Distributed Denial Of Service Research Articles

Computer vision based distributed denial of service attack detection for resource-limited devices

The growing adoption of Internet of Things (IoT) has rendered them a desirable target for cyber-attacks. One of the biggest threats to these systems is the distributed denial of service (DDoS) attack, which is a botnet-based attack. The reason for the increasing usage of machine learning and deep learning-based intrusion detection systems in IoT network security is their ability to recognize DDoS attack. Recent studies, however, shows how susceptible IoT networks are to these kinds of attacks and detection accuracy can be greatly lowered. While a majority of studies has concentrated on DDoS attack detection for deep learning, little attention has been paid to computer vision, especially image-based artificial intelligence technologies like convolutional neural network (CNN). In this study, we use an image-based dataset to evaluate the effectiveness of CNN, an effective computer vision approach, for DDoS attack detection in IoT contexts. Owing to the small size of the selected dataset and in order to improve the CNN model’s detection efficiency, we implement various data augmentation techniques prior to the model’s training, including scaling, rotation, and vertical and horizontal flipping. Next, we introduce an efficient CNN-based method for detection of DDoS attacks in IoT settings. Ultimately, we came to the conclusion that the statistical significance testing showed that there is a significance difference among the five models employed during the study, and the VGG19 which has higher accuracy (99.74%) and less computing cost (6020.80 s), which enables IoT devices to perform DDoS attack detection with cost-effectiveness.

The Role of Internet of Battlefield Things in Modern Warfare: A Cybersecurity Perspective

The Internet of Battlefield Things (IoBT) is revolutionizing military operations by integrating interconnected devices and autonomous systems into combat environments, significantly enhancing decision-making, communication, and battlefield efficiency. However, this reliance on IoBT introduces substantial cybersecurity risks, exposing military networks to sophisticated cyber threats such as hacking, malware, and Distributed Denial of Service (DDoS) attacks. This paper explores the interplay between IoBT and the cyber domain, identifying the key challenges, vulnerabilities, and solutions necessary to secure these interconnected systems in the context of modern warfare. The study reviews the architectural components of IoBT, including sensors, drones, and communication networks, and examines their potential weaknesses in the face of cyberattacks. Additionally, it discusses common attack vectors such as Man-in-the-Middle (MitM) and zero-day exploits, which pose significant risks to military systems. To counter these threats, the paper evaluates various cybersecurity strategies, including the application of artificial intelligence (AI), machine learning (ML), blockchain technology, and robust encryption protocols designed to enhance the resilience and security of IoBT networks. Case studies of real-world IoBT deployments and cyber breaches further illustrate the practical implications of these challenges and defense mechanisms.

Reflective Distributed Denial of Service Detection: A Novel Model Utilizing Binary Particle Swarm Optimization-Simulated Annealing for Feature Selection and Gray Wolf Optimization-Optimized LightGBM Algorithm.

The fast growth of the Internet has made network security problems more noticeable, so intrusion detection systems (IDSs) have become a crucial tool for maintaining network security. IDSs guarantee the normal operation of the network by tracking network traffic and spotting possible assaults, thereby safeguarding data security. However, traditional intrusion detection methods encounter several issues such as low detection efficiency and prolonged detection time when dealing with massive and high-dimensional data. Therefore, feature selection (FS) is particularly important in IDSs. By selecting the most representative features, it can not only improve the detection accuracy but also significantly reduce the computational complexity and attack detection time. This work proposes a new FS approach, BPSO-SA, that is based on the Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) algorithms. It combines these with the Gray Wolf Optimization (GWO) algorithm to optimize the LightGBM model, thereby building a new type of reflective Distributed Denial of Service (DDoS) attack detection model. The BPSO-SA algorithm enhances the global search capability of Particle Swarm Optimization (PSO) using the SA mechanism and effectively screens out the optimal feature subset; the GWO algorithm optimizes the hyperparameters of LightGBM by simulating the group hunting behavior of gray wolves to enhance the detection performance of the model. While showing great resilience and generalizing power, the experimental results show that the proposed reflective DDoS attack detection model surpasses conventional methods in terms of detection accuracy, precision, recall, F1-score, and prediction time.

