A New Approach for ARP Poisoning Attack Detection Based on Network Traffic Analysis

With the rapid increasing of network scale, the size of traffic data also expands a lot. In traditional traffic data analysis, there are some problems, such as high computation complexity, low analysis efficiency, long learning period, and difficulty of development. To address these problems, we design and implement a GPU-accelerated parallel analysis scheme for network traffic - EasyAnalyze. In EasyAnalyze, we introduce GPU parallel computing, Map/Reduce architecture into network traffic analysis, which greatly improves the efficiency but does not increase the difficulty in programming. In the experiments, EasyAnalyze shows very promising results: 1 the speed is 6-17 times faster than conventional serial analysis in network traffic data analysis; and 2 the size of code is only 2% of the mainstream GPU Map/Reduce.

- Performance and Secuity analysis is essential in the communication systems in the digital world we live in today. This paper discusses the creation of a Network Traffic Tracer, a network system written in Python with integrated Wireshark for capturing packets and performing automated traffic analysis. It employs Scapy and PyShark libraries to capture, f ilter, and process network packets to analyze crucial data about the traffic patterns, unusual activities, and possible security threats. Furthermore, it uses GeoLiteCity IP address geolocation database to enable visual mapping of suspicious network activities all over the world. Such visualization is plenty in identifying strange patterns such as unauthorized invasion or flooding the network which is known as Distributed Denial of Service (DDoS). Furthermore, the system supports real-time monitoring along with statistical report generation for traffic anomaly detection to increase the level of secuity and optimize performance of the network. In this paper, we show how effective the combination of automation in Python and traditional network analysis methods is in addressing issues pertaining to security and monitoring of networks.Keywords: Network Traffic Analysis, Python, Wireshark, Cybersecurity, Scapy, PyShark, Anomaly Detection. Keywords: Network Traffic Analysis, Packet Capture, Python, Wireshark, Scapy, PyShark, Cybersecurity, GeoIP, Anomaly Detection

NTLFlowLyzer: Towards generating an intrusion detection dataset and intruders behavior profiling through network and transport layers traffic analysis and pattern extraction

Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Analysis of big data is currently considered as an integral part of many computational and statistical departments. As a result, novel approaches in data analysis are evolving on a daily basis. Thousands of transaction requests are handled and processed everyday by different websites associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog analysis comes to play a crucial role in such situations where Hadoop can be suggested as an efficient solution for processing the Netflow data collected from switches as well as website access-logs during fixed intervals.

The Address Resolution Protocol (ARP) is a foundational component of local area network communication, responsible for resolving IP addresses to MAC addresses within a subnet. Its stateless and unauthenticated design makes it susceptible to a class of attacks known as ARP spoofing, where a malicious actor transmits falsified ARP messages to corrupt the ARP caches of other devices on the same network segment. This paper presents a detailed analysis of ARP spoofing attacks, covering the protocol's inherent weaknesses, the step-by-step mechanics of cache poisoning, and the tools commonly used by attackers. It further investigates the impact of these attacks on LAN and public Wi-Fi environments, addressing data interception, session hijacking, and network availability disruption. A review of current mitigation techniques including Dynamic ARP Inspection (DAI), static ARP entries, and network monitoring is provided, along with an assessment of their practical effectiveness. The paper also discusses emerging trends such as machine learning-based anomaly detection and Software-Defined Networking as future directions for defending local networks against ARP-based threats. Keywords: ARP spoofing, ARP poisoning, MAC address, man-in-the-middle attack, LAN security, Dynamic ARP Inspection, session hijacking, gratuitous ARP, network monitoring

The function of Address Resolution Protocol (ARP) is critical in local area networking as well as for routing Internet traffic across gateways. ARP, being a Stateless protocol, is prone to various attacks such as ARP spoofing, ARP flooding and ARP poisoning. This work discusses about an efficient scalable implementation of an Intrusion Detection System (IDS) with active detection, to detect ARP spoofing, flooding and related attacks like Man-in-the-Middle(MiTM) and Denial-of-Service(DoS) etc.

Due to the rapid development of Internet the size of computer network are continue to grow in size. Thus the network traffic are increasing drastically. To keep the data transfer smooth and faster in the network the monitoring, and management of network traffic is an important factor in field of computer network. The data, information are communicated via TCP or UDP Internet protocol between nodes in the network. These transport layer protocol carry the packet or datagram from source-to-destination site. The packet analysis or packet sniffing is process of capturing the packet and analyze the log traffic passes over the network or a part of network. Packet sniffer is used for wired or wireless network. A tool is developed to accomplish the monitoring task that removes deficiency of existing tool. The packet sniffer is used to capture the traffic and analyzed those captured traffic. The report is generated based on analyzed captured traffic. Various transport layer protocol like TCP, UDP, etc., can be implemented for analysis and filtering can be done based on protocol used. A new approach of packet sniffing and network traffic analysis over TCP connection oriented network using Wireshark software has been discussed in this paper.

In order to handle a computer security incident or network failure, it is important to grasp a list of pairs of IP and MAC addresses of hosts. Ones may then traditionally poll an Address Resolution Protocol (ARP) table of a core switch at an interval using SNMP or other methods. This traditional method based upon polling, however, has two major drawbacks that (1) some pairs of IP and MAC addresses may not be obtained and (2) incurs a heavy load on a core switch. This paper then proposes AXARPS that is the novel scalable ARP snooping to build a list of pairs of IP and MAC addresses. AXARPS can avoid missing pairs of IP and MAC addresses by monitoring all ARP traffic instead of the traditional method. AXARPS also can reduce a CPU load on a recent high-end core switch by approximately 20% in comparison with the traditional method. AXARPS is scalable because AXARPS incurs no additional CPU load even the number of hosts increases. AXARPS employs a policy-based mirroring of a switch that mirror traffic that matches a specified filter. The policy-based mirroring can then mirror ARP traffic only, and reduce a load on an ARP parsing server.

SUMMARYNetwork traffic monitoring and analysis are essential for effective network operation and resource management. In particular, multidimensional analysis for long‐term traffic data is necessary for comprehensive understanding of the traffic trend and effective quality‐of‐service provision considering the extremely dynamic behavior of the current Internet, where various types of traffic occur from high‐speed network links and greatly increasing number of applications. However, only limited analysis results are provided, as the existing network traffic analysis tools and systems are developed and deployed focusing on their own specialized analysis purposes. Consequently, it is difficult to understand the network comprehensively and deeply, which increases the necessity for multilateral analysis of long‐term traffic data. In this paper, we propose a novel traffic analysis model for large volumes of Internet traffic accumulated over a long period of time. TheNetCube, the proposed network traffic analysis model using online analytical processing (OLAP) on a multidimensional data cube, provides an easy and fast way to construct a multidimensional traffic analysis system for comprehensive and detailed analysis of long‐term traffic data by utilizing simple OLAP operations and powerful data‐mining techniques on various abstraction levels of traffic data to complete the analysis purpose. We validate the feasibility and applicability of the proposedNetCubetraffic analysis model by implementing a traffic analysis system and applying it to our campus network. Copyright © 2012 John Wiley & Sons, Ltd.

Local area network (LAN) access is the top vector for insider threats and misuses according to the Verizon's Data Breach Investigations Report. Address Resolution Protocol (ARP) is often exploited by the attackers who have gained access to the LAN. It is critical for students to learn how attacks on ARP work and know the countermeasures. In an earlier work, authors developed a hands-on lab to help students learn how an ARP spoofing attack works by asking them to create and send attack packets. To enhance learning further, we present an interactive visualization tool that intuitively shows the effects of the ARP spoofing attack in real-time. The Hacker Graphical User Interface (HGUI) is an interactive visualization tool developed to assist students in learning how ARP Spoofing works. By modeling a controlled ARP Spoofing attack using virtual machines, we give students the ability to alter elements of the attack by interacting with the visualization. This tool was developed using Processing, an open source programming language used in many visual art communities. It runs on virtual machines installed with Kali Linux. This tool animates attack packets, normal packets, and the status of ARP cache in real-time. If students have successfully carried out the ARP spoofing attack, they can see the normal packets being routed to the attacker machine and the victim's ARP cache being poisoned. In this paper, we present the design, implementation, and evaluation of the lab. Tests were conducted to measure the performance of students before and after using this tool. We also gave students surveys after they completed the hands-on lab. The result shows that this tool can significantly enhance students' understanding of the concept of ARP spoofing attacks and motivate them in learning more about cyber security.

Abstract—The rapid expansion of interconnected smart devices has enabled widespread automation and connectivity across smart homes, healthcare, manufacturing, and industrial control systems. However, this large-scale connectivity has significantly increased vulnerabilities, leading to a sharp rise in IoT botnet attacks. Compromised devices are remotely controlled and used to perform cyber-crimes such as Distributed Denial of Ser- vice (DDoS), unauthorized data access, and aggressive network flooding. Traditional signature-based detection methods fail to identify evolving botnet families due to rapid mutation of attack patterns. This paper introduces a machine learning-based intelligent detection framework capable of identifying IoT botnet behavior through traffic flow analysis. The proposed system preprocesses collected network traffic, extracts relevant features, classifies flows as benign (0) or malicious (1), and triggers an automated alert notification when malicious activity is detected. Experimental evaluation demonstrates highly reliable perfor- mance and strong prediction accuracy, proving the suitability of machine learning methods for real-time IoT botnet detection and prevention. Additional discussions include feature selection techniques, model optimization, and deployment considerations for scalable and robust IoT security solutions. Index Terms—IoT Security, Botnet Detection, Machine Learn- ing, Network Traffic Analysis, Intrusion Detection System, Real- Time Alerting, Flow-Based Detection.

Modern networks generate a massive amount of traffic data streams. Analyzing this data is essential for various purposes, such as network resources management and cyber-security analysis. There is an urgent need for data analytic methods that can perform network data processing in an online manner based on the arrival of new data. Online machine learning (OL) techniques promise to support such type of data analytics. In this paper, we investigate and compare the OL techniques that facilitate data stream analytics in the networking domain. We also investigate the importance of traffic data analytics and highlight the advantages of online learning in this regard, as well as the challenges associated with OL-based network traffic stream analysis, e.g., concept drift and the imbalanced classes. We review the data stream processing tools and frameworks that can be used to process such data online or on-the-fly along with their pros and cons, and their integrability with de facto data processing frameworks. To explore the performance of OL techniques, we conduct an empirical evaluation on the performance of different ensemble- and tree-based algorithms for network traffic classification. Finally, the open issues and the future directions in analyzing traffic data streams are presented. This technical study presents valuable insights and outlook for the network research community when dealing with the requirements and purposes of online data streams analytics and learning in the networking domain.

Network Traffic Analysis (NTA) helps identify security threats, monitor network performance, and plan for future capacity. While real-time analysis is ideal, it can be difficult due to high data volume and complexity. Large amounts of traffic require parsing, and real-time data may miss hidden threats. Post-analysis can address these challenges. It hardly depends on choosing an effective and appropriate storage solutions. A variety of storage systems exist, each employing different approaches and formats to retain data. This article explores the applications of various storage systems for NTA results. Three different types of storage systems considered, including Greenplum, Nebula graph and OpenSearch. A comparative approach is employed, analyzing the same dataset across various storage systems.This allows to examine how different database structures and query capabilities influence the efficiency and accuracy of NTA. The resulting insights will not only provide valuable guidance for selecting the optimal storage solution for specific NTA tasks, but also serve as a foundation for future research in this area.

High-speed network poses challenges on heavy traffic measurement and analysis, which becomes more serious due to the scale of modern data centers. Cloud platform is an alternative to crack the nut because it gives concurrent considerations to both high-performance computing and high-extensible data storage. Therefore, we use Hadoop to construct a trial cloud platform to tackle the challenges derived from the heavy traffic, and perform network traffic analysis and target packet dissection. Our testbed evaluates the performance against different processing strategies, and shows the statistic results. According to the experimental results, the Hadoop-based platform can fit to process massive traffic data very well, and it's appropriate to manage today's data centers with cloud computing services.

Netflow-based network traffic analysis is one of today's mainstream network traffic monitoring and analysis techniques. However, port-based traffic identification that employed in Netflow-based protocol analysis is inaccurate. Netflow cannot provide enough details for analysis of site access behaviour. In order to overcome the above limitation, we propose an enhanced Netflow data collection method combining packet capturing and flow technique for accurate application identification. Then we design and implement an enhanced Netflow data collection system, which lays a data foundation for traffic analysis. Finally, the feasibility and effectiveness of the system are verified.