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Academic literature on the topic 'Man-in-the-middle-attack network scalability'
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Journal articles on the topic "Man-in-the-middle-attack network scalability"
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Yu, Wangke, Limeihui Yang, and Shuhua Wang. "New Lattice-Based Broadcast Authentication Protocol for Wireless Sensor Networks." Security and Communication Networks 2022 (September 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/6809875.
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At present, wireless sensor networks have become one of the indispensable infrastructures in people’s lives. Considering the frequent mobility of wireless sensor network nodes, an efficient lattice-based random broadcast authentication protocol is proposed. The security of the proposed protocol is based on the SIS problem which helps to resist against quantum attacks. In this paper, we propose two forms of broadcast authentication, that is, one-to-many broadcast authentication and one-to-one broadcast authentication. The proposed protocol satisfies the broadcast authentication property. Moreover, the resistance against man in the middle attack, antireplay attack, unforgeability, and scalability properties are achieved. Moreover, its efficiency has certain advantages with other lattice-based studies.
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Buzura, Sorin, Mihaiela Lehene, Bogdan Iancu, and Vasile Dadarlat. "An Extendable Software Architecture for Mitigating ARP Spoofing-Based Attacks in SDN Data Plane Layer." Electronics 11, no. 13 (2022): 1965. http://dx.doi.org/10.3390/electronics11131965.
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Software-defined networking (SDN) is an emerging network architecture that brings benefits in network function virtualization, performance, and scalability. However, the scalability feature also increases the number of possible vulnerabilities through multiple entry points in the network. Address Resolution Protocol (ARP) spoofing-based attacks are widely encountered and allow an attacker to assume the identity of a different computer, facilitating other attacks, such as Man in the Middle (MitM). In the SDN context, most solutions employ a controller to detect and mitigate attacks. However, interacting with the control plane involves asynchronous network communication, which causes delayed responses to an attack. The current work avoids these delays by being implemented solely in the data plane through extendable and customizable software architecture. Therefore, faster response times improve network reliability by automatically blocking attackers. As attacks can be generated with a variety of tools and in networks experiencing different traffic patterns, the current solution is created to allow flexibility and extensibility, which can be adapted depending on the running environment. Experiments were run performing ARP spoofing-based attacks using KaliLinux, Mininet, and OpenVSwitch. The presented results are based on traffic pattern analysis offering greater customization capabilities and insight compared to similar work in this area.
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Hassen, Shaho, and Ahmed Abdlrazaq. "Contextual Deep Semantic Feature Driven Multi-Types Network Intrusion Detection System for IoT-Edge Networks." Zanco Journal of Pure and Applied Sciences 36, no. 6 (2024): 132–47. https://doi.org/10.21271/zjpas.36.6.14.
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Recent years have witnessed an exponential rise in wireless networks and allied interoperable distributed computing frameworks, where the different sensory units transfer real-world event data to the network analyzer for run-time decisions. There exists an array of applications employing edge- internet of things (Edge-IoT) where the edge nodes collect local data to perform real-time decisions. However, the at-hand edge-IoT systems being decentralized, infrastructure-less, and dynamic remain vulnerable to man-in-the-middle attacks, intrusion, denial of service attacks, etc. Though in the past, numerous efforts were made towards intrusion detection in IoT networks, the major approaches focused merely on standalone intrusion detection, and therefore their scalability towards multiple attack detection remains unaddressed. On the contrary, applying a unit intrusion detection system for each type of attack can impose resource exhaustion and delay. Recently authors have used deep learning methods like convolutional neural network (CNN), and long- and short-term memory (LSTM) to perform learning-based intrusion detection. However, being reliant on merely local features its reliability remains suspicious. Such methods ignore long-term dependency problems that limit their efficacy in intrusion detection in temporal Edge-IoT network traffic. With this motivation, in this paper, a contextual deep semantic feature-driven multi-type intrusion detection model (CDS-MNIDS) is proposed for Edge-IoT networks. The proposed CDS-MNIDS model at first performs network traffic segmentation from the temporal network traces obtained from the network gateway. Subsequently, the node’s dynamic features including the node’s address, packet size, transmission behavior, etc., are processed for Word2Vec encoding, followed by a cascaded deep network-based learning and prediction. The CDS-MNIDS model embodied a cascaded deep network encompassing LSTM and bidirectional LSTM networks, where the first extracted local features. At the same time, the latter obtained contextual features from the input local feature vector. The extracted local and contextual features were projected to the global average pooling layer followed by the fully connected layer that in conjunction with the Softmax layer performed multi-class classification.
