Man-in-the-middle Research Articles

Deep learning approaches for protecting IoT devices in smart homes from MitM attacks

The primary objective of this paper is to enhance the security of IoT devices in Software-Defined Networking (SDN) environments against Man-in-the-Middle (MitM) attacks in smart homes using Artificial Intelligence (AI) methods as part of an Intrusion Detection and Prevention System (IDPS) framework. This framework aims to authenticate communication parties, ensure overall system and network security within SDN environments, and foster trust among users and stakeholders. The experimental analysis focuses on machine learning (ML) and deep learning (DL) algorithms, particularly those employed in Intrusion Detection Systems (IDS), such as Naive Bayes (NB), k-Nearest Neighbors (kNN), Random Forest (RF), and Convolutional Neural Networks (CNN). The CNN algorithm demonstrates exceptional performance on the training dataset, achieving 99.96% accuracy with minimal training time. It also shows favorable results in terms of detection speed, requiring only 1 s, and maintains a low False Alarm Rate (FAR) of 0.02%. Subsequently, the proposed framework was deployed in a testbed SDN environment to evaluate its detection capabilities across diverse network topologies, showcasing its efficiency compared to existing approaches.

Design and Performance Evaluation of an Authentic End-to-End Communication Model on Large-Scale Hybrid IPv4-IPv6 Virtual Networks to Detect MITM Attacks

After the end of IPv4 addresses, the Internet is moving towards IPv6 address architecture quickly with the support of virtualization techniques worldwide. IPv4 and IPv6 protocols will co-exist long during the changeover process. Some attacks, such as MITM attacks, do not discriminate by appearance and affect IPv4 and IPv6 address architectures. In an MITM attack, the attacker secretly captures the data, masquerades as the original sender, and sends it toward the receiver. The receiver replies to the attacker because the receiver does not authenticate the source. Therefore, the authentication between two parties is compromised due to an MITM attack. The existing authentication schemes adopt complicated mathematical procedures. Therefore, the existing schemes increase computation and communication costs. This paper proposes a lightweight and authentic end-to-end communication model to detect MITM attacks using a pre-shared symmetric key. In addition, we implement and analyze the performance of our proposed security model on Linux-based virtual machines connected to large-scale hybrid IPv4-IPv6 virtual networks. Moreover, security analyses prove the effectiveness of our proposed model. Finally, we compare the performance of our proposed security model with existing models in terms of computation cost and communication overhead.

Implementation of a novel secured authentication protocol for cyber security applications

Robust verification protocols are crucial for maintaining the security and reliability of sensitive information due to the increasing complexity of cyber-attacks. This paper introduces a novel 5G Secure Handover Protocol aimed at addressing security and effectiveness issues encountered in existing systems. The proposed protocol is robust against various attacks, including de-synchronization, replay, man-in-the-middle (MITM), denial of services (DoS), and jamming, ensures perfect forward key secrecy, safeguarding communication confidentiality. The proposed protocol utilizes a combination of spiking neural network and fuzzy logic (SNN-FL) techniques that must choose the goal cell as carefully as possible before initiating the transfer process. By combining fuzzy logic and spiking neural networks to reduce handover latency and thwart several types of cyberattacks, the proposed 5G Secure Handover Protocol improves security. Extensive simulations show its efficacy and emphasize its potential for safe communication in large-scale cybersecurity applications. The paper presents a novel secure authentication protocol that significantly reduces handover delays and improves efficiency. Simulations show its resilience against common security threats, protecting sensitive information and maintaining secure communication channels. The protocol, with low communication expenses, complex spatial, and latency for changeover verification, is ideal for large-scale cybersecurity applications, contributing to the development of secure digital authentication mechanisms.

VOTEMAT: A Blockchain Based Voting System

This study aims to show that a secure, trustable and immutable voting system can be established with Blockchain technology. Decentralized structure of the Blockchain excludes the central authority and provides transparency. Moreover, its cryptographic functions enable secure transactions. Therefore, the operation is prevented from potential frauds, such as multiple votes, fake vote attempts, and fraudulent vote counts. The proposed method, VOTEMAT, covers both electronic voting and paper ballot as a complete solution. A mobile application and a Website, connected to Ethereum private Blockchain network, were developed for the voters who prefer to cast their votes remotely. It is also possible to vote in the voting centres via the mobile device or paper ballot placed in the vote boxes; but these votes are also recorded in the same Blockchain and equally secure. For the remote users, a two-step authentication is designed, based on the information on the national identity card and face recognition. An additional encryption based security measure is used to avoid hacking attempts, such as man in the middle attacks. Since the proposed system is more practical than the traditional voting methods, it can increase the participation and be utilized in all kinds of local or national elections.

Security Assessment Framework for DDOS Attack Detection via Deep Learning

Internet of Things (IoT) is a broad network of actual, physical objects that can exchange data and communicate with other devices and systems over the Internet. Due to the scalability of the internet, the number of IoT users is increasing, and therefore it is vital to ensure the security of user info. Attacks such as DDoS (Distributed Denial of Service) and MitM (Man-in-the-Middle) can seriously interrupt operations and threaten the confidentiality and integrity of sensitive data. In this paper, a novel Iot based Security Assessment for intrusion using Deep Learning (ISAI-DL) technique has been proposed to identify IoT device vulnerabilities such as DDoS and MitM attacks. Initially, the features are extracted from the API documents using Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) techniques. In proposed ISAI-DL technique, it triggers or activates the vulnerabilities deep within target devices by sending API documents. The vulnerability is detected via the GloVe-CNN-BiLSTM Model; if the attack occurs in an IoT device, the output is classified as either DDoS, MitM, or normal. Evaluation metrics such as accuracy, recall, precision, time efficiency, F1 score, false alarm rate, and detection rate have been used to measure how effective the proposed ISAI-DL technique is. Based on a comparative investigation, the detection rate of the proposed ISAI-DL technique is 17.23%, 18.53%, and 3.24% greater than that of the existing P2ADF, HDA-IDS, and BT-TPF techniques respectively.

Networked Control of a Small Drone Resilient to Cyber Attacks

With increasing advances in networked systems and networked control systems in everyday life, the problem of cybersecurity becomes crucial. Moreover, in some applications like small UAVs, the safety and integrity of the system and its surroundings are highly susceptible to cyberattacks. In this context, the current paper proposes a resilient networked control approach. The control structure is split into an inner and an outer loop. The outer position control loop uses measurements from motion cameras connected to a remote computer, while the commands are sent through the network. We consider the resilience problem for two types of cyberattacks: denial of service (DoS), emulated as an increase in the network transmission delay, and man in the middle (MitM), emulated as additive input disturbances. The mitigation for the DoS attack is performed through the help of a reference governor (RG), which uses the delay estimates and the system’s model to predict future safety violations and adapts the reference accordingly. The MitM attack is mitigated by an unknown input disturbance observer (UIDO) together with a RG. Experimental results on a Parrot Mambo drone show that both types of attacks are rejected successfully, ensuring a safe and stable flight.

RPKI Defense Capability Simulation Method Based on Container Virtualization

As the main inter-domain routing protocol in today’s internet, the Border Gateway Protocol (BGP) faces serious security risks during actual usage. Research on BGP malicious attack methods requires a realistic network environment, and evaluation methods based on physical networks often suffer from high costs and insufficient flexibility. Thus, we propose an efficient BGP simulated network deployment system based on a virtualization technology called the SOD–BGP. This system, combining cloud computing and virtualization technologies, creates a scalable, highly flexible basic network environment that allows for the automated simulation and evaluation of actual BGP prefix hijacking attack scenarios. A Resource Public Key Infrastructure (RPKI) simulation suite is introduced into the system, emulating a certificate issuance system, certificate storage, and a certificate synchronization verification mechanism, thus aligning the simulation environment with real-world usage scenarios. Finally, we propose a data collection and performance evaluation technique to evaluate BGP networks deploying RPKI under different attack scenarios and to explore the effectiveness of RPKI defense mechanisms at various deployment rates. A comparative analysis with other simulation techniques demonstrates that our approach achieves a balanced performance in terms of deployment speed, complexity, and RPKI integrity, providing a solid simulation technology foundation for large-scale BGP security defense strategies.

5G Security Features, Vulnerabilities, Threats, and Data Protection in IoT and Mobile Devices: A Systematic Review

The evolution of wireless communications, from the first to the fifth generation, has driven Internet of Things (IoT) advancements. IoT is transforming sectors like agriculture, healthcare, and transportation, but also presents challenges like spectrum bandwidth demand, speed requirements, and security issues. IoT environments, with embedded sensors and actuators, connect to other devices to transmit and receive data over the internet. These data are processed locally or in the cloud, enabling decision-making and automation. Various wireless technologies, including Bluetooth, Zigbee, LoRa, and WiFi, cater to specific IoT applications. 5G, with its high speeds, low latency, and efficient bandwidth sharing, is ideal for high-speed data transmission, and thus it provides robust infrastructure for low-latency and bandwidth-sensitive applications. This latest mobile network technology is being rapidly implemented worldwide, providing unprecedented throughput and low latency, which are crucial for the IoT. This paper presents a comprehensive systematic review on 5G security features, vulnerabilities, threats to mobile devices, security in IoT devices, common attacks to IoT devices and protection of data in this environment. Despite enhanced security, 5G faces challenges like Distributed Denial of Service (DDoS), Man In The Middle (MITM), cross-slice disruptions, and side-channel attacks.

An improved man-in-the-middle (MITM) attack detections using convolutional neural networks

The increasing reliance on digital communication networks has made information security a critical concern for individuals, organizations, and governments worldwide. Man-in-the-middle (MITM) attacks are significant, prevalent, and damaging concerning cyber-attacks. Detecting MitM attacks is complex due to their stealthy nature and the sophisticated methods employed by attackers. There is the need for researchers to address this issue using current and novel methods like artificial intelligence. In this paper, an improved MitM attack detection approach using the Convolutional Neural Network (CNN) deep learning algorithm is developed, resulting in an overall detection accuracy of 0.986%. The results confirms that the proposed model is very efficient in comparision to other proposed solutions by other authors.

IoT and Machine Learning Based Attacks Detection Model on Wearable Health Care Devices

The Internet of Things (IoT) in healthcare is becoming more and more popular in the field of research aimed at improving the effectiveness of intelligent healthcare networks and applications. Nonetheless, distinct risks affect the security and privacy of data in smart health (S-Health). IoT enables healthcare professionals to engage with patients more proactively and with greater vigilance. Smart gadgets with tiny sensors attached to them that communicate with one another to track each other's performance are part of the Internet of Things. To defend S-Health from MITM attacks. The suggested method employs two layers of machine learning algorithms for attack detection and security mechanisms, including low-cost access policies for SHRs (Smart Health Records), lightweight IoT detection schemes, and timely detection of to lessen their impact on the network. According to simulation data, the suggested Hybrid ML performs better than current methods and has a higher attack detection rate overall. The main goal of this research article is to develop an attack detection technique.

Digital-Signature Oriented Steganography Approach against Man-in-the-Middle Attack

Nowadays, man-in-the-middle (MITM) attacks have become a large problem due to the advancement of computational power and interactive mobile technologies. Message security is a crucial concern that ought to be managed in order to help protect vital data from unauthorized people, such as MITM. Steganography is the technique of hiding secret data within an ordinary, non-secret file or message in order to avoid detection when communicating through an unsecured network. Steganography applications play a vital role in various fields that involve classified data transfer, such as healthcare, multimedia, and the military. Hence, the application of steganography in those fields attracts MITM attacks. Thus, in an attempt to address the challenge. This study proposes a hybrid approach that integrates an image steganography technique, the Advanced Encryption Standard (AES) for message encryption, and EdDSA (Edward-Curve Digital Signature Algorithm) for signature verification to enhance steganography against MITM attacks. The proposed hybrid approach was tested and measured using image metrics (MSE, PSNR, and SSIM) and histogram visualization and verified through experimentation. The results have proven that the proposed hybrid approach is an enhanced security approach with low execution time, more payload size for hiding messages, and a high invisibility embedded message to MITM compared with other existing approaches. This study has potential limitations. It does not explore tamper resistance or algorithm robustness, and it was not tested on a public image dataset.

Securing industrial control systems: Developing a SCADA/IoT test bench and evaluating lightweight cipher performance on hardware simulator

This paper addresses the critical need for enhancing security in Supervisory Control and Data Acquisition (SCADA) networks within Industrial Control Systems (ICSs) to protect the industrial processes from cyber-attacks. The purpose of our work is to propose and evaluate lightweight security measures to safeguard critical infrastructure resources. The scope of our effort involves simulating a secure SCADA/IoT-based hardware test bench for ICSs, utilizing Modbus and MQTT communication protocols. Through case studies in remote servo motor control, water distribution systems, and power system voltage level indicators, vulnerabilities such as Denial of Service (DoS) and Man-in-The-Middle (MiTM) attacks are identified, and security recommendations are provided. To execute our work, we deploy lightweight ciphers such as Prime Counter & Hash Chaining (PCHC) and Ascon algorithm with Compression Rate (ACR) for secure information exchange between the plant floor and the control center. Evaluation of these ciphers on Raspberry Pi focuses on execution speed and memory utilization. Additionally, a comparison with the AGA-12 protocol standard for SCADA networks is conducted to underscore the efficacy of the proposed security measures. Our findings include the identification of SCADA network vulnerabilities and the proposal of lightweight security measures to mitigate risks. Performance evaluation of the proposed ciphers on Raspberry Pi demonstrates their effectiveness, emphasizing the importance of deploying such measures to ensure resilience against cyber threats in SCADA environments.

A distributed and cooperative signature-based intrusion detection system framework for multi-channel man-in-the-middle attacks against protected Wi-Fi networks

A Multi-Channel Man-in-the-Middle (MC-MitM) attack is an advanced form of MitM attack, characterized by its ability to manipulate encrypted wireless communications between the Access Point (AP) and clients within a WiFi network. MC-MitM attacks can target any Wi-Fi client, regardless of the authentication method used with the AP. Notable examples of such attacks include Key Reinstallation Attacks and FragAttacks, which have impacted millions of WiFi systems worldwide, especially those involving Internet of Things devices. Current defense mechanisms are inadequate against these attacks due to interoperability challenges and the need for modifications to devices or protocols within the targeted Wi-Fi networks. This paper introduces a distributed and cooperative signature-based wireless intrusion detection mechanism designed for online passive monitoring to detect malicious traffic patterns during MC-MitM attacks in any environment, from apartments and houses to large areas like hotels, offices or industrial sites. We implemented the proposed framework on Raspberry Pis and evaluated it in real-world settings. Our evaluation demonstrates that this framework can effectively identify MC-MitM attacks with an average accuracy of 98% when deployed across different locations within our experimental testbed.

APPS: Authentication-enabled privacy protection scheme for secure data transfer in Internet of Things

The Internet of Things (IoT) is an emerging field that encompasses several heterogeneous devices and smart objects that are integrated with the network. In open platforms, these objects are deployed to present advanced services in numerous applications. Innumerable security-sensitive data is generated by the IoT device and therefore, the security of these devices is an important task. This work formulates a secure data transfer technique in IoT, named Authentication enabled Privacy Protection (APPS) scheme for resource-constrained IoT devices. The proposed scheme demonstrates resilience against various attacks; such as resisting reply attacks, device anonymity, untracebility, session key establishment, quantum attacks and resisting MITM attacks. For the privacy protection scheme, the secured data transfer is initiated between the entities, like IoT devices, servers, and registration centers, by using various phases, namely registration phase, key generation phase, data encryption, authentication, verification, and data retrieval phase. Here, a mathematical model is designed for protecting data privacy using hashing, encryption, secret keys, etc. Finally, performance of proposed APPS model is analyzed; wherein the outcomes reveal that the proposed APPS model attained the maximum detection rate of 0.85, minimal memory usage of 0.497MB, and minimal computational time of 112. 79 sec and minimal turnaround time 131.91 sec.

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things

ABSTRACT Nowadays, the network intrusion and cyberattack have emerged as the two main issues with Internet of Things (IoT) applications. The existing methods for preventing and detecting intrusions are limited in many ways, making it impossible to accurately identify any kind of attack occurring within network traffic. A number of machine learning-based methods that attains poor performance in the multiple class categorization accuracy are provided by the researchers. This research presents Data-Driven Intrusion Detection System in Internet of Things utilizing Optimized Bayesian Regularization-Back Propagation Neural Network (DIDS-BRBPNN-BBWOA-IoT) to overcome these issues. The input data is taken from TON_IoT Dataset. The data balancing of the training dataset is enhanced using Class decomposition and synthetic minority class oversampling method (CDSMOTE). Then, the data is pre-processed using Variational Bayesian-based Maximum Correntropy Cubature Kalman Filtering (VBMCCKF) of noise removal and data enhancement. The preprocessed output is given into feature extraction to extract features by using Dual-Tree Biquaternion Wavelet Transform (DTBWT). The extracted features are fed into Bayesian Regularization-Back Propagation Neural Network (BRBPNN) which detects the intrusion as Ransomware, Password attack, Scanning, Denial of Service (DoS), Distributed Denial of Service (DDoS), Data injection, Backdoor, Cross-Site Scripting (XSS), and Man-In-The-Middle (MITM). In general, BRBPNN does not show any optimization adaption methods to determine the optimal parameter for appropriate detection. Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. The proposed DIDS-BRBPNN-BBWOA-IoT method is implemented using Python. The performance of the DIDS-BRBPNN-BBWOA-IoT approach is examined using performance metrics like accuracy, precision, recall, f1-score, specificity, error rate; computation time, and ROC. The proposed SAPVAEGAN-LCC-IR approach attains 18.44%, 26% ,and 29% greater accuracy; 26.55%, 24.12%, and 27.22% greater recall compared with existing MIDS-MIoT, AID-SDN-IoT, and IID-LW-IoT techniques.

An optimized hybrid encryption framework for smart home healthcare: Ensuring data confidentiality and security

This study proposes an optimized hybrid encryption framework combining ECC-256r1 with AES-128 in EAX mode, tailored for smart home healthcare environments, and conducts a comprehensive investigation to validate its performance. Our framework addresses current limitations in securing sensitive health data and demonstrates resilience against emerging quantum computing threats. Through rigorous experimental evaluation, we show that the proposed configuration outperforms existing solutions by delivering unmatched security, processing speed, and energy efficiency. It employs a robust yet streamlined approach, meticulously designed to ensure simplicity and practicality, facilitating seamless integration into existing systems without imposing undue complexity. Our investigation affirms the framework's capability to resist common cybersecurity threats like MITM, replay, and Sybil attacks while proactively considering quantum resilience. The proposed method excels in processing speed (0.006 seconds for client and server) and energy efficiency (3.65W client, 95.4W server), offering a quantum-resistant security level comparable to AES-128. This represents a security-efficiency ratio of 21.33 bits per millisecond, a 25.6% improvement in client-side processing speed, and up to 44% reduction in server-side energy consumption compared to conventional RSA-2048 methods. These improvements enable real-time encryption of continuous health data streams in IoT environments, making it ideal for IoT devices where AES-128′s smaller footprint is advantageous. By prioritizing high-grade encryption alongside ease of use and implementation, the proposed framework presents a future-proof solution that anticipates the trajectory of cryptographic standards amid advancing quantum computing technologies, signifying a pivotal advancement in safeguarding IoT-driven healthcare data.

Design and Implementation of Lightweight Certificateless Secure Communication Scheme on Industrial NFV-Based IPv6 Virtual Networks

With the fast growth of the Industrial Internet of Everything (IIoE), computing and telecommunication industries all over the world are moving rapidly towards the IPv6 address architecture, which supports virtualization architectures such as Network Function Virtualization (NFV). NFV provides networking services like routing, security, storage, etc., through software-based virtual machines. As a result, NFV reduces equipment costs. Due to the increase in applications on Industrial Internet of Things (IoT)-based networks, security threats have also increased. The communication links between people and people or from one machine to another machine are insecure. Usually, critical data are exchanged over the IoE, so authentication and confidentiality are significant concerns. Asymmetric key cryptosystems increase computation and communication overheads. This paper proposes a lightweight and certificateless end-to-end secure communication scheme to provide security services against replay attacks, man-in-the-middle (MITM) attacks, and impersonation attacks with low computation and communication overheads. The system is implemented on Linux-based Lubuntu 20.04 virtual machines using Java programming connected to NFV-based large-scale hybrid IPv4-IPv6 virtual networks. Finally, we compare the performance of our proposed security scheme with existing schemes based on the computation and communication costs. In addition, we measure and analyze the performance of our proposed secure communication scheme over NFV-based virtualized networks with regard to several parameters like end-to-end delay and packet loss. The results of our comparison with existing security schemes show that our proposed security scheme reduces the computation cost by 38.87% and the communication cost by 26.08%.

Clustering man in the middle attack on chain and graph-based blockchain in internet of things network using k-means

Network security on internet of things (IoT) devices in the IoT development process may open rooms for hackers and other problems if not properly protected, particularly in the addition of internet connectivity to computing device systems that are interrelated in transferring data automatically over the network. This study implements network detection on IoT network security resembles security systems from man in the middle (MITM) attacks on blockchains. Security systems that exist on blockchains are decentralized and have peer to peer characteristics which are categorized into several parts based on the type of architecture that suits their use cases such as blockchain chain based and graph based. This study uses the principal component analysis (PCA) to extract features from the transaction data processing on the blockchain process and produces 9 features before the k-means algorithm with the elbow technique was used for classifying the types of MITM attacks on IoT networks and comparing the types of blockchain chain-based and graph-based architectures in the form of visualizations as well. Experimental results show 97.16% of normal data and 2.84% of MITM attack data were observed.

Clustering man in the middle attack on chain and graph-based blockchain in internet of things network using k-means

Network security on internet of things (IoT) devices in the IoT development process may open rooms for hackers and other problems if not properly protected, particularly in the addition of internet connectivity to computing device systems that are interrelated in transferring data automatically over the network. This study implements network detection on IoT network security resembles security systems from man in the middle (MITM) attacks on blockchains. Security systems that exist on blockchains are decentralized and have peer to peer characteristics which are categorized into several parts based on the type of architecture that suits their use cases such as blockchain chain based and graph based. This study uses the principal component analysis (PCA) to extract features from the transaction data processing on the blockchain process and produces 9 features before the k-means algorithm with the elbow technique was used for classifying the types of MITM attacks on IoT networks and comparing the types of blockchain chain-based and graph-based architectures in the form of visualizations as well. Experimental results show 97.16% of normal data and 2.84% of MITM attack data were observed.

Machine learning-based detection of the man-in-the-middle attack in the physical layer of 5G networks

Fifth generation communication networks (5G) has received a great deal of attention from academia and industry alike, which will enable a wide variety of vertical applications by connecting heterogeneous devices and machines. Assessing availability and reliability in many circumstances and environments is critical. Researchers have recently focused on investigating and analyzing new multimedia networks with artificial intelligence (AI) technologies to achieve higher data rates and secure communication traffic between parties. User information privacy and security are of vital importance and of growing concerns that present evolving challenges to overcome in preventing attacks. Man-in-the-middle (MITM) attack is considered one of the most common attacks, where an attacker can impersonate one of the parties in a communication system to steal user data or forge the malicious data. Due to the limitation of using conventional cryptographic techniques for mobile networks and similar systems, new methods have been introduced to validate and authenticate transmitted signals dynamically, depending on the physical layer. In this paper, we present the distance-time directional delay (DTDD) model to detect the MITM attack in a variety of contexts and scenario. Indoor hotspots (InH) and urban micro-cellular (UMi) propagation environments were investigated to verify the reliability of the proposed approaches using realistic 5G millimeter-wave configurations and system setups. Simulations have been constructed based on the mmWave 5G channel simulator tool NYUSIM, in conjunction with a collection of machine learning algorithms (ML) including the extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM) as the core of the presented models and methodologies. Numerical simulations results produced a detection accuracy approaching 100% in the InH environment scenario, whereas for UMi environment scenario, a detection accuracy approaching 99% was attained.