoi, and Rashmi Ranjan Sahoo. "LB-IDS: Securing Wireless Sensor Network Using Protocol Layer Trust-Based Intrusion Detection System." Journal of Computer Networks and Communications 2019 (January 6, 2019): 1–13. http://dx.doi.org/10.1155/2019/2054298.

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Wireless sensor network (WSN) faces severe security problems due to wireless communication between the nodes and open deployment of the nodes. The attacker disrupts the security parameters by launching attacks at different layers of the WSN. In this paper, a protocol layer trust-based intrusion detection system (LB-IDS) is proposed to secure the WSN by detecting the attackers at different layers. The trust value of a sensor node is calculated using the deviation of trust metrics at each layer with respect to the attacks. Mainly, we consider trustworthiness in the three layers such as physical

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Gupta, Shaveta, Dinesh Grover, and Abhinav Bhandari. "Vulnerability Analysis of Existing Distributed Denial of Service (DDoS) Defense Frameworks." Asian Journal of Computer Science and Technology 7, no. 3 (2018): 62–67. http://dx.doi.org/10.51983/ajcst-2018.7.3.1895.

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The world is quickly moving towards an era of digitization. Scope and volume of the network are becoming vast that makes these machines more prone to cyber-attack due to vulnerabilities of the network. There are various types of cyber-attacks but most common and equally impactful is DDOS attack. A foolproof defense mechanism is need of the hour. Analysis of various existing defense mechanism frameworks has been done and their shortcomings have been reported by us. This analysis will help to define a framework which can provide better accuracy, lesser detection time and reduced false negative a

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Abd, Nuha, Khattab M. Ali Alheeti, and Salah Sleibi Al-Rawi. "Intelligent Intrusion Detection System in Internal Communication Systems for Driverless Cars." Webology 17, no. 2 (2020): 376–93. http://dx.doi.org/10.14704/web/v17i2/web17039.

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The modern car is a complicated system consisting of Electronic Control Units (ECUs) with engines, detectors and wired and wireless communication protocols, that communicate through different types of intra-car networks. The cyber-physical design relies on this ECU network that has been susceptible to several kinds of attacks using wireless, internal and external access. The internal network contains several security vulnerabilities that make it possible to launch attacks via buses and propagation over the entire ECU network, therefore anomaly detection technology, which represents the securit

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Abuadlla, Yousef, Omran Ben Taher, and Hesham Elzentani. "Flow Based Intrusion Detection System Using Multistage Neural Network." مجلة الجامعة الأسمرية: العلوم التطبيقية 2, no. 2 (2017): 87–77. http://dx.doi.org/10.59743/aujas.v2i2.1158.

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With the rapid expansion of computer networks during the past decade, security has become a crucial issue for computer systems. And to keep security at highest level, there is an increasing need for effective security monitors such as Network Intrusion Detection System to prevent such illicit. In the recent years many researchers focus their hard work on this field using different approaches to build dependable intrusion detection systems. One of these approaches is Flow-based intrusion detection systems that rely on aggregated network traffic flows. In this paper, Multistage Neural Network in

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Aroon, Nut, Luke Kane, Vicky Liu, and Yuefeng Li. "Detection of TCP and MQTT-Based DoS/DDoS Attacks on MUD IoT Networks." Electronics 14, no. 8 (2025): 1653. https://doi.org/10.3390/electronics14081653.

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Mitigating cyberattacks on IoT networks is critical and remains a significant challenge, as such attacks can cause severe damage to the network systems and services. Moreover, the large volume of devices in IoT networks presents another challenge in managing security to reduce the risk of attacks. The Manufacturer Usage Description (MUD) is a standard for limiting attack risks on IoT networks. However, MUD has limitations, as it relies solely on pre-defined access control list (ACL) rules to allow permitted traffic and block unknown traffic. This can lead to false-negative filtering, where mal

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Ariffin, Sharifah H. S., Jia Le Chong, Nurul Mu’azzah Abdul Latif,, et al. "Intrusion Detection System (IDS) Accuracy Testing for Software Defined Network Internet of Things (SDN-IOT) Testbed." ELEKTRIKA- Journal of Electrical Engineering 21, no. 3 (2022): 23–27. http://dx.doi.org/10.11113/elektrika.v21n3.361.

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 Intrusion detection system (IDS) are considered as one of the best solutions for network security as it can detect intrusion and alert the network administrator on possible intrusions. However there are possible false alert that could cause unnecessary trigger of the network to the administrator. This paper provides a proof of concept of the accuracy test of an intrusion detection system (IDS) using software defined network IoT platform. The testbed uses UNSW-NB15 dataset that feeds the testbed and the traffic are mirror in a Ryu Controller that is installed with Snort IDS to monitor an

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