Service Attacks Research Articles

Attack-Resilient Distributed Nash Equilibrium Seeking of Uncertain Multiagent Systems Over Unreliable Communication Networks.

This article investigates the distributed Nash equilibrium (NE) seeking problem of uncertain multiagent systems in unreliable communication networks. In this problem, the action of each agent is subject to a class of nonlinear systems with uncertain dynamics, and the communication network among agents will be affected by the nonperiodic denial of service (DoS) attacks. Note that, in this insecure network environment, the existence of DoS attacks will directly destroy the connectivity of the network, which leads to performance degradation or even failure of the most existing distributed NE seeking algorithms. To address this problem, we propose a two-stage distributed NE seeking strategy, including the attack-resilient distributed NE estimator and the neuroadaptive tracking controller. The estimator based on the projection subgradient method and the consensus protocol can converge exponentially to virtual NE against DoS attacks. Then, the neuroadaptive tracking controller is designed for uncertain multiagent systems with the output of the estimator as the reference signal such that the actual action of all agents can reach NE. Based on the Lyapunov stability theory and improved average dwell time automaton, the stability of the estimator and the controller is proven, and all signals in the closed-loop system are uniformly bounded. Numerical examples are presented to verify the effectiveness of the proposed strategy.

Machine learning for network defense: automated DDoS detection with telegram notification integration

As the prevalence and sophistication of distributed denial of service (DDoS) attacks escalate, the imperative for advanced defense mechanisms becomes paramount, especially in rapidly growing digital landscapes like Indonesia. This research presents the development of an innovative intrusion detection system (IDS) that harnesses machine learning (ML) algorithms to automate the detection of DDoS attacks in real time. By monitoring TCP streams, the system utilizes ML-enhanced IDS components to identify malicious traffic patterns indicative of DDoS activities. An automatic alert is dispatched to network administrators via Telegram upon detection, ensuring immediate awareness and facilitating swift countermeasures. Additionally, the system embodies a self-improving architecture by retraining its ML model with newly encountered attack data, thus continuously refining its detection capabilities. The system's efficacy, marked by its adaptive learning and proactive notification system, not only contributes to the fortification of network security but also underscores the potential for ML in cybersecurity within Indonesia’s expanding digital domain. The deployment of this system is anticipated to significantly bolster cybersecurity infrastructure by addressing the urgent need for advanced and responsive defense strategies against the evolving landscape of cyber threats.

Representation-Learning-Based CNN for Intelligent Attack Localization and Recovery of Cyber-Physical Power Systems.

Enabled by the advances in communication networks, computational units, and control systems, cyber-physical power systems (CPPSs) are anticipated to be complex and smart systems in which a large amount of data are generated, exchanged, and processed for various purposes. Due to these strong interactions, CPPSs will introduce new security vulnerabilities. To ensure secure operation and control of CPPSs, it is essential to detect the locations of the attacked measurements and remove the state bias caused by malicious cyber-attacks such as false data inject attack, jamming attack, denial of service attack, or hybrid attack. Accordingly, this article makes the first contribution concerning the representation-learning-based convolutional neural network (RL-CNN) for intelligent attack localization and system recovery of CPPSs. In the proposed method, the cyber-attacks' locational detection problem is formulated as a multilabel classification problem for CPPSs. An RL-CNN is originally adopted as the multilabel classifier to explore and exploit the implicit information of measurements. By comparing with previous multilabel classifiers, the RL-CNN improves the performance of attack localization for complex CPPSs. Then, to automatically filter out the cyber-attacks for system recovery, a mean-squared estimator is used to handle the difficulty in state estimation with the removal of contaminated measurements. In this scheme, prior knowledge of the system state is obtained based on the outputs of the stochastic power flow or historical measurements. The extensive simulation results in three IEEE bus systems show that the proposed method is able to provide high accuracy for attack localization and perform automatic attack filtering for system recovery under various cyber-attacks.

Hybrid RNN-LSTM Networks for Enhanced Intrusion Detection in Vehicle CAN Systems

Electric vehicles (EVs) use electric motors for propulsion, relying on electric energy stored in batteries or other energy storage devices. The standard communication protocol used in EVs is the Control Area Network (CAN), a communication protocol widely used in the automotive industry for networking and communication between various components within a vehicle. CAN protocol, designed without any care about protection, as automotive systems become more connected, the vulnerability to cyber threats, including intrusion attacks. The most common intrusion attacks on EVs are Denial of Service (DoS), Fuzzy, and Impersonation Attacks. These become a significant challenge due to the imperative need for robust Intrusion Detection Systems (IDS) in CAN networks. This paper explores the application of advanced deep learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, to enhance the effectiveness of intrusion detection in the EV domain. We will use a hybrid Deep-learning model to improve the analysis. First, we will apply the RNN model, and the output will come as input for the second model, LSTM. Our proposed hybrid model achieved an accuracy of 93%. The outcomes of this research contribute to advancing cybersecurity measures in vehicular networks, ensuring the integrity and safety of connected vehicles. The applicability of RNN and LSTM techniques in the context of CAN networks demonstrates their potential to evolve as integral components of next-generation intrusion detection systems, fostering a secure and resilient automotive ecosystem.

ANALYSIS OF BLOCKCHAIN TECHNOLOGY IN CYBERSECURITY IN THE FIELD OF ACCOUNTING: SYSTEMATIC LITERATURE REVIEW

In the digital era, cybersecurity has become a primary focus due to increasingly complex threats. Blockchain technology, promising high levels of security, has garnered attention in the field of cybersecurity and opened up new opportunities in the world of accounting. However, the success of blockchain-based accounting practices also raises concerns related to overall security. This research utilizes a Systematic Literature Review (SLR) to understand the integration of blockchain in safeguarding data security, particularly in the context of accounting. The research findings indicate that the Internet of Things (IoT) is a primary focus within the theme of blockchain in cybersecurity, with a positive impact on the efficiency and accuracy of financial data management. However, vulnerabilities to Distributed Denial of Service (DDoS) and Sybil Attacks in blockchain technology underscore the need for innovation in developing more robust security solutions in the field of accounting. Therefore, the implications of this research are to support the development of proactive blockchain security solutions against dynamic cyber threats, ensuring the integrity of financial data and the sustainability of company operations in an increasingly advanced digital era.

DAR-LFC: A data-driven attack recovery mechanism for Load Frequency Control

In power systems, generation must be maintained in constant equilibrium with consumption. A key indicator for this balance is the frequency of the power grid. The load frequency control (LFC) system is responsible for maintaining the frequency close to its nominal value and the power deviation of tie-lines at their scheduled levels. However, the remote communication system of LFC exposes it to several cyber threats. A successful cyberattack against LFC attempts to affect the field measurements that are transferred though its remote control loop. In this work, a data-driven, attack recovery method is proposed against denial of service and false data injection attacks, called DAR-LFC. For this purpose, a deep neural network is developed that generates estimations of the area control error (ACE) signal. When a cyberattack against the LFC occurs, the proposed estimator can temporarily compute and replace the affected ACE, mitigating the effects of the cyberattacks. The effectiveness and the scalability of the DAR-LFC is verified on a single and a two area LFC simulations in MATLAB/Simulink.

IPv6 addressing with hidden duplicate address detection to mitigate denial of service attacks in the internet of drone

SummaryThe fusion of drones with the Internet, termed the Internet of Drones (IoD), resembles the Internet of Things (IoT) paradigm. In IoD, drones necessitate unique identifiers similar to IPv6 addresses. This paper presents a novel approach for securing the duplicate address detection (DAD) process in the IoD environment. This paper focuses on addressing the security challenges posed by malicious drones in the DAD process. The proposed solution introduces a hidden DAD (H‐DAD) process, to mitigate attacks on the DAD process. In this scheme, drones transmit a transformed portion of their interface identifier (IID) while concealing the full IID, thus thwarting potential disruptions by malicious entities. We evaluated the effectiveness of the H‐DAD scheme through experimental analysis using Python with SimPy, comparing it with the Improved DAD, Secure DAD, and Standard DAD schemes. Our results demonstrate a notable improvement, with the Hidden DAD scheme achieving a 100% address success rate under attack. Moreover, it exhibits approximately 5% and 12% lower overhead and energy consumption, respectively, compared to the other schemes.

Distributed Denial of Service Attack Detection in IoT Networks using Deep Learning and Feature Fusion: A Review

The explosive growth of Internet of Things (IoT) devices has led to escalating threats from distributed denial of service (DDoS) attacks. Moreover, the scale and heterogeneity of IoT environments pose unique security challenges, and intelligent solutions tailored for the IoT are needed to defend critical infrastructure. The deep learning technique shows great promise because automatic feature learning capabilities are well suited for the complex and high-dimensional data of IoT systems. Additionally, feature fusion approaches have gained traction in enhancing the performance of deep learning models by combining complementary feature sets extracted from multiple data sources. This paper aims to provide a comprehensive literature review focused specifically on deep learning techniques and feature fusion for DDoS attack detection in IoT networks. Studies employing diverse deep learning models and feature fusion techniques are analysed, highlighting key trends and developments in this crucial domain. This review provides several significant contributions, including an overview of various types of DDoS attacks, a comparison of existing surveys, and a thorough examination of recent applications of deep learning and feature fusion for detecting DDoS attacks in IoT networks. Importantly, it highlights the current challenges and limitations of these deep learning techniques based on the literature surveyed. This review concludes by suggesting promising areas for further research to enhance deep learning security solutions, which are specifically tailored to safeguarding the fast-growing IoT infrastructure against DDoS attacks.

Cybersecurity Considerations for Smart Bangladesh: Challenges and Solutions

Cybersecurity stands as a cornerstone for safeguarding the integrity, security, and prosperity of a nation in the digital era. It involves a multifaceted approach to protect interconnected digital systems, critical infrastructure, and sensitive data. In the context of a smart nation, where interconnected devices, sensors, and systems are integrated to enhance efficiency and quality of life, cybersecurity becomes even more crucial. As Bangladesh progresses towards its vision of becoming a Smart Nation, characterized by extensive integration of digital technologies into various sectors, cybersecurity emerges as a critical concern. The rapid digitization of infrastructure and services, driven by initiatives such as smart cities, e-governance, and digital healthcare, introduces new vulnerabilities and threats that must be addressed to safeguard against cyber-attacks and protect sensitive data. This paper explores the cybersecurity landscape of Bangladesh in the context of its Smart Nation aspirations, identifying key challenges and proposing solutions to mitigate risks. The cybersecurity challenges facing Smart Bangladesh initiatives are multifaceted and include inadequate cybersecurity policies, limited awareness and education, insufficient investment in cybersecurity infrastructure, and the proliferation of Internet of Things (IoT) devices with inherent vulnerabilities. These challenges expose smart systems to a range of threats, including malware and ransomware attacks, data breaches, insider threats, Distributed Denial of Service (DDoS) attacks, and IoT vulnerabilities. To address these challenges, stakeholders must adopt a proactive and multi-layered approach to cybersecurity. Recommendations include the development of comprehensive cybersecurity policies, enhanced public awareness and education campaigns, investment in cybersecurity infrastructure, implementation of secure-by-design principles in smart infrastructure development, and fostering public-private partnerships to share threat intelligence and resources. By prioritizing cybersecurity considerations and implementing robust cybersecurity measures, Bangladesh can build a resilient and secure digital ecosystem that supports its Smart Nation goals. However, addressing cybersecurity challenges requires coordinated efforts from government agencies, private sector organizations, academia, and civil society to create a cyber-resilient environment conducive to sustainable development and innovation. The author found this area of utmost interest and decided to delve deep into the aspects related to cybersecurity, in order to develop a well-designed journey towards a digitally transformed Bangladesh.

Cryptanalysis with improvement on lattice‐based authenticated key exchange protocol for mobile satellite communication networks

SummaryThe mobile satellite communication (MSC) system represents a crucial means of communication, facilitating connections between mobile users and network control centres through satellites. Given the wireless nature of satellite communication, ensuring communication security is paramount for maintaining accountability. Key exchange and authentication (KEA) mechanisms are commonly employed to secure data transmitted over insecure or open channels. Developing a lattice‐based KEA protocol for MSC systems is imperative to achieve both quantum security and efficient communication. In this direction, Yadav et al. devised ring learning with an error‐based KEA technique to ensure a quantum‐safe environment. Their scheme is quantum‐safe and satisfies desirable security attributes but has low efficiency in computation. Moreover, we have identified that their scheme is susceptible to denial of service attack and session key established at user and NCC ends is different. To overcome the flaws of Yadav et al., we propose a lattice‐based KEA protocol for the MSC system to improve computation efficiency, and get resistance against key mismatch attack. The security analysis of the proposed scheme is presented in the random oracle model. Comparative study is also presented to observe advantages in computation efficiency.

Improvement of Distributed Denial of Service Attack Detection through Machine Learning and Data Processing

The early and accurate detection of Distributed Denial of Service (DDoS) attacks is a fundamental area of research to safeguard the integrity and functionality of organizations’ digital ecosystems. Despite the growing importance of neural networks in recent years, the use of classical techniques remains relevant due to their interpretability, speed, resource efficiency, and satisfactory performance. This article presents the results of a comparative analysis of six machine learning techniques, namely, Random Forest (RF), Decision Tree (DT), AdaBoost (ADA), Extreme Gradient Boosting (XGB), Multilayer Perceptron (MLP), and Dense Neural Network (DNN), for classifying DDoS attacks. The CICDDoS2019 dataset was used, which underwent data preprocessing to remove outliers, and 22 features were selected using the Pearson correlation coefficient. The RF classifier achieved the best accuracy rate (99.97%), outperforming other classifiers and even previously published neural network-based techniques. These findings underscore the feasibility and effectiveness of machine learning algorithms in the field of DDoS attack detection, reaffirming their relevance as a valuable tool in advanced cyber defense.

PETRAK: A solution against DDoS attacks in vehicular networks

In recent years, the frequently reported incidents of Distributed Denial of Service assaults on vehicular networks in various countries have made researchers find new protective solutions. DDoS attacks can propagate through the charging points for electric vehicles in a charging station and affect the production of critical infrastructures such as electric grids. Existing solutions are efficient in attack detection; however, current systems do not offer multi-level protection, and zero-day vulnerabilities are prone to escape from the detection systems.In this paper, we address the problems mentioned above and introduce the first Machine Learning (ML)–based DDoS protective solution to combine prevention and detection mechanisms in vehicular networks. To be more specific, our proposed model is the first to consider the adaptive traffic threshold to generate the alarm for a suspicious amount of traffic flow in an Intrusion Detection Prevention System (IDPS). We call our proposed approach Protecting vEhicular neTworks against distRibuted deniAl of service attacKs (PETRAK). PETRAK uses four functions: prevention, alarm, training, and detection. The alarming system uses the flow parameters and activates the detection module to detect malicious packets. The prevention system works in two modes: immediate and future. PETRAK uses logistic regression to identify incoming packets and signatures of malicious packets to prevent future attacks. We also show that our proposed model is implacable towards advanced post-quantum cryptography-based traffic and also able to analyse side-channel attacks. We run a comprehensive set of experiments to test PETRAK on our synthesized dataset, KDDCUP’99 dataset, CIC-MalMem-2022, and the ToN-IoT dataset. We observe that PETRAK shows an accuracy of 99%. The results claim the efficiency of PETRAK in detecting and preventing DDoS attacks in vehicular networks.

KS-SDN-DDoS: A Kafka streams-based real-time DDoS attack classification approach for SDN environment

Software-Defined Networking (SDN) is a modern networking architecture that segregates control logic from data plane and supports a loosely coupled architecture. It provides flexibility in this advanced networking paradigm for any changes. Further, it controls the complete network in a centralized using controller(s). However, it comes with several security issues: Exhausting bandwidth and flow tables, Distributed Denial of Service (DDoS) attacks, etc. DDoS is a powerful attack for Internet-based applications and services, traditional and SDN paradigms. In the case of the SDN environment, attackers frequently target the central controller(s). This paper proposes a Kafka Streams-based real-time DDoS attacks classification approach for the SDN environment, named KS-SDN-DDoS. The KS-SDN-DDoS has been designed using highly scalable H2O ML techniques on the two-node Apache Hadoop Cluster (AHC). It consists of two modules: (i) Network Traffic Capture (NTCapture) and (ii) Attack Detection and Traffic Classification (ADTClassification). The NTCapture is deployed on the two nodes Apache Kafka Streams Cluster (AKSC-1). It captures incoming network traffic, extracts and formulates attributes, and publishes significant network traffic attributes on the Kafka topic. The ADTClassification is deployed on the two nodes Apache Kafka Streams Cluster (AKSC-2). It consumes network flows from the Kafka topic, classifies it based on the ten attributes, and publishes it to the decision Kafka topic. Further, it saves attributes with outcome to the Hadoop Distributed File System (HDFS). The KS-SDN-DDoS approach is designed and validated using the recent “DDoS Attack SDN dataset”. The result shows that the proposed system gives better classification accuracy (100%).

Predicting DoS and DDoS attacks in network security scenarios using a hybrid deep learning model

Abstract Network security faces increasing threats from denial of service (DoS) and distributed denial of service (DDoS) attacks. The current solutions have not been able to predict and mitigate these threats with enough accuracy. A novel and effective solution for predicting DoS and DDoS attacks in network security scenarios is presented in this work by employing an effective model, called CNN-LSTM-XGBoost, which is an innovative hybrid approach designed for intrusion detection in network security. The system is applied and analyzed to three datasets: CICIDS-001, CIC-IDS2017, and CIC-IDS2018. We preprocess the data by removing null and duplicate data, handling imbalanced data, and selecting the most relevant features using correlation-based feature selection. The system is evaluated using accuracy, precision, F1 score, and recall. The system achieves a higher accuracy of 98.3% for CICIDS-001, 99.2% for CICIDS2017, and 99.3% for CIC-ID2018, compared to other existing algorithms. The system also reduces the overfitting of the model using the most important features. This study shows that the proposed system is an effective and efficient solution for network attack detection and classification.

Leveraging datasets for effective mitigation of DDoS attacks in software-defined networking: significance and challenges

Software-Defined Networking (SDN) has emerged as a transformative paradigm for network management, offering centralized control and programmability. However, with the proliferation of Distributed Denial of Service (DDoS) attacks that pose significant threats to network infrastructures, effective mitigation strategies are needed. The subject matter of this study is to explore the importance of datasets in the mitigation of DDoS attacks in SDN environments. The paper discusses the significance of datasets for training machine learning models, evaluating detection mechanisms, and enhancing the resilience of SDN-based defense systems. Goal of the paper is to assist researchers in effectively selecting and usage of datasets for DDoS mitigation in SDN, thereby maximizing benefits and overcoming challenges involved in dataset selection. This paper outlines the challenges associated with dataset collection, labeling, and management, along with potential solutions to address these challenges. Effective detection and mitigation of DDoS attacks in SDN require robust datasets that capture the diverse and evolving nature of attack scenarios. Characterization of tasks for each section is as follows: Importance of datasets in DDoS attack mitigation in SDN, challenges in dataset utilization in DDoS mitigation in SDN, Guidelines for dataset selection, comparison of datasets used and their results and different dataset usage according to the need. Methodology involves collecting results in tabular form based on prior research to analyze the characteristics of existing datasets, techniques for dataset augmentation and enhancement, and evaluating the effectiveness of different datasets in detecting and mitigating DDoS attacks through comprehensive experimentation. Results of our findings indicate that effective detection and mitigation of DDoS attacks in SDN require robust datasets that capture the diverse and evolving nature of attack scenarios. Our findings provide valuable insights into the importance of datasets in enhancing the resilience of SDN infrastructures against DDoS attacks. In conclusion, our findings provide valuable insights into the importance of datasets in enhancing the resilience of SDN infrastructures against DDoS attacks and highlight the need for further research in this critical area. Thorough guidelines for dataset selection and impacts of different datasets used in recent studies, provide research challenges and future directions in this area.

Case Study in Network Security System Using Random Port Knocking Method on The Principles of Availability, Confidentiality and Integrity

Preventing unidentified individuals from misusing their access to information is a major concern when it comes to data security. Network administrators are charged with working harder to be able to secure the computer network they manage. The utilization of right method is a challenge for network administrators to protect computer network from intruders. The RPK method is one of solution to overcome this problem. This research aims to implement RPK method on the principles of availability, confidentiality, and integrity which have not been explored by previous studies. The network system configuration stage involved installing Debian 9, NMAP, Hydra, RPK, cloud server, remote admin, and attacker. The network security system's performance was tested, revealing a 99.97% availability rate and 100% confidentiality. The system's integrity was assessed, with an average response time of 0.22 seconds and 100% blocking accuracy. The test results indicate that the system's network security performance, using the RPK method, capable of protecting server attacks and effectively upholding security stability.

Enhancing DDoS Attack Detection and Network Resilience Through Ensemble-Based Packet Processing and Bandwidth Optimization

It is critical to identify Distributed Denial of Service (DDoS) attacks to preserve network integrity and guarantee continuous service delivery. Our research suggests a novel way to lower the network's packet drop ratio and improve the accuracy of DDoS attack detection. Conventional techniques occasionally just use anomaly detection or signature-based detection, which might not be sufficient to protect against DDoS assault schemes that are always changing. To increase the precision and resilience of DDoS detection, our system incorporates several detection strategies, such as signature-based, anomaly-based, and machine learning-based techniques. Additionally, we use network traffic analysis and anomaly detection tools to quickly discover and block harmful traffic patterns. During suspected DDoS attempts, we dynamically modify network parameters and reroute data to reduce the packet drop ratio and maintain service for authorized users. Additionally, our system has feedback systems that allow us to continuously adjust and improve detection algorithms, improving the overall dependability and effectiveness of DDoS attack detection. We illustrate how successfully our method lowers packet drop ratios and strengthens network resilience against DDoS attacks using both simulation and real-world experience.

Deep Learning Algorithms for Detecting and Mitigating DDoS Attacks

Raising the threat of Distributed Denial of Service (DDoS) attacks means that high and adapted detection tools are required now more than ever. This research focuses on exploring the latest solutions in preventing DDoS attacks and emphasizes how Artificial Intelligence (AI) is involved in enhancing end-to-end detection techniques. Through the analysis of several key approaches, this work notes that AI-guided models quickly identify and counteract any unusual traffic patterns that may indicate an oncoming DDoS attack. Essential aspects towards creating more resilient networks against such attacks include machine learning algorithms, sophisticated data analytics together with AI based detection systems for traffic pattern recognition. Importantly, AI does well in behavioral analysis because it can distinguish and adapt to changing attack vectors. Additionally, it puts AI into perspective as making positive mitigation strategies possible that contain quick interferences such as temporary halt of traffic, rerouting and targeted block listing with real time control panel operations. On the contrary, current DDoS detection prevention techniques remain critically addressed of persistent challenges and limitations fundamental to them. From what emerges, they should always be ready for innovation and improvement because of how attacks might evolve over time. This paper aligns itself with the position that AI-driven detection mechanisms are natural to network security against DDoS attacks. It underlines the importance of integrating AI-based solutions with conventional practices in order to enhance network resilience and efficiently counteract cyber threats that are evolving all the time.

Network security framework for Internet of medical things applications: A survey

Abstract Limited device resources and an ever-changing cybersecurity landscape compound the challenges faced by the network protection infrastructure for Internet of medical things (IoMT) applications, which include different device ecosystems, privacy concerns, and problems with interoperability. Protecting private medical information in IoMT apps is challenging; a comprehensive strategy that provides user education, standard protocols, and robust security mechanisms is necessary to overcome these obstacles. With the advancement of IoMT, the network of clinical systems, gadgets, and sensors is integrated with the Internet of things (IoT) to enable intelligent healthcare solutions. However, the sensitive data sharing and the substantial connections in the IoMT systems raise security and privacy concerns in the network. Therefore, network security is critical in IoMT applications due to data breaches, vulnerabilities, and distributed denial of service attacks on medical data. This study reviews the network security techniques implemented in the existing studies for IoMT applications using machine learning and blockchain technology. This study presents an overview of IoMT healthcare applications by highlighting the security challenges encountered and the necessity of adopting advanced techniques to deal with complex threats. The research is mainly about how deep reinforcement learning (DRL), commonly used for intrusion detection, access control, and anomaly detection, works over time and how it can be used in IoMT applications. With the notion of providing robust security in IoMT applications, this study appraises the benefits of blockchain technology, such as data integrity, accountability, and confidentiality. Besides, this study addresses the limitations and challenges of various security techniques that IoMT systems employ. This work assesses the findings, research gaps, and future advancements for enhancing network security in IoMT applications. With an extensive analysis of existing research, this survey guides researchers, medical practitioners, and decision-makers to scale up the DRL and blockchain in IoMT systems more efficiently in the future.

Feature-Selection-Based DDoS Attack Detection Using AI Algorithms

SDN has the ability to transform network design by providing increased versatility and effective regulation. Its programmable centralized controller gives network administration employees more authority, allowing for more seamless supervision. However, centralization makes it vulnerable to a variety of attack vectors, with distributed denial of service (DDoS) attacks posing a serious concern. Feature selection-based Machine Learning (ML) techniques are more effective than traditional signature-based Intrusion Detection Systems (IDS) at identifying new threats in the context of defending against distributed denial of service (DDoS) attacks. In this study, NGBoost is compared with four additional machine learning (ML) algorithms: convolutional neural network (CNN), Stochastic Gradient Descent (SGD), Decision Tree, and Random Forest, in order to assess the effectiveness of DDoS detection on the CICDDoS2019 dataset. It focuses on important measures such as F1 score, recall, accuracy, and precision. We have examined NeTBIOS, a layer-7 attack, and SYN, a layer-4 attack, in our paper. Our investigation shows that Natural Gradient Boosting and Convolutional Neural Networks, in particular, show promise with tabular data categorization. In conclusion, we go through specific study results on protecting against attacks using DDoS. These experimental findings offer a framework for making decisions.