Machine learning-based detection of DDoS attacks on IoT devices in multi-energy systems

With the growing integration of IoT devices in critical infrastructure, cybersecurity threats such as Distributed Denial of Service (DDoS) attacks on Energy Hubs (EH) have become a significant concern. This study aims to address these challenges by evaluating the effectiveness of various supervised machine learning (ML) algorithms in predicting DDoS attacks targeting EH systems through IoT devices. Using the CICDDOS2019 and KDD-CUP datasets, a comprehensive analysis was conducted on several classifiers, including Decision Tree (DT), Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest. The results highlight Gradient Boosting as the most effective model, particularly for the CICDDOS2019 dataset, demonstrating superior accuracy and predictive capability. Additionally, hybrid models combining Gradient Boosting with SVM or DT showed strong performance, though with varying precision and recall. This study provides valuable insights into the selection and tailoring of ML models for specific security challenges, emphasizing the need for ongoing research to enhance the resilience of EH systems and IoT devices against evolving DDoS threats.

The Rise of Cybercrime-as-a-service: Implications and Countermeasures

The rise of Cybercrime-as-a-Service (CaaS) represents a new frontier in the evolution of cybercrime, where sophisticated tools and malicious services are made readily available to a broad range of users through online marketplaces, often on the dark web. CaaS has democratized cybercrime, enabling even low-skilled attackers to launch powerful cyberattacks such as Distributed Denial of Service (DDoS), phishing campaigns, and ransomware attacks with ease. This paper explores the operational structure of CaaS, including the use of cryptocurrencies for transactions and the global reach of these illicit platforms. The implications of this shift extend beyond economic losses, threatening national security, corporate stability, and personal privacy. While governments and law enforcement agencies are struggling to keep pace with the rapid evolution of these services, the paper examines legal and regulatory challenges in combating CaaS, as well as the role of international cooperation. Furthermore, it discusses technological countermeasures, including artificial intelligence and machine learning, as potential solutions to mitigate the threat. Ethical considerations surrounding the surveillance and control of online spaces are also addressed. The paper concludes by highlighting future trends in CaaS and stressing the need for a balanced approach between innovation and security to effectively counter this growing threat.

5G Security Features, Vulnerabilities, Threats, and Data Protection in IoT and Mobile Devices: A Systematic Review

The evolution of wireless communications, from the first to the fifth generation, has driven Internet of Things (IoT) advancements. IoT is transforming sectors like agriculture, healthcare, and transportation, but also presents challenges like spectrum bandwidth demand, speed requirements, and security issues. IoT environments, with embedded sensors and actuators, connect to other devices to transmit and receive data over the internet. These data are processed locally or in the cloud, enabling decision-making and automation. Various wireless technologies, including Bluetooth, Zigbee, LoRa, and WiFi, cater to specific IoT applications. 5G, with its high speeds, low latency, and efficient bandwidth sharing, is ideal for high-speed data transmission, and thus it provides robust infrastructure for low-latency and bandwidth-sensitive applications. This latest mobile network technology is being rapidly implemented worldwide, providing unprecedented throughput and low latency, which are crucial for the IoT. This paper presents a comprehensive systematic review on 5G security features, vulnerabilities, threats to mobile devices, security in IoT devices, common attacks to IoT devices and protection of data in this environment. Despite enhanced security, 5G faces challenges like Distributed Denial of Service (DDoS), Man In The Middle (MITM), cross-slice disruptions, and side-channel attacks.

