Network Traffic Analysis Research Articles

AFF_CGE: Combined Attention-Aware Feature Fusion and Communication Graph Embedding Learning for Detecting Encrypted Malicious Traffic

While encryption enhances data security, it also presents significant challenges for network traffic analysis, especially in detecting malicious activities. To tackle this challenge, this paper introduces combined Attention-aware Feature Fusion and Communication Graph Embedding Learning (AFF_CGE), an advanced representation learning framework designed for detecting encrypted malicious traffic. By leveraging an attention mechanism and graph neural networks, AFF_CGE extracts rich semantic information from encrypted traffic and captures complex relations between communicating nodes. Experimental results reveal that AFF_CGE substantially outperforms traditional methods, improving F1-scores by 5.3% through 22.8%. The framework achieves F1-scores ranging from 0.903 to 0.929 across various classifiers, exceeding the performance of state-of-the-art techniques. These results underscore the effectiveness and robustness of AFF_CGE in detecting encrypted malicious traffic, demonstrating its superior performance.

Fortifying cloud environments against data breaches: A novel AI-driven security framework

As cloud computing continues to dominate the modern technological landscape, organizations face growing challenges in preventing data breaches and sophisticated cyber threats. The increasing complexity and scale of cloud environments require advanced security mechanisms to address evolving threats. This paper introduces "SecureCloudAI," a cutting-edge AI-driven security framework designed to fortify sensitive data within cloud infrastructures. SecureCloudAI leverages a hybrid approach that combines machine learning models like Random Forest and deep learning techniques, including Long Short-Term Memory (LSTM) networks, to detect, classify, and respond to potential security breaches in real-time. The system offers robust malware detection, network traffic analysis, and web intrusion detection while maintaining scalability and efficiency across large cloud environments. Experimental results demonstrate SecureCloudAI's high accuracy, with malware detection at 94.78% and network traffic classification at 90.92%, ensuring the system can handle both complex and emerging threats. This AI-driven solution marks a significant advancement in cloud security, providing organizations with an adaptive and scalable tool to safeguard their data against the ever-growing cyber threat landscape.

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

Network security remains a critical concern in modern computing systems due to the constant emergence of threats and attacks. This paper introduces a comprehensive behavioral profiling solution to address the limitations of current intrusion detection methods in identifying zero-day attacks and novel malicious behaviors. Beginning with raw network data, the proposed framework progresses through multiple stages, ultimately culminating in the creation of activity-specific profiles. Central to this approach is NTLFlowLyzer, a novel network traffic analyzer, which generates an updated dataset, BCCC-CIC-IDS2017, for enhanced profile generation. The core of the profiling system leverages the distinct behaviors exhibited by individual features and the diverse correlations observed across various activities. The profiling procedure attains accuracy and robustness by integrating a novel feature selection algorithm and a pattern extraction process. Furthermore, behavior similarity is introduced to quantify the resemblance between activities based on their features and behaviors. We rigorously evaluate the effectiveness of our model by subjecting it to comprehensive testing, followed by meticulous comparison with previous works. Our proposed framework proficiently characterizes eight malicious activities with an accuracy rate surpassing 99.8%, while displaying promising performance in profiling various other activities. These findings, derived from our comprehensive experiments, provide valuable guidance for accurately implementing behavioral profiling.

AI-Based Network Traffic Analysis for Threat Hunting

The focus of this study is to explore the effectiveness of advanced models like CNN and RNN to detect cyber threats on our network systems that are posted to us with every passing day. By using the CIC-DDoS 2019 dataset we test the possibility of detecting threats on network traffic in a realistic environment. This study tests both the limitations and strengths of neural networks for our case study. Our aim is to make a system efficient enough to improve itself over time and mitigate the threats posed in future by predicting them beforehand. This involves difficult testing of the CNN and RNN models to identify any patterns or anomalies that are indicate DDos attacks. This will work with the integration of AI with Cybersecurity and working on the real-world data and improve the accuracy and speed of these models to work efficiently. This will help make our environment resistant to the threats that will emerge with the advent of technology. These findings are intended to inform the development of more robust and autonomous AI systems that safeguard Internet while respecting user privacy.

Research on the Delineation Method of Urban Community Living Circle Based on Transportation Network Analysis in Yuanping, China

In the context of accelerating global urbanization and the demands for high-quality and sustainable development, the exploration of an urban community living area delineation method based on traffic network analysis offers a fresh perspective for defining urban living areas. This paper employs a traffic network analysis method rooted in objective analysis. It leverages open-source big data on Points of Interest (POI) from public facilities and road network data obtained from relevant urban planning to construct a comprehensive road network dataset for the central urban area. Following the construction of the road network dataset, the paper conducts a service area and accessibility analysis of public facilities, which serves as an initial step in outlining the boundaries of the urban community living area. Subsequently, the delineation is further refined by considering factors such as development boundaries, as well as natural and man-made constraints such as rivers, railways, and major roads. The boundaries of the urban community living area undergo further adjustments, taking into account the scope of the development boundary and various constraining elements such as rivers, railways, and main roads. A final revision is made by considering subjective factors, where the boundaries of the ten-minute urban community living area are ultimately determined through extensive communication and coordination with various stakeholders. This approach is aimed at meeting the current societal development needs, which include breaking down segregation, fostering social interactions, strengthening neighbourly relationships, enhancing overall quality of life, promoting sustainable development, and creating a more humane and sustainable city. Its ultimate goal is to enable residents to feel a sense of belonging and happiness within the bustling urban environment.

AN ANALYSIS OF APACHE SPARK AND GPU PERFORMANCES ON DATABASE SQL QUERIES FOR DISTRIBUTED NETWORKS

The use of GPU in different fields and its successful results initiate efforts to use GPU in database systems. It is also effective in distributed systems and computer networks in that accelerates computational tasks by leveraging parallel processing capabilities across multiple nodes and for tasks that require high computational power, such as network traffic analysis and real-time data processing. Digital transformation in all areas of life has led to the emergence of needs such as increased data diversity and faster data analysis. Upgrading the hardware capacity of the system or software-based studies are possible solutions to analyze this data for meeting the needs. In this study, Apache Spark and GPU performance differences are examined in commonly used SQL queries on big data. In this context, SQL queries such as grouping, sorting, and filtering, which are commonly used in data analysis, are used. While the queries performed with the GPU showed similar results in simple queries compared to the queries performed with Apache Spark, the GPU was completed 3x faster in queries requiring calculation.

Research of ways to improve the security of corporate networks

Objective. The article explores a way to improve the security of a corporate network by using the technique of spoofing sender addresses when forwarding packets between computers. Method. Network traffic analysis methods, network protocol level programming and data packet processing algorithms are used. Computers on the network are connected to one server. When sending packets between computers in the network, the sender's address is replaced, and when a packet is received by another computer, the packet is disassembled, and the real sender's address is inside it. Result. The method allows for effective protection of a corporate network from attacks associated with the forgery of addresses of data packet senders. Conclusion. Using the sender address substitution technique when transmitting data in a corporate network is an effective way to increase security and protection from external threats. Further research is aimed at developing complex and reliable methods for protecting networks.

Practical and lightweight defense against website fingerprinting

Website fingerprinting is a passive network traffic analysis technique that enables an adversary to identify the website visited by a user despite encryption and the use of privacy services such as Tor. Several website fingerprinting defenses built on top of Tor have been proposed to guarantee a user’s privacy by concealing trace features that are important to classification. However, some of the best defenses incur a high bandwidth and/or latency overhead. To combat this, new defenses have sought to be both lightweight—i.e., introduce a small amount of bandwidth overhead—and zero-delay to real network traffic. This work introduces a novel zero-delay and lightweight website fingerprinting defense, called BRO, which conceals the feature-rich beginning of a trace while still enabling the obfuscation of features deeper into the trace without spreading the padding budget thin. BRO schedules padding with a randomized beta distribution that can skew to both the extreme left and right, keeping the applied padding clustered to a finite portion of a trace. This work specifically targets deep learning attacks, which continue to be among the most accurate website fingerprinting attacks. Results show that BRO outperforms other well-known website fingerprinting defenses, such as FRONT, with similar bandwidth overhead.

Optimizing Cyber Threat Detection in IoT: A Study of Artificial Bee Colony (ABC)-Based Hyperparameter Tuning for Machine Learning

In the rapidly evolving landscape of the Internet of Things (IoT), cybersecurity remains a critical challenge due to the diverse and complex nature of network traffic and the increasing sophistication of cyber threats. This study investigates the application of the Artificial Bee Colony (ABC) algorithm for hyperparameter optimization (HPO) in machine learning classifiers, specifically focusing on Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) for IoT network traffic analysis and malware detection. Initially, the basic machine learning models demonstrated accuracies ranging from 69.68% to 99.07%, reflecting their limitations in fully adapting to the varied IoT environments. Through the employment of the ABC algorithm for HPO, significant improvements were achieved, with optimized classifiers reaching up to 100% accuracy, precision, recall, and F1-scores in both training and testing stages. These results highlight the profound impact of HPO in refining model decision boundaries, reducing overfitting, and enhancing generalization capabilities, thereby contributing to the development of more robust and adaptive security frameworks for IoT environments. This study further demonstrates the ABC algorithm’s generalizability across different IoT networks and threats, positioning it as a valuable tool for advancing cybersecurity in increasingly complex IoT ecosystems.

Analyzing Darknet Traffic: Examining how Tor Modifications Affect Onion Service Traffic Classification

The important work of classifying network traffic for control and monitoring is examined in this study. Data protection has taken centre stage as privacy concerns have grown over the last two decades. Online privacy is possible through the Tor network, which is well-known for enabling Onion Services and offering user anonymity. But the abuse of this anonymity—especially with Onion Services—has prompted the government to work on de-anonymizing users. In this work, we address three main goals: first, we achieve over 99% accuracy in distinguishing Onion Service traffic from other Tor traf- fic; second, we assess how well our methods perform in the event that Tor traffic is modified to hide information leaks; and third, we detect the utmost significant article integrations for our classification task. This study tackles issues related to privacy challenges and misuse concerns in network traffic analysis.

From FFT to sparse FFT: Innovations in efficient signal processing for large sparse data

This paper explores the advancements from the traditional Fast Fourier Transform (FFT) to the Sparse Fast Fourier Transform (sFFT) and their implications for efficient signal processing of large, sparse datasets. FFT has long been a fundamental component in digital signal processing, significantly lowering the runtime of the Discrete Fourier Transform. However, the ingress of big data has necessitated much more efficient algorithms. In contrast, the sFFT exploits the sparsity in the signals themselves to reduce computational demand, and it becomes very efficient. This paper will discuss the theoretical backing of these two developments, FFT and sFFT, and the algorithmic development in both. In addition, it will also discuss the practical applications of both with emphasis on how the latter outperforms the former in large, sparse data. Comparative analysis shows that sFFT has far greater efficiency and noise tolerance, which is of value for network traffic analysis, astrophysical data analysis, and real-time medical imaging. The purpose of this paper is to provide clarity regarding these transformations and their relationship to being paradigms in modern signal analysis.

Leveraging Machine Learning: Advancements in Cheating Detection Strategies for Ensuring Fair Online Gaming Environments

Cheating detection in online gaming is a crucial challenge that affects the fairness and integrity of virtual environments. This literature review delves into the advancements made in the field of cheating detection, focusing on machine-learning-based approaches and encrypted network traffic analysis. Various methodologies, including Support Vector Machines, Logistic Regression, and GPU acceleration, are explored in detecting cheating behaviors within different gaming scenarios. The review also examines the application of supervised learning techniques in Unreal Tournament III, showcasing their potential in identifying cheating instances. Additionally, the challenges posed by limited labeled data and covariate shifts in encrypted network traffic analysis are addressed through innovative solutions like the GCI framework. Insights into the interplay of data attributes and classification performance are provided, offering directions for future research. Overall, this review contributes to the understanding of cheating detection strategies and their implications for maintaining equitable and enjoyable online gaming experiences.

A novel graph convolutional networks model for an intelligent network traffic analysis and classification

A novel graph convolutional networks model for an intelligent network traffic analysis and classification

Network Traffic Analysis and Anomaly Detection in a Simulated Environment Using Explainable AI

Network Traffic Analysis and Anomaly Detection in a Simulated Environment Using Explainable AI

An efficient intrusion detection systems in fog computing using forward selection and BiLSTM

Intrusion detection systems (IDS) play a pivotal role in network security and anomaly detection and are significantly impacted by the feature selection (FS) process. As a significant task in machine learning and data analysis, FS is directed toward pinpointing a subset of pertinent features that primarily influence the target variable. This paper proposes an innovative approach to FS, leveraging the forward selection search algorithm with hybrid objective/fitness functions such as correlation, entropy, and variance. The approach is evaluated using the BoT-IoT and TON_IoT datasets. By employing the proposed methodology, our bidirectional long-short term memory (BiLSTM) model achieved an accuracy of 98.42% on the TON_IoT dataset and 98.7% on the BoT-IoT dataset. This superior classification accuracy underscores the efficacy of the synergized BiLSTM deep learning model and the innovative FS approach. The study accentuates the potency of the proposed hybrid approach in FS for IDS and highlights its substantial contribution to achieving high classification performance in internet of things (IoT) network traffic analysis.

Real-Time Network Traffic Analysis and Anomaly Detection to Enhance Network Security and Performance: Machine Learning Approaches

There are numerous proceedings that take place within an actual computer network, and one of them is the monitoring of the network traffic in real-time with the added function of anomaly detection. This research focuses on the use of machine learning to improve these capabilities as stated in the following section. In the context of the current study, the emphasis is made of building powerful anomaly detection models that would be capable to work in real life by defining network and potential threats on their own due to their machine learning capabilities. Furthermore, the study gives a detailed analysis of the more complex methods like feature selection in addition to dimensionality reduction for enhancing the abilities of machine learning algorithms in the management of big data samples for world-wide network traffic. Furthermore, the presented research focuses on the application of definitions of edge computing paradigms to facilitate decentralized processes of the identification of anomalies, thereby enhancing the sensitivity and response time of essential networks. Thus, the research objectives are to address the aforelisted challenges and generate insights into constructing better network security frameworks to prevent and respond to future threats in a precise and effective mechanism.

Enhancing IoT Routing Security and Efficiency: Towards AI-Enabled RPL Protocol

In the rapidly evolving landscape of the Internet of Things (IoT), the security and efficiency of network routing are paramount. The standard Routing Protocol for Low-Power and Lossy Networks (RPL) faces challenges in the form of various cyber-attacks, impacting its reliability and performance. To address this problem, we propose a Novel Algorithm for Network Traffic Analysis and Response (NANTAR), a novel algorithm that optimizes the security and efficiency of RPL routing using AI-based techniques. Through evaluation against traditional RPL, NANTAR has demonstrated remarkable improvements in key performance metrics: a 20% increase in throughput, latency reductions of 20-30%, and more efficient resource utilization. Notably, NANTAR maintains robust network performance with scaling, evidenced by 15-20% lower false positive and negative rates. The algorithm’s efficacy is further highlighted by its high detection rates in the face of diverse attacks and its power consumption efficiency, which is crucial for IoT devices. NANTAR’s adaptability and seamless integration across various IoT platforms affirm its potential as a solution for securing IoT ecosystems, with results indicating significant improvements in key operational metrics.

Machine Learning in Cybersecurity: Advanced Detection and Classification Techniques for Network Traffic Environments

In the digital age, the integrity of business operations and the smoothness of their execution heavily depend on cybersecurity and network efficiency. The need for robust solutions to prevent cyber threats and enhance network functionality has never been more critical. This research aims to utilize machine learning (ML) techniques for the meticulous analysis of network traffic, with the dual goals of detecting anomalies and categorizing network activities to bolster security and performance. Employing a detailed methodology, this study begins with data preparation and progresses through to the deployment of advanced ML models, including logistic regression, decision trees, and ensemble learning techniques. This approach ensures the accuracy of the analysis and facilitates a nuanced understanding of network dynamics. Our findings indicate a notable enhancement in identifying network inefficiencies and in the more accurate classification of network traffic. The application of ML models significantly reduces network delays and bottlenecks by providing a strong defence strategy against cyber threats and network shortcomings, thereby improving user satisfaction, and boosting the organizational reputation as a secure and effective service layer. Conclusively, the research highlights the pivotal role of machine learning in network traffic analysis, offering innovative insights and fresh perspectives on anomaly detection and the identification of malicious activities. It lays a foundation for future explorations and acts as an evaluation benchmark in the fields of cybersecurity and network management.

TLS key material identification and extraction in memory: Current state and future challenges

Memory forensics is a crucial part of digital forensics as it can be used to extract valuable information such as running processes, network connections, and encryption keys from memory. The last is especially important when considering the widely used Transport Layer Security (TLS) protocol used to secure internet communication, thus hampering network traffic analysis. Particularly in the context of cybercrime investigations (such as malware analysis), it is therefore paramount for investigators to decrypt TLS traffic. This can provide vital insights into the methods and strategies employed by attackers. For this purpose, it is first and foremost necessary to identify and extract the corresponding TLS key material in memory.In this paper, we systematize and evaluate the current state of techniques, tools, and methodologies for identifying and extracting TLS key material in memory. We consider solutions from academia but also identify innovative and promising approaches used “in the wild” that are not considered by the academic literature. Furthermore, we identify the open research challenges and opportunities for future research in this domain. Our work provides a profound foundation for future research in this crucial area.

밀집 샘플링 기법을 이용한 네트워크 트래픽 예측 성능 향상

If the future can be predicted from network traffic data, which is a time series, it can achieve effects such as efficient resource allocation, prevention of malicious attacks, and energy saving. Many models based on statistical and deep learning techniques have been proposed, and most of these studies have focused on improving model structures and learning algorithms. Another approach to improving the prediction performance of the model is to obtain a good-quality data. With the aim of obtaining a good-quality data, this paper applies a dense sampling technique that augments time series data to the application of network traffic prediction and analyzes the performance improvement. As a dataset, UNSW-NB15, which is widely used for network traffic analysis, is used. Performance is analyzed using RMSE, MAE, and MAPE. To increase the objectivity of performance measurement, experiment is performed independently 10 times and the performance of existing sparse sampling and dense sampling is compared as a box plot. As a result of comparing the performance by changing the window size and the horizon factor, dense sampling consistently showed a better performance.