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Luo, Fuyuan, Tao Feng, and Lu Zheng. "Formal Security Evaluation and Improvement of Wireless HART Protocol in Industrial Wireless Network." Security and Communication Networks 2021 (November 23, 2021): 1–15. http://dx.doi.org/10.1155/2021/8090547.
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With the rapid development of wireless communication technology in the field of industrial control systems, Wireless HART is an international wireless standard, because of its low cost and strong scalability, as well as its wide range of applications in the industrial control field. However, it is more open communication so that the possibility of increased attacks by external. At present, there are many types of research on wireless protocol security at home and abroad, but they all focus on the realization of the security function of the protocol itself, which has certain limitations for the formal modeling of the protocol security assessment. Taking into account the aforementioned research status, this paper takes the Wireless HART protocol as the research object and adopts the model detection method combining eCK model theory and colored Petri net theory to evaluate and improve the security of the protocol. First, the colored Petri net theory and CPN Tools modeling tool were introduced to verify the consistency of the original model of the protocol. And the eCK model was used to evaluate the security of the original protocol model. It was found that the protocol has two types of man-in-the-middle attack vulnerabilities: tampering and deception. Aiming at the attack loopholes of the protocol, an improvement plan was proposed. After improving the original protocol, CPN Tools modeling tool was used for security verification. It was found that the new scheme improvement can effectively prevent the existing attacks and reasonably improve the security of the protocol.
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Alnaim, Abdulrahman K., and Ahmed M. Alwakeel. "Zero Trust Strategies for Cyber-Physical Systems in 6G Networks." Mathematics 13, no. 7 (2025): 1108. https://doi.org/10.3390/math13071108.
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This study proposes a Zero Trust security framework for 6G-enabled Cyber-Physical Systems (CPS), integrating Adaptive Access Control (AAC), end-to-end encryption, and blockchain to enhance security, scalability, and real-time threat detection. As 6G networks facilitate massive device connectivity and low-latency communication, traditional perimeter-based security models are inadequate against evolving cyber threats such as Man-in-the-Middle (MITM) attacks, Distributed Denial-of-Service (DDoS), and data breaches. Zero Trust security eliminates implicit trust by enforcing continuous authentication, strict access control, and real-time anomaly detection to mitigate potential threats dynamically. The proposed framework leverages blockchain technology to ensure tamper-proof data integrity and decentralized authentication, preventing unauthorized modifications to CPS data. Additionally, AI-driven anomaly detection identifies suspicious behavior in real time, optimizing security response mechanisms and reducing false positives. Experimental evaluations demonstrate a 40% reduction in MITM attack success rates, 5.8% improvement in authentication efficiency, and 63.5% lower latency compared to traditional security methods. The framework also achieves high scalability and energy efficiency, maintaining consistent throughput and response times across large-scale CPS deployments. These findings underscore the transformative potential of Zero Trust security in 6G-enabled CPS, particularly in mission-critical applications such as healthcare, smart infrastructure, and industrial automation. By integrating blockchain-based authentication, AI-powered threat detection, and adaptive access control, this research presents a scalable and resource-efficient solution for securing next-generation CPS architectures. Future work will explore quantum-safe cryptography and federated learning to further enhance security, ensuring long-term resilience in highly dynamic network environments.
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Ravindra, Krishnapura Srinivasa, and Malode Vishwanatha Panduranga Rao. "A novel secured open standard framework for internet of things applications integrating elliptic curve cryptography and fog computing." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 6 (2024): 7224. http://dx.doi.org/10.11591/ijece.v14i6.pp7224-7235.
Full textAbstract:
The internet of things (IoT) has revolutionized various fields by enabling seamless connectivity and data exchange among numerous devices. However, this interconnectivity introduces significant security challenges, particularly in ensuring data confidentiality, integrity, and authenticity. This study proposes a novel secure open standard framework for IoT applications, addressing these challenges through the integration of elliptic curve cryptography (ECC) and fog computing. The framework consists of three core components: secure device registration, data encryption within the fog gateway, and a robust mechanism for detecting man-in-the-middle (MITM) attacks. The unique aspect of the proposed method lies in its comprehensive approach to IoT security. Utilizing ECC, the framework ensures secure communication among resource constrained IoT devices, balancing encryption strength and efficiency. The integration of fog computing reduces latency and enhances processing efficiency by offloading intensive tasks from IoT devices to the fog layer. The MITM attack detection mechanism continuously monitors cryptographic keys and communication patterns, providing an additional layer of security against advanced cyber threats. The system was implemented and evaluated using the NS-3.26 network simulator and Python for data visualization. The experimental setup included 100 IoT devices, 25 users, a fog gateway, a datacenter, and a cloud server. Results demonstrate the framework's scalability and efficiency, with consistent throughput increases and balanced power consumption across varying IoT device numbers.
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Railkar, Poonam Ninad, Parikshit Narendra Mahalle, Gitanjali Rahul Shinde, and Nilesh P. Sable. "Policy-aware Distributed and Dynamic Trust based Access Control Scheme for Internet of Things." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 1s (2022): 155–65. http://dx.doi.org/10.17762/ijritcc.v10i1s.5820.
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The use of smart devices is driving the Internet of Things (IoT) trend today. Day by day IoT helps to support more services like car services, healthcare services, home automation, and security services, weather prediction services, etc, to ease user’s life. Integration of heterogeneous IoT devices and social resources sometimes creates many problems like the privacy of data. To avoid privacy issues, an appropriate access control mechanism is required to check authorized and trusted devices, so that only valid devices can access the data which is only required. In the sequel, this paper presents implementation of distributed and dynamic trust based access control mechanism (DDTAC) for secure machine to machine communication or distributed IoT environment. Novelty of this mechanism is that, it uses trust calculation and device classification for dynamic access control. The proposed scheme is implemented, tested and deployed on Node MCU and same mechanism is also simulated on NS-2 for large number of nodes. This access control model support Scalability, Heterogeneity, Privacy, Trust, Selective disclosure, Principle of least privileges, and lightweight calculation features. Results of this models proves that it gives good performance as compared to existing scheme in terms of scalability, throughput and delay. As number of devices increase it does not degrade performance. This mechanism is also protected against the Man-in-the-Middle attack, Sniffing attack, Session Hijacking attacks and Injection attacks. It required less time to detect and resist those attacks.
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Shcherbyna, Serhii, and Trokhym Babych. "Framework for Threat Management and Incident Response in IoT Systems." NaUKMA Research Papers. Computer Science 7 (May 12, 2025): 77–88. https://doi.org/10.18523/2617-3808.2024.7.77-88.
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The article presents the development and implementation of a framework for managing threats and responding to incidents in Internet of Things (IoT) systems. The proposed framework integrates elements of a distributed architecture, including Nginx as a load balancer, the MQTT HiveMQ broker, an authorization server, and the ELK Stack for data analysis and visualization. This solution ensures secure communication between IoT devices using the TLS protocol and employs advanced mechanisms for encryption, authentication, and authorization. Particular attention is paid to leveraging machine learning for real-time anomaly detection, which enables effective responses to potential threats in various IoT domains. The framework is designed to accommodate the computational constraints of IoT devices while meeting stringent security requirements.The importance of IoT lies in its ability to autonomously collect, process, and transmit information without human intervention. However, this autonomy introduces several security vulnerabilities. IoT devices, often operating within public and private networks, increase the attack surface for malicious actors targeting data confidentiality, integrity, and availability. With an estimated 25.1 billion IoT devices expected by 2025, each device represents a potential entry point for cyber threats. Issues like unpatchable vulnerabilities and outdated firmware exacerbate security risks, highlighting the need for innovative solutions.The proposed framework addresses these challenges by establishing a modular and scalable architecture tailored to the diverse and resource-constrained nature of IoT ecosystems. Components such as Nginx, HiveMQ, and the ELK Stack enable reliable communication and data analysis. Nginx serves as a reverse proxy and an entry point, simplifying TLS certificate management and load balancing. HiveMQ, selected for its extensibility and clustering capabilities, acts as a message broker that facilitates efficient and secure data exchange. The ELK Stack, comprising Logstash, Elasticsearch, and Kibana, provides a comprehensive pipeline for real-time data ingestion, processing, and visualization.A key feature of the framework is its integration of machine learning models for anomaly detection. These models, trained on historical data, monitor real-time metrics to identify deviations from normal patterns. This capability is crucial for detecting potential security breaches and irregular operations. Moreover, the system employs certificate pinning and other cryptographic measures to protect against Man-in-the-Middle (MITM) attacks and ensure secure device-server interactions.The framework’s modularity allows for customization across specific IoT domains, such as smart cities, healthcare, and industrial IoT. By providing foundational functionality, the framework facilitates the development of domain-specific solutions that address unique challenges while ensuring scalability and security.In conclusion, the proposed framework represents a comprehensive approach to managing IoT threats and responding to incidents. By integrating secure communication protocols, machine learning-driven anomaly detection, and a modular architecture, it lays the groundwork for reliable and adaptive IoT security solutions.
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K, S. Ravindra, and Dr. MV Panduranga Rao. "A novel secured open standard framework for internet of things applications integrating elliptic curve cryptography and fog computing." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 6 (2024). https://doi.org/10.11591/ijece.v14i6.pp7224-7235.
Full textAbstract:
The internet of things (IoT) has revolutionized various fields by enabling seamless connectivity and data exchange among numerous devices. However, this interconnectivity introduces significant security challenges, particularly in ensuring data confidentiality, integrity, and authenticity. This study proposes a novel secure open standard framework for IoT applications, addressing these challenges through the integration of elliptic curve cryptography (ECC) and fog computing. The framework consists of three core components: secure device registration, data encryption within the fog gateway, and a robust mechanism for detecting man-in-the-middle (MITM) attacks. The unique aspect of the proposed method lies in its comprehensive approach to IoT security. Utilizing ECC, the framework ensures secure communication among resource constrained IoT devices, balancing encryption strength and efficiency. The integration of fog computing reduces latency and enhances processing efficiency by offloading intensive tasks from IoT devices to the fog layer. The MITM attack detection mechanism continuously monitors cryptographic keys and communication patterns, providing an additional layer of security against advanced cyber threats. The system was implemented and evaluated using the NS-3.26 network simulator and Python for data visualization. The experimental setup included 100 IoT devices, 25 users, a fog gateway, a datacenter, and a cloud server. Results demonstrate the framework's scalability and efficiency, with consistent throughput increases and balanced power consumption across varying IoT device numbers.
Book chapters on the topic "Man-in-the-middle-attack network scalability"
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"Wireless Hacking." In Constructing an Ethical Hacking Knowledge Base for Threat Awareness and Prevention. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7628-0.ch009.
Full textAbstract:
Wired networks add to cost and space required to setup while wireless networks are easy to expand without adding complexity of cables. Most organizations implement wireless networks as an extension to an existing wired connection by installing multiple access points at various locations to cover larger area. The wi-fi network users can be assigned limited and restricted access to the actual wired network and organizational resources. Although less reliable, wireless networks offer mobility, flexibility, ease of deployment, scalability with reduced cost of implementation. However, besides these many advantages, wireless network expands the security threat level by offering ease of intercepting network traffic to the hackers via open networks. Hence, there is a need to determine the potential wi-fi security threats, attacks, attacking tools, and possible countermeasures to be used to secure organizational wireless networks. This chapter focuses on different IEEE 802.11 wireless standards, authentication and association processes in 802.11, and WLAN frame structure. This chapter explains different wireless attacks like war-driving, war-chalking, wi-fi signal jamming, denial of service (DOS) attack, rogue access point attack, wireless traffic analysis, MAC spoofing, de-authentication attack, man-in-the-middle attack, evil twin attack, cracking wi-fi encryptions, spectrum analysis, bluetooth devices attacks, etc. The chapter also discusses different tools used for carrying out wireless attacks or auditing wireless security like NetStumbler, Kismet, Aircrack, insider, KisMAC, WEPWedgie, WIDZ, and Snort-wireless. The chapter also discusses countermeasures against these attacks.