Security Model for the Detection of Distributed Denial of Service Attack (DDoS) at the Access Layer in Cloud Reference Architecture

DDoS attacks on the cloud have become an emerging problem, which calls for an intelligent protection solution for information, communication and computing resources. Cloud reference architecture as a new technology with wider access facility, the access layer in the reference architecture becomes grave to numerous security challenges, and it is mostly exploited by the attacker using compromised computers to launch DDoS attack which affects its performance by shutting down some services. Many security frameworks have been proposed in the past, but they are not enough to provide security solution for accurate detection and control of DDoS attack at the access layer. Intelligent security model based on artificial neural network was developed to detect and control DDoS attack at the access layer. DDoS dataset obtained online was used to train and test the intelligent security model using MATHLAB workspace. Simulated results implemented and generated with receive operator characteristic analyzer (Roc) showed a true positive rate of 98.2% to detect and control DDoS attack at the access layer and a false negative rate of 0.02%. The inclusion of additional parameters to determined and identifies the source of the attack, whether it is lunch from outside or inherent within the reference architecture is recommended. KEYWORDS: Reference Architecture, Access Layer, Distributed Denial of Service (DDOS), Artificial Neural Network’s (ANN’s)

Research on Multi-Layer Defense against DDoS Attacks in Intelligent Distribution Networks

With the continuous development of new power systems, the intelligence of distribution networks has been increasingly enhanced. However, network security issues, especially distributed denial-of-service (DDoS) attacks, pose a significant threat to the safe operation of distribution networks. This paper proposes a novel DDoS attack defense mechanism based on software-defined network (SDN) architecture, combining Rényi entropy and multi-level convolutional neural networks, and performs fine-grained analysis and screening of traffic data according to the amount of calculation to improve the accuracy of attack detection and response speed. Experimental verification shows that the proposed method excels in various metrics such as accuracy, precision, recall, and F1-score. It demonstrates significant advantages in dealing with different intensities of DDoS attacks, effectively enhancing the network security of user-side devices in power distribution networks.

Blockchain Security:"Botnets and Bitcoin Mining” - A Study on the Impacts and Countermeasures

Introduction: Botnets have become a significant threat to cybersecurity, as they can be used for a wide range of malicious activities, including Distributed Denial-of-Service (DDoS) attacks, spamming, and cryptocurrency mining. Bitcoin Mining, in particular, has become a lucrative target for cybercriminals, as it requires massive computing power and can generate significant profits. Method: In this paper, the author presents a study on a botnet that uses an HTA file to gain initial access and execute code on a victim's device, followed by the installation of mining software to infect the device and bitcoins. Result: The author analyzes the botnet's behaviour, including its evasion techniques and Bitcoin Mining activities, and discusses the implications of current findings for cybersecurity and Bitcoin Mining. Conclusion: Future research should also investigate the use of different command and control servers and other advanced attack frameworks in botnet operations and examine the potential connections between botnets and other cybercrime activities, such as ransomware and espionage.

Botnet Attack Identification and Mitigation condition Software-Defined Networks Utilizing CNN Algorithm

One new design that makes managing and communicating across large-scale networks easier and more flexible is software-defined networking, or SDN. It allows for the smooth and dynamic execution of complicated network choices via programmable and centralized interfaces. But SDN opens doors for people and companies to tailor network apps to their needs, allowing them to enhance services. On the other hand, it began to encounter a host of new privacy and security issues and brought the dangers of one point of failure all at once. In most cases, hackers use OpenFlow switches to conduct botnets or distributed Denial of Service (DDoS) assaults against the controller. Popular security apps that use deep learning (DL) to quickly identify and counteract attacks are on the rise. Here, we examine botnet-based DDoS attack detection using DL approaches in an SDN-supported context and demonstrate their performance. For the assessment, we utilize a dataset that we just created ourselves. In order to choose the most useful subset of characteristics, we used weighting of features and tuning techniques. Using both a synthetic dataset and actual testbed conditions, we validate the measurements or simulation results. The primary objective of this research is to identify botnet-based DDoS assaults using easily-obtained characteristics and data using a lightweight DL approach with baseline hyper-parameters. We found that the DL technique's performance is affected by the optimal subset of features, and that the accuracy of predictions of the same approach may be varied with a different collection of features. Lastly, our empirical findings show that the CNN approach works better than both the dataset and the actual testbed environments. With CNN, the detection rate for typical flows is 99% and for malicious flows it drops to 97%.

A Framework for Detecting Distributed Denial of Services Attack in Cloud Enviorment using Machine Learning Techniques

Distributed Denial of Service (DDoS) persists in Online Applications as One of those significant threats. Attackers can execute DDoS by the more natural steps. Then with the high productivity to slow the consumer access services down. To detect an attack on DDoS and using machine learning algorithms. The Overseen to detect and mitigate the attack, machine learning algorithms such as Naive Bayes, decision tree, k-nearest neighbours (k-NN) and random forest are used. There are three steps: gathering information, preprocessing and feature Extraction in "Normal or DDoS" classification algorithm for detection Attack use Dataset NSL-KDD. Similar algorithms have different functions Conduct that is dependent on the features selected. DDOS-attack performance Detection is compared, and it indicates the best algorithm. Attempts at Distributed Denial of Service ( DDoS) Were the most powerful attacks of the last period. A Virtual Network. The intrusion detection system (NIDS) should be designed seamlessly to Fight the latest strategies and trends of those attackers NIDS on DDoS. In this paper, we propose an NIDS capable of detecting Current DDoS attacks, as well as new forms. The main characteristic of Our NIDS is the combination of various classifiers using an ensemble Models, with the concept of each classifier being able to target different Aspects/types of intrusions, and more effective in doing so Mechanism for protecting against new intrusions. Additionally, we perform a detailed study of and based on, DDoS attacks check the reduced set of functions [27, 28] to be essential to Enhance accuracy. We are playing with and analyzing NSL-KDD Dataset with a feature set reduced, and our proposed NIDS will Detect 99.1 per cent of active DDoS attacks. Let's compare our Tests with other methods which already exist. Our approach to NIDS has Able to learn to keep up with existing and evolving DDoS Attack trends.

Integrating machine learning for sustaining cybersecurity in digital banks

Cybersecurity continues to be an important concern for financial institutions given the technology's rapid development and increasing adoption of digital services. Effective safety measures must be adopted to safeguard sensitive financial data and protect clients from potential harm due to the rise in cyber threats that target digital organizations. The aim of this study is to investigates how machine learning algorithms are integrated into cyber security measures in the context of digital banking and its benefits and drawbacks. We initially provide a general overview of digital banks and the particular security concerns that differentiate them from conventional banks. Then, we explore the value of machine learning in strengthening cybersecurity defenses. We revealed that insider threats, distributed denial of service (DDoS) assaults, ransomware, phishing attacks, and social engineering are main cyberthreats that are digital banks exposed. We identify the appropriate machine learning algorithms such as support vector machines (SVM), recurrent neural networks (RNN), hidden markov models (HMM), and local outlier factor (LOF) that are used for detection and prevention cyberthreats. In addition, we provide a model that considers ethical concerns while constructing a cybersecurity framework to address potential vulnerabilities in digital banking systems. The advantages and disadvantages of incorporating machine learning into the cybersecurity strategy of digital banks are outlined using strengths, weaknesses, opportunities, threats (SWOT) analysis. This study seeks to provide a thorough knowledge of how machine learning may strengthen cybersecurity procedures, protect digital banks, and maintain customer trust in the ecosystem of digital banking.

Identifying and classifying cyberattacks on airports

This paper describes research to identify and classify cyberattacks in the aviation industry in order to present the true reality of airports as a critical infrastructure and the threats that airport operators face. We conducted a critical review related to types of cyberattacks and supported by updated studies to analyse cyberattacks in the aviation industry from 2000 to 2023 due to the increase of attacks occurring in this period. Data was collected from verