Network Traffic Patterns Research Articles

DETECTION OF WIRELESS NETWORK ATTACKS USING SUPERVISED MACHINE LEARNING TECHNIQUE

The security and integrity of computer networks are seriously threatened by network assaults. Keeping a safe network environment requires the capacity to anticipate and stop these threats. Supervised machine learning methods have become powerful instruments for attacking network traffic and spotting patterns that point to malicious behaviour. We provide an in-depth examination of supervised machine learning methods for network attack prediction. We gather the data, preprocess it, extract pertinent features, and structure it so that machine learning algorithms may use it.We assess these algorithms' performance. To understand the fundamental patterns and traits of network assaults, we look at how interpretability the trained models are. This enables network managers to comprehend the types of threats and create suitable. Keywords - Network Attacks, Supervised Machine Learning, Network security,Network traffic analysis, Malicious behavior detection, Feature extraction,Machine learning algorithm,Model interpretability,Network attack patterns,Network defense strategies,Naive Bayes Algorithm, Ridge Classfier ,Random Forest Classfier

Darknet Traffic Analysis Investigating the Impact of Modified Tor Traffic on Onion Service Traffic Classification

Darknet Traffic Analysis Investigating the Impact of Modified Tor Traffic on Onion Service Traffic Classification

Detection of Botnet in IOT Using Machine Learning

The proliferation of Internet of Things (IoT) devices has introduced unprecedented connectivity and convenience but also heightened the vulnerability to botnet attacks. There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This project presents a straightforward approach to detect botnet activity within IoT networks through the utilization of machine learning techniques. By analyzing network traffic patterns and employing unsupervised learning algorithms, we demonstrate an effective method to identify and mitigate botnet threats in IoT environments. By this project we intend to offer a valuable contribution in enhancing the security of IoT ecosystem. Key Word: Internet of Things(IoT),cybersecurity, botnet attacks, machine learning(ML),UNSW-NB15 dataset, exploratory data analysis, XgBoost

Intelligent load balancing in data center software‐defined networks

AbstractIn response to the increasing demand for efficient resource utilization in data center networks (DCNs), the development of intelligent load‐balancing algorithms has become crucial. This article introduces the dual double deep Q network (DDQN) algorithm, designed for software‐defined networking (SDN) environments within data centers. By leveraging deep reinforcement learning, DDQN addresses the challenges posed by dynamic traffic patterns, diverse flow requirements, and the coexistence of elephant and mice flows. Our algorithm adopts a comprehensive SDN approach, evaluating the network's status by analyzing switch load and bandwidth utilization. Using convolutional neural networks for elephant and mice flows in DCN, our algorithm enables adaptive learning and training tailored to the specific demands of elephant flows. Employing a double deep Q network architecture (DDQN), DDQN optimizes paths for both elephant and mice flows independently. Real‐time adaptation mechanisms make routing decisions based on the robust learning capabilities of DDQN, enhancing network utilization and reducing packet loss by generating optimal forwarding paths according to the current network state and traffic patterns. Simulations conducted in a Mininet environment with RYU as the controller, utilizing a fat‐tree data center topology, validate the efficacy of DDQN. The results demonstrate its effectiveness in achieving higher throughput, lower latency, and superior load balancing compared to traditional algorithms like equal‐cost multipath and Hedera.

Network Traffic Virtualization Using Wireshark and Pygeoip

Security analysts possess the requisite expertise to discern various types of network traffic, detect instances of unusually high activity, and pinpoint the specific network nodes responsible. Furthermore, network traffic visualization aids users in pinpointing servers and tracking data flows, thereby enabling researchers to identify potential online threats. Leveraging Wireshark's functionalities as both a packet tracer and capture tool, the process of network analysis hinges on the selection of appropriate packet capture parameters. In this study, Wireshark serves as the cornerstone for creating a network tracking system. By employing Wireshark to capture data packets, analyze them, and subsequently translate the IP addresses into latitude and longitude coordinates, researchers can plot the source and destination IPs on a Google map. This innovative approach allows users to visualize network traffic patterns on a geographical scale and effectively monitor them.

A Comprehensive Survey on Exploring Covert Timing Channels in Network Security

Covert timing channels play pivotal roles in spying, hacking, and data theft, enabling secret communication and surreptitious data exchange. They also find utility in secure conversations, message concealment, security testing, and privacy protection, underscoring their versatile application across diverse domains. However, the accuracy of covert timing channels may be compromised by network traffic patterns and time delays, potentially affecting data integrity. This survey paper primarily provides an brief overview of identifying and mitigating these influencing factors of existing covert timing channels and presents a suggested system that integrates Reversible Covert Timing Channel (CTC) steganography and Elliptic Curve Cryptography (ECC) to ensure secure communication and data encryption. By leveraging this combination, the system aims to enhance the resilience of covert communication channels against adversarial interference while safeguarding the confidentiality and integrity of transmitted data.

Research on gaussian mixture model and its distributed data mining algorithm in wireless sensor networks

Optimization of the routing represents an important challenge when considering the rapid development of Wireless Sensor Networks (WSN), which involve efficient energy methods. Applying the effectiveness of a Deep Neural Network (DNN) and a Gaussian Mixture Model (GMM), the present article proposes an innovative method for attaining Energy-Efficient Routing (EER) in WSN. When it comes to dealing with dynamic network issues, conventional routing protocols generally conflict, resulting in unsustainable Energy consumption (EC). By applying algorithms based on data mining to adapt routing selections in an effective procedure, the GMM + DNN methodology that has been developed is able to successfully address this problem. The GMM is a fundamental Feature Extraction (FE) method for accurately representing the features of statistical analysis associated with network parameters like signal frequency, the amount of traffic, and channel states. By learning from previous data collection, the DNN, which relies on these FE, provides improved routing selections, resulting in more efficient use of energy. Since routing paths are constantly optimized to ensure dynamic adaptation, where less energy is used, networks last longer and perform more efficiently. Network simulations highlight the GMM + DNN method’s effectiveness and depict how it outperforms conventional routing methods while preserving network connectivity and data throughput. The GMM + DNN’s adaptability to multiple network topologies and traffic patterns and its durability make it an efficient EER technique in the diverse WSN context. The GMM + DNN achieves an EC of 0.561 J, outperforming the existing state-of-the-art techniques.

Network Traffic Characteristics and Analysis in Recent Mobile Games

The landscape of mobile gaming has evolved significantly over the years, with profound changes in network reliability and traffic patterns. In the early 2010s, mobile games faced challenges due to unreliable networks and primarily featured asynchronous gameplay. However, in the current era, modern mobile games benefit from robust network connectivity, mirroring PC gaming experiences by relying on persistent connections to game servers. This shift prompted us to conduct an in-depth traffic analysis of two mobile games that represent opposite ends of the genre spectrum: a massively multiplayer game resembling PC MMORPGs with tightly synchronized gameplay, and a single-player puzzle game that incorporates asynchronous social interactions. Surprisingly, both games exhibited remarkably similar traffic footprints; small packets with short inter-packet arrival times, indicating their high expectations for network reliability. This suggests that game developers now prioritize network quality similarly to their PC gaming counterparts. Additionally, our analysis of packet lengths unveiled that recent mobile games predominantly employ short packets dominated by a few key packet types closely tied to player actions, which conforms to observations from PC online games. However, the self-similarity in traffic patterns, a notable feature in PC online games, only partially explains the traffic in mobile games, varying across genres. These findings shed light on the evolving traffic patterns in mobile games and emphasize the need for further research in this dynamic domain.

Multilayer Seasonal Autoregressive Integrated Moving Average Models for Complex Network Traffic Analysis

The ever-increasing amount of network traffic generated by various devices and applications has made it crucial to have efficient methods for analyzing and managing network traffic. Traditional approaches, such as statistical modeling, have yet to be proven enough due to network traffic's complex nature and dynamic characteristics. Recent research has shown the effectiveness of complex network analysis techniques for understanding network traffic patterns. This paper proposes multilayer seasonal autoregressive integrated moving average models for analyzing and predicting network traffic. This approach considers the seasonal patterns and interdependencies between different layers of network traffic, allowing for a more accurate and comprehensive representation of the data. The Multilayer Seasonal Autoregressive Integrated Moving Average (MSARIMA) model consists of multiple layers, each representing a different aspect of network traffic, such as time of day, day of week, or type of traffic. Each layer is modeled separately using SARIMA, a popular time series forecasting technique. The models for different layers are combined to capture the overall behavior of network traffic. The proposed approach has several benefits over traditional statistical approaches. It can capture network traffic's complex and dynamic nature, including short-term and long-term seasonal patterns. It also allows for the detection of anomalies and the prediction of future traffic patterns with high accuracy.

Exploring a novel framework for DoS/DDoS attack detection and simulation in contemporary networks

Currently, the internet serves as the predominant means of communication and is utilized by a vast number of individuals worldwide. Simultaneously, the commercial aspect of the internet is contributing to a rise in susceptibility to cybercrimes, leading to a significant surge in the occurrence of distributed Denial of Service (DDoS) assaults over the last decade. DoS/DDoS assaults primarily target network resources such as network bandwidth, CPU time, memory consumption, web servers, and network switches. Network security is an essential and crucial problem in the modern interconnected society. Numerous studies have been undertaken by multiple researchers thus far in order to identify this attack. However, there is still room for improvement in past investigations. This paper presents a novel approach for detecting and simulating DoS/DDoS attacks in modern networking environments, introducing a new paradigm. It is done in a controlled environment. The primary focus of this work is to simulate an attacker's perspective of a DoS/DDoS attack by repeatedly sending huge SYN flood packets to a specific target or network server using the hping3 tool. On the server side, the proposed attacker detector script continuously monitors incoming network connections on the network server using the netstat command. It identifies potential DoS/DDoS attacks by analyzing the connection count and comparing connections count with an assumed threshold. This experiment results in 61% CPU usage and 7.1% memory consumption while a DDoS attack triggers on the target server. Additionally, the proposed script performs statistical analysis and displays warning messages on the console when suspicious activity is detected on the network server. Wireshark is also utilized in this work to detect anomalous network traffic patterns in order to identify distributed denial-ofservice (DDoS) attacks that are targeting a network server. Additionally, it offers the capability to block the IP address of the attacker if the configuration allows for it. This proposed approach efficiently identifies DDoS activity in real-time network traffic, further helping to improve network security.

Trustworthy-Based Authentication Model with Intrusion Detection for IoT-Enabled Networks with Deep Learning Algorithm

In the burgeoning field of the Internet of Things (IoT), ensuring secure and trustworthy communication between devices is paramount. This paper proposes a novel Trustworthy-Based Authentication Model (TBAM) integrated with Intrusion Detection Systems (IDS) leveraging deep learning algorithms to secure IoT-enabled networks. The proposed model addresses the dual challenges of authenticating legitimate devices and detecting malicious intrusions. Specifically, we employ a Convolutional Neural Network (CNN) to analyse network traffic patterns for intrusion detection, leveraging its prowess in feature extraction and classification. Additionally, a Long Short-Term Memory (LSTM) network is utilized for continuous monitoring and anomaly detection, capturing temporal dependencies in data flows that are indicative of potential security threats. The authentication mechanism integrates a trust evaluation system that assigns trust scores to devices based on their behaviour, enhancing the model's capability to distinguish between trusted and malicious entities. Our extensive experiments on real-world IoT datasets demonstrate that the TBAM significantly outperforms traditional security models in terms of detection accuracy, false-positive rate, and computational efficiency. Specifically, our model achieves a detection accuracy of 98.7%, a false-positive rate of 1.2%, and a processing time reduction of 30% compared to baseline models. This work contributes a robust, scalable, and efficient solution to the pressing security concerns in IoT networks, paving the way for more secure and reliable IoT applications.

Machine Learning Approaches for Smart Home Device Recognition from Network Traffic

This paper presents a comprehensive methodology for precisely identifying smart home devices through the intricate analysis of network traffic data. The research framework comprises a multifaceted approach, encompassing critical stages such as in-depth exploratory data analysis, sophisticated feature engineering, robust machine learning modeling, iterative model refinement, and meticulous interpretation of results. The study evaluates various machine learning algorithms, with the Random Forest Classifier emerging as the optimal choice due to its superior performance in device recognition. Subsequently, the Random Forest Classifier's effectiveness is substantially bolstered through an exhaustive hyperparameter tuning procedure. This optimization effort culminates in a final accuracy score of 87.79%, demonstrating the excellent efficacy of this approach in precisely identifying smart home devices based on intricate network traffic patterns. Future research endeavors may concentrate on further refining these models and exploring additional feature sets to comprehensively augment their capabilities, thereby addressing the constantly evolving landscape of smart home technology and its associated security concerns.

Exploring the Benefits of Carbon-Aware Routing

Carbon emissions associated with fixed networks can be significant. However, accounting for these emissions is hard, requires changes to deployed equipment, and has contentious benefits. This work sheds light on the benefits of carbon aware networks, by exploring a set of potential carbon-related metrics and their use to define link-cost in carbon-aware link-state routing algorithms. Using realistic network topologies, traffic patterns and grid carbon intensity, we identify useful metrics and limitations to carbon emissions reduction. Consequently, a new heuristic carbon-aware traffic engineering algorithm, CATE, is proposed. CATE takes advantage of carbon intensity and routers' dynamic power consumption, combined with ports power down, to minimize carbon emissions. Our results show that there is no silver bullet to significant carbon reductions, yet there are promising directions without changes to existing routers' hardware.

Flow and unified information-based DDoS attack detection system for multi-topology IoT networks

Internet of Things (IoT) networks are vulnerable to Distributed Denial of Service (DDoS) attacks, which can degrade their Quality of Service (QoS). In general, DDoS attacks are classified into high- and low-volume attacks. Existing statistical-based methods for DDoS attack detection in IoT networks are effective only for high-volume or low-volume attacks, but not for both. The majority of research in this domain relies on single-dimensional analysis and static thresholds. In response to these limitations, this paper introduces a Flow and Unified Information-based DDoS (FLUID) attack detection system, a lightweight statistical approach, for DDoS attack detection in IoT networks. The FLUID system incorporates multi-dimensional analysis by integrating unified information and flow behavior to effectively identify both high- and low-volume DDoS attacks. FLUID utilizes entropy and distance metrics, such as Kullback–Leibler (KL) divergence and greedy bin-packing, as unified information measures to distinguish legitimate traffic from malicious activity. Additionally, it examines flow behavior to gain insights into network traffic patterns. Notably, the FLUID system maintains its lightweight nature through a streamlined set of network features and optimized computational efficiency. Evaluations on real-world IoT client/server and Event-Driven Architecture (EDA) testbeds with the ToN-IoT, CICIDS 2017, CICIDS 2019, and DoS/DDoS-MQTT-IoT datasets show that the FLUID system can achieve over 90% detection accuracy for both high- and low-volume DDoS attacks.

Enhancing Intrusion Detection Systems with a Hybrid Deep Learning Model and Optimized Feature Composition

Systems for detecting intrusions (IDS) are essential for protecting network infrastructures from hostile activity. Advanced methods are required since traditional IDS techniques frequently fail to properly identify sophisticated and developing assaults. In this article, we suggest a novel method for improving IDS performance through the use of a hybrid deep learning model and feature composition optimization. RNN and CNN has strengths that the proposed hybrid deep learning model leverages to efficiently capture both spatial and temporal correlations in network traffic data. The model can extract useful features from unprocessed network packets using CNNs and RNNs, giving a thorough picture of network behaviour. To increase the IDS's ability to discriminate, we also offer feature optimization strategies. We uncover the most pertinent and instructive features that support precise intrusion detection through a methodical feature selection and engineering process. In order to reduce the computational load and improve the model's efficiency without compromising detection accuracy, we also use dimensionality reduction approaches. We carried out extensive experiments using a benchmark dataset that is frequently utilized in intrusion detection research to assess the suggested approach. The outcomes show that the hybrid deep learning model performs better than conventional IDS methods, obtaining noticeably greater detection rates and lower false positive rates. The performance of model is further improved by the optimized feature composition, which offers a more accurate depiction of network traffic patterns.

Network Intrusion Detection Using Autoencode Neural Network

In today's interconnected digital landscape, safeguarding computer networks against unauthorized access and cyber threats is of paramount importance. NIDS play a crucial role in identifying and mitigating potential security breaches. This research paper explores the application of autoencoder neural networks, a subset of deep learning techniques, in the realm of Network Intrusion Detection.Autoencoder neural networks are known for their ability to learn and represent data in a compressed, low-dimensional form. This study investigates their potential in modeling network traffic patterns and identifying anomalous activities. By training autoencoder networks on both normal and malicious network traffic data, we aim to create effective intrusion detection models that can distinguish between benign and malicious network behavior.The paper provides an in-depth analysis of the architecture and training methodologies of autoencoder neural networks for intrusion detection. It also explores various data preprocessing techniques and feature engineering approaches to enhance the model's performance. Additionally, the research evaluates the robustness and scalability of autoencoder-based NIDS in real-world network environments. Furthermore, ethical considerations in network intrusion detection, including privacy concerns and false positive rates, are discussed. It addresses the need for a balanced approach that ensures network security while respecting user privacy and minimizing disruptions. operation. This approach compresses the majority samples & increases the minority sample count in tough samples so that the IDS can achieve greater classification accuracy.

Secure routing in the Internet of Things (IoT) with intrusion detection capability based on software-defined networking (SDN) and Machine Learning techniques

Routing and security are the two main prerequisites for ensuring the correct operation of wireless networks. The importance of these cases doubles in wide networks such as IoT. This paper presents an algorithm to improve Secure Routing in IoT called SRAIOT. This algorithm uses a hierarchical structure to determine the connections between network components and data transfer routing. In SRAIOT, the network structure is managed hierarchically and through SDN. For this purpose, the IoT network is first divided into a set of subnets using the SDN solution, communication control and authentication are managed using the controller nodes of each subnet. The communication between two objects (located in different subnets) will be possible if their identity is confirmed through the controller nodes related to them. On the other hand, in order to identify the sources of attacks and network security threats, the controller nodes in each subnet monitor the network traffic pattern using an ensemble learning model and identify possible attacks in their subnet. The performance of SRAIOT was tested in the simulation, and the results were compared with previous methods. The results of these tests show that SRAIOT improves network performance regarding routing and detecting attacks.

An Analysis of Temporal Features in Multivariate Time Series to Forecast Network Events

Analyzing network traffic over time is crucial for understanding the changes in network activity. To properly examine network traffic patterns over time, multiple network events in each timestamp need to be converted to time series data. In this study, we propose a new approach to transform network traffic data into time series formats by extracting temporal features to analyze normal/attack patterns. The normal patterns indicate network traffic occurred without any intrusion-related activities, whereas the attack patterns denote potential threats that deviate from the normal patterns. To evaluate the features, long short-term memory (LSTM) is applied to forecast multi-step network normal and attack events. Visual analysis is also performed to enhance the understanding of key features in the network. We compared the performance differences using time scales of 60 and 120 s. Upon evaluation, we found that the temporal features extracted with the 60 s time scale exhibited better performance in forecasting future network events.

Enhancing IoT network security through deep learning-powered Intrusion Detection System

The rapid growth of the Internet of Things (IoT) has brought about a global concern for the security of interconnected devices and networks. This necessitates the use of efficient Intrusion Detection System (IDS) to mitigate cyber threats. Deep learning (DL) techniques provides a promising approach to effectively detect irregularities in network traffic, enhancing IoT network security and reducing cyber threats. In this paper, DL-based IDS is proposed using Feed Forward Neural Networks (FFNN), Long Short-Term Memory (LSTM), and Random Neural Networks (RandNN) to protect IoT networks from cyberattacks. Each DL model has its potential benefit as reported in this paper. For example, the FFNN can handle complex IoT network traffic patterns, while the LSTM is good in capturing long-term dependencies present in the network traffic. With its random connections and flexible dynamics, the RandNN model uses its data learning ability to adapt and learn from network data. These algorithms boost cybersecurity by enabling defense mechanisms against challenging cyber threats and ensuring the security of sensitive data as IoT networks expand. The proposed technique exhibits superior performance when compared with the current state-of-the-art DL-IDS using the CIC-IoT22 dataset. An accuracy of 99.93 % is achieved for the FFNN model, 99.85 % for the LSTM model, and 96.42 % for the RandNN model in detecting intrusion. Moreover, the models have the potential to enhance intrusion detection in IoT networks by generating swift responses to security problems in IoT networks.

A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques

The study in this paper characterizes lightweight IoT networks as being established by devices with few computer resources, such as reduced battery life, processing power, memory, and, more critically, minimal security and protection, which are easily vulnerable to DDoS attacks and propagating malware. A DDoS attack detection model is crucial for attacks in various industries, ensuring the availability and reliability of their networks and systems. The model distinguishes between legitimate and malicious traffic by analyzing network traffic patterns and identifying anomalies. This safeguards critical infrastructure, preserves business continuity, and protects the user experience, minimizing the impact of DDoS attacks. Numerous scholars have studied the notion that protecting lightweight IoT networks essentially requires improving intrusion detection systems. This research is valuable, as it follows a tailored pre-processing methodology specific to IoT network challenges, addressing a pressing need in cybersecurity by focusing on a growing concern related to IoT devices and DDoS attacks, enhancing the security of essential network systems in various industries by effectively detecting DDoS attacks, and developing a lightweight intrusion detection system that aligns with the limited resources of IoT devices. This manuscript proposes a compact and lightweight intrusion detection system that blends machine learning classifiers with a fresh approach to data pre-processing. The handling of missing values, data standardization using Standard Scalar, feature selection using ExtraTreeClassifier wherein only the 15 best features are extracted, and anomaly detection using a classifier are performed. The network dataset of TON-IOT and BOT-IOT datasets is used for experiments, specifically binary classifications and multiple-class classification for the experiment with DDoS and all attacks, respectively. There is an imbalance between the TON-IOT and BOT-IOT attack classes. In trials using the TON-IOT and BOT-IOT datasets, the classes were balanced using several iterations of the SMOTE approach. This research provides a number of classifier types, namely logistic regression, random forest, naïve bayes, artificial neural network, and k nearest neighbor algorithms, which are used to build a lightweight intrusion detection system that is ideally suited for protecting against DDoS attacks in IoT networks. The time taken to train and predict the DDoS attacks is also implemented. Random forest performed well under TON-IOT and naïve bayes performed well under BOT-IOT under binary and multiple-class classification, achieving an accuracy of 100% with less training and prediction time.