Address Resolution Protocol Spoofing Research Articles

Enhancing security in Software-Defined Networks: An approach to efficient ARP spoofing attacks detection and mitigation

The proliferation of Software-Defined Networks (SDNs) has introduced unparalleled flexibility and efficiency to network management, but at the same time, it has introduced new challenges in securing network infrastructures. Among these challenges, Address Resolution Protocol (ARP) spoofing attacks remain a pervasive threat, compromising network integrity and data confidentiality. In this manuscript, we present an approach to ARP spoofing mitigation within SDNs, addressing the limitations of existing methodologies. Our proposed solution employs a multifaceted strategy that combines dynamic ARP cache management, real-time traffic analysis, and adaptive flow rule orchestration. Central to our approach is a dedicated device that continuously monitors the network topology and detects any deviations from established norms. Notably, our solution adapts seamlessly to networks of varying sizes, ensuring scalability and efficacy across diverse infrastructures. One of our key contributions is the integration of a deep learning-based Deep Neural Network (DNN) model to detect and mitigate ARP spoofing attacks. Leveraging a self-generated ARP spoofing dataset from SDN environments, our model demonstrates exceptional accuracy and adaptability, enhancing the network’s capability to identify and counter such threats effectively. Our approach showcases exceptional reliability, achieving 100% accuracy rate in detection of ARP spoofing, which is crucial for sustaining network responsiveness.

Advanced approaches to prevent ARP attacks

Advanced approaches to prevent ARP attacks

Countering ARP spoofing attacks in software-defined networks using a game-theoretic approach

The Address Resolution Protocol (ARP) spoofing is a form of attack typically used by attackers to cause a denial of service or man-in-the-middle attacks. This attack comes from ARP weaknesses and aims to compromise victims' ARP caches by sending ARP packets containing fake IP-MAC pairs. To overcome ARP spoofing attacks, several approaches leverage the advantages of software-defined networking (SDN) to detect malicious users in the network. To achieve their goal, the SDN controller consecutively checks the characteristics of each ARP packet to ensure its correctness. However, this verification method can lead to latency and congestion at the controller level or render the system unusable for large-scale networks. To address this drawback, we propose a game-theoretic approach to provide an optimal verification method that considers the intelligent attackers' decision-making process during an ARP cache poisoning attempt. This approach is a zero-sum game between the attacker who wants to poison the victims' ARP caches and the defender who must avoid this poisoning. The game model results in mixed-strategy Nash equilibria that identify optimal verification methods to prevent control plane latency and congestion during attacker detection. The results show that an intelligent attacker will refrain from poisoning ARP caches with a high-impact strategy because the defender frequently checks such a strategy. In addition, the attacker's penalty value can deter both rational and irrational attackers from poisoning ARP caches. Simulations in the Mininet simulator have shown that the proposed approach can significantly mitigate control plane latency and congestion during the attacker's characteristic checking.

ARP-PROBE: An ARP spoofing detector for Internet of Things networks using explainable deep learning

The proliferation of the Internet of Things (IoT) devices and application domains has made IoT security an unavoidable challenge. Spoofing the address resolution protocol (ARP) can be exploited by botnets and other malicious programs to propagate and cause damage. Conventional ARP spoofing detection and prevention methods are, in most cases, inefficient for the IoT. This paper presents an ARP spoofing detection system using explainable deep learning, namely ARP-PROBE, for IoT networks. The proposed system relies on features extracted from network packets to detect ARP spoofing quickly and effectively using a feature selection and extraction module that identifies and selects the highly influential features. In a performance evaluation, the proposed system achieved an accuracy of 99.98% and an F1 score of 0.999. ARP-PROBE had a false positive rate of 0.026% and a false negative rate of 0.001%. To ensure that the model generalizes beyond the training data, a second dataset was used to evaluate it, and the results obtained were consistent with those of the first dataset. To provide a better understanding of the impact of each feature on the performance of the proposed deep learning model, which is the core of ARP-PROBE, a model explanation using SHAP explainability was provided.

Protection Schemes for DDoS, ARP Spoofing, and IP Fragmentation Attacks in Smart Factory

Industry Revolution 4.0 connects the Internet of Things (IoT) resource-constrained devices to Smart Factory solutions and delivers insights. As a result, a complex and dynamic network with a vulnerability inherited from the Internet becomes an attractive target for hackers to attack critical infrastructures. Therefore, this paper selects three potential attacks with the evaluation of the protections, namely (1) distributed denial of service (DDoS), (2) address resolution protocol (ARP) spoofing, and (3) Internet protocol (IP) fragmentation attacks. In the DDoS protection, the F1-score, accuracy, precision, and recall of the four-feature random forest with principal component analysis (RFPCA) model are 95.65%, 97%, 97.06%, and 94.29%, respectively. In the ARP spoofing, a batch processing method adopts the entropy calculated in the 20 s window with sensitivity to network abnormalities detection of various ARP spoofing scenarios involving victims’ traffic. The detected attacker’s MAC address is inserted in the block list to filter malicious traffic. The proposed protection in the IP fragmentation attack is implementing one-time code (OTC) and timestamp fields in the packet header. The simulation shows that the method detected 160 fake fragments from attackers among 2040 fragments.

An Effective Approach to Detect and Prevent ARP Spoofing Attacks on WLAN

Address Resolution Protocol (ARP) is used to resolve a host’s MAC address, given its IP address. ARP is stateless, as there is no authentication when exchanging a MAC address between the hosts. Hacking tactics using ARP spoofing are constantly being abused differently; many previous studies have prevented such attacks. However, prevention requires modification of the underlying network protocol or additional expensive equipment, so applying these methods to the existing network can be challenging. In this paper, we examine the limitations of previous research in preventing ARP spoofing. In addition, we propose a defense mechanism that does not require network protocol changes or expensive equipment. Before sending or receiving a packet to or from any device on the network, our method checks the MAC and IP addresses to ensure they are correct. It protects users from ARP spoofing. The findings demonstrate that the proposed method is secure, efficient, and very efficient against various threat scenarios. It also makes authentication safe and easy and ensures data and users’ privacy, integrity, and anonymity through strong encryption techniques.

Defending a wireless LAN against ARP spoofing attacks using a Raspberry Pi

The Address Resolution Protocol (ARP) is a protocol that converts Internet Protocol (IP) addresses to Media Access Control (MAC) addresses. Due to a security issue known as "Man in the Middle," identity theft is feasible using the ARP protocol. ARP spoofing is one of the weaknesses in wireless networks when an attacker effectively masquerades as a legitimate one. Spoofing attacks will reduce network performance and break several security measures. In networks that use MAC address-based filtering to verify clients, all a spoofer needs is an actual MAC address from an authorised client to gain an unfair advantage. The research recommends developing a security system recognising and preventing ARP spoofing attacks. This system detects ARP spoofing attempts by comparing the static MAC address of the original router to the router's MAC address in the ARP cache table. After detecting the attack using information collected from the router's MAC address in the ARP cache table, the system will conduct a de-authentication attack against the attacker's MAC address. If the attacker is disconnected from the WLAN, they cannot perform ARP spoofing attacks. This system is operated using a Raspberry Pi Model B. Most ARP spoofing attacks can be detected in 0.93 seconds, and responding takes 3.05 seconds.

Features of Address Resolution Protocol Operation in Computer Networks

The paper analyzes the network protocols of computer networks to identify potential vulnerabilities at the software level. The conditions for carrying out a man-in-the-middle attack in networks using the Address Resolution Protocol (ARP) are investigated. Such attacks are of a rather dangerous type, since they are based on the shortcomings of the ARP protocol. A detailed analysis of the stages of the attack and the sequence of impact on the attacked node is given. The technology of ARP spoofing (poisoning) and methods that allow one to infiltrate an existing connection and communication process are examined in detail. An implementation of an ARP spoofing attack in the Python and C# programming languages using the Soapy and SharpPcap libraries is presented. Examples of implementation of denial-of-service (DoS) attacks in a peer-to-peer network using the ARP protocol in C# are given. The article also describes examples of man-in-the-middle attacks associated with various protocols and infiltration into the address space of routers, such as DHCP (a protocol that dynamically assigns an IP address to a client computer) spoofing and ICMP (Internet Control Message Protocol) redirection. Methods for hacking a router and substituting a MAC address and examples of scripts that implement: sending a fake ARP packet; a function for performing a DoS attack; changing the Linux MAC address; router hacks, are presented in the article.

ASD: ARP Spoofing Detector Using OpenWrt

The address resolution protocol (ARP) is one of the most important communication protocols in a local area network (LAN). However, since there is no authentication procedure, the ARP is vulnerable to cyberattack such as ARP spoofing. Since ARP spoofing can be connected to critical attacks, including a man-in-the-middle (MITM) attack, detecting ARP spoofing initially without returning false-positive alarms is important. In general, however, existing works for ARP spoofing are unable to distinguish between ARP spoofing and connections from virtual machine (VM) guests, which results in false-positive alarms. In this article, we propose an access point-based ARP Spoofing Detector (ASD) that can detect ARP spoofing attacks without returning a false-positive rate. Our proposed system distinguishes between ARP spoofing and connections from VM guests using three information tables, AssocList, ARP cache table, and DHCP table, which are commonly managed by the access point based on a Linux system. We evaluated the performance of ASD on ARP spoofing attack experiments.

The problem of security address resolution protocol

This paper examines the conditions for conducting a Man in the middle (MITM) attack in networks using the Address Resolution Protocol (ARP), as well as possible methods for detecting and preventing such attacks. Examples of implementation of denial of service (DoS) attacks in a peer-to-peer network using the ARP protocol are given. An implementation of an ARP spoofing attack in Python and C # using the sharppcap library is presented. Examples of Man-in-the-middle attacks such as DHCP spoofing and ICMP redirection are provided. Describes the techniques of hacking the router and spoofing the MAC address.

Implementing an intrusion detection and prevention system using Software-Defined Networking: Defending against ARP spoofing attacks and Blacklisted MAC Addresses

This work focuses on infiltration methods, such as Address Resolution Protocol (ARP) spoofing, where adversaries sends fabricated ARP messages, linking their Media Access Control (MAC) address to a genuine device’s Internet Protocol (IP) address. We developed a Software-Defined Networking (SDN)-based Intrusion Detection and Prevention System (IDPS), which defends against ARP spoofing and Blacklisted MAC Addresses. This is done by dynamically adjusting SDN’s operating parameters to detect malicious network traffic. Bespoke software was written to conduct the attack tests and customise the IDPS; this was coupled to a specifically developed library to validate user input. Improvements were made to SDN in the areas of attack detection, firewall, intrusion prevention, packet dropping, and shorter timeouts. Our extensive experimental results show that the developed solution is effective and quickly responds to intrusion attempts. In the considered test scenarios, our measured detection and mitigation times are sufficiently low (in the order of a few seconds).

A Dual Detection Method for Siemens Inverter Motor Modbus RTU Attack

Since the Modbus RTU wired communication protocol of Siemens variable frequency motors is unstable and lacks a protection mechanism, there is a risk of user information leakage. Aiming at the problems of insufficient flexibility of traditional defense methods and poor defense effects, The present work proposed a new dual detection method based on MODBUS RTU, which combines the dual monitoring mechanism of “Address Resolution Protocol (ARP) request detection” and “ARP response detection”. In order to improve detection efficiency, two real-time updated linear tables are introduced, which can effectively deal with the three ARP spoofing methods of updating the ARP buffer. Based on the analysis of the hidden dangers of the Modbus RTU wired communication protocol, a wired connection between the S7-1200 PLC and the variable frequency motor was established, and a real experimental platform was constructed to demonstrate the attack. The intensity of ARP attacks has gradually increased over time. Through comparative experiments with traditional defense methods, it is proved that the algorithm enhances the protocol mechanism in principle, and is more flexible and reliable than traditional methods.

E2BaSeP: Efficient Bayes Based Security Protocol Against ARP Spoofing Attacks in SDN Architectures

Virtual networks, just like classical IP networks, usually face many external threats such as ARP spoofing attacks. These attacks come from Address Resolution Protocol (ARP) vulnerabilities. Indeed, the ARP protocol can allow a virtual machine to be identified by one or more IP-MAC pairs, thus facilitating users’ impersonation and forged IP-MAC pair insertion into the victims’ ARP caches. This type of attack is the beginning of more dangerous attacks such as man-in-the-middle and denial-of-service. Several solutions based on SDN (Software-Defined Network) technology, known for their suitable adaptation to large-scale networks, have been proposed. These solutions use a global ARP cache built into the controller which contains the virtual machines’ IP-MAC pairs, as attacker detection knowledge. The main drawbacks of these methods are the collection and unsecured storage of IP-MAC pairs into the global ARP cache and failure to consider IP address reallocation cases, as well as users’ connection and reconnection scenarios in the attacker detection process. To remedy these shortcomings, we propose an Efficient Bayes Based Security Protocol (E2BaSeP) which detects attackers using a Bayes-based algorithm. This solution works in both dynamically and statically addressing networks. Simulation results show that the E2BaSeP protocol provides effective protection for ARP caches and performs better than those observed in the literature.

Analisis Bukti Serangan Address Resolution Protocol Spoofing menggunakan Metode National Institute of Standard Technology

This research intends to find information about evidence of Address Resolution Protocol (ARP) Spoofing attacks that is the MAC address of the attacker and victim also the time of the attack. This research uses Wireshark tools to inspect network traffic, especially on the ARP protocol. It uses the National Institute of Technology Technology (NIST) method as a framework in the simulation process to produce evidence reports. ARP Spoofing attacks can lead to other attacks, such as Denial of Service and Man in the Middle Attack, this attack allows users not to be able to access the network and data theft. During the simulation stage, 2 ARP Spoofing attacks are carried out on 1 laptop and 1 router connected to the network. The results of the attack simulation found 2 attacks and obtained information about the MAC address of the attacker and victim and also the time of the attack. Based on the results of tests carried, successfully found all ARP Spoofing attacks that occur on the network with a success rate of 100%

Prevention and Detection of ARP Spoofing and Man-In-The-Middle Attacks using EDMAC-IP Algorithm

The great Man-in - the-Middle assault is centered around convincing two has that the other host is the machine in the center. In the event that the framework utilizes DNS to order the other host or address goals convention (ARP) ridiculing on the LAN, this might be accomplished with an area name parody. This paper targets presenting and delineating ARP ridiculing and its job in Man-in - the-Middle assaults. The expression "Man-in - the-Middle" is normal utilization—it doesn't imply that these assaults must be utilized by individuals. Maybe progressively sensible wording would be Teenager-in - the-Middle or Monkey-in - the-Middle. Progressively contact the assault can be identified using timing data much of the time. The most widely recognized kind of assaults happen because of reserve harming of Address Resolution Protocol (ARP), DNS satirizing, meeting commandeering, and SSL seizing.

SM ARP Stochastic Markovian Game Model for Packet Forwarding Based ARP Spoofing Attacks Detection

Address Resolution Protocol (ARP) spoofing attacks have become the most pivotal attacks in deteriorating the performance of computer networks. The objective of this paper is to develop SM-ARP, Stochastic Markovian game model based ARP spoofing attack detection scheme.Although many recent techniques have been developed to detect and protect against ARP spoofing attacks, the practical challenges has led to ineffective utilization. The major challenge is that the attackers employing ARP spoofing tend to alter the attack strategy at each point and increases the difficulty in detection and security implementations. The packet forwarding relaying is one suchattack strategy which is harder to detect using traditionally proven methodologies. This paper tackles the packet forwarding relay strategy based ARP spoofing attack strategy by using the proposed SM-ARPto eliminate the attack in a practically feasible manner. The proposed model utilizes a stationary Markov model for optimizing the packet forwarding behaviour of the networks. When an ARP spoofing attack is initiated, the SM-ARP model tracks the changes in the packet forwarding patterns through cache table and detects the misbehaviours. As a security measure, these misbehaved nodes are entitled to recovery and repair process to restore the network to stabilized state. Experiments are conducted to evaluate the performance of SM-ARP in an application for student marks management system. The results prove that the proposed SM-ARP model improves the detection of ARP spoofing attacks with accuracy of 88.2% and also reduces the complexity and errors.

Implementation of ARP Spoofing for IOT Devices Using Cryptography AES and ECDSA Algorithms

The Internet of Things is the network of numerous devices and communicate with an internet by using the IP address. The IOT objects shares the information using wireless connection. During the data transmission, that can be distorted by the Hackers by knowing their IP address. In IOT (Internet of Things), the wireless communication between the devices makes the users to be vulnerable. So, the hackers may spoof the MAC address of the communicating devices. The receiver MAC address is identified and then false MAC (Media Access Control) address is created by the hacker. Then, attackers replaces the original MAC address in the ARP (Address Resolution Protocol) table of the sender. So,the hackers may impersonate like the sender. Therefore, Cryptographic algorithms like AES (Advanced Encryption Standard) for confidentiality and ECDSA (Elliptic Curve Digital Signature Algorithm) for Authentication are applied in the proposed algorithm to safeguard the data as well as the devices from the hackers. The following attacks such as Man-in-the-Middle, Denial -of -Service (DOS) and ARP spoofing are strongly prevented in the proposed algorithm. Thus, the implementation of an algorithm is carried out in Ubuntu Linux environment with installing Python dependencies. This algorithm affords an efficient way to thwart ARP (Address Resolution Protocol) spoofing by the hackers for IOT devices.

An Active Defense Solution for ARP Spoofing in OpenFlow Network

As an emerging network technology, Software-defined network (SDN), has been rapidly developing for recent years due to its advantage in network management and updating. There are still a lot of open problems while applying this novel technology in reality, especially for meeting security demands. The Address resolution protocol (ARP) spoofing, a representative network attack in traditional networks is investigated. We implement the ARP spoofing in SDN network firstly and find that the threat of ARP attack still exists and has big impact on the network. We propose a novel mechanism as defense solution for ARP spoofing oriented to OpenFlow platform. Theoretical analyzation is given, and the mechanism is implemented as a module of POX controller. Experiment results and performance evaluations show that our solution can reduce the security threat of ARP spoofing remarkably on OpenFlow platform and related SDN platforms.

Address resolution protocol spoofing attacks and security approaches: A survey

Address resolution protocol (ARP) is a very popular communication protocol in the local area network (LAN), working under the network layer, as per the open systems interconnection (OSI) model. It is used to associate Internet protocol (IP) address to medium access control (MAC) address; hence, the IP‐MAC addresses are stored in ARP cache of any network host. ARP has weakness in authentication that an attacker can exploit to gain unauthorized access to one's sensitive data. There are some approaches that can be used against ARP spoofing attacks. These provide an efficient and secure way to mitigate ARP attacks and detect ARP poisoning that can reduce the attack. This paper will discuss: various effective approaches, security issues in ARP spoofing, and poisoning attacks.

Anomalous Traffic Detection and Self-Similarity Analysis in the Environment of ATMSim

Internet utilisation has steadily increased, predominantly due to the rapid recent development of information and communication networks and the widespread distribution of smartphones. As a result of this increase in Internet consumption, various types of services, including web services, social networking services (SNS), Internet banking, and remote processing systems have been created. These services have significantly enhanced global quality of life. However, as a negative side-effect of this rapid development, serious information security problems have also surfaced, which has led to serious to Internet privacy invasions and network attacks. In an attempt to contribute to the process of addressing these problems, this paper proposes a process to detect anomalous traffic using self-similarity analysis in the Anomaly Teletraffic detection Measurement analysis Simulator (ATMSim) environment as a research method. Simulations were performed to measure normal and anomalous traffic. First, normal traffic for each attack, including the Address Resolution Protocol (ARP) and distributed denial-of-service (DDoS) was measured for 48 h over 10 iterations. Hadoop was used to facilitate processing of the large amount of collected data, after which MapReduce was utilised after storing the data in the Hadoop Distributed File System (HDFS). A new platform on Hadoop, the detection system ATMSim, was used to identify anomalous traffic after which a comparative analysis of the normal and anomalous traffic was performed through a self-similarity analysis. There were four categories of collected traffic that were divided according to the attack methods used: normal local area network (LAN) traffic, DDoS attack, and ARP spoofing, as well as DDoS and ARP attack. ATMSim, the anomaly traffic detection system, was used to determine if real attacks could be identified effectively. To achieve this, the ATMSim was used in simulations for each scenario to test its ability to distinguish between normal and anomalous traffic. The graphic and quantitative analyses in this study, based on the self-similarity estimation for the four different traffic types, showed a burstiness phenomenon when anomalous traffic occurred and self-similarity values were high. This differed significantly from the results obtained when normal traffic, such as LAN traffic, occurred. In further studies, this anomaly detection approach can be utilised with biologically inspired techniques that can predict behaviour, such as the artificial neural network (ANN) or fuzzy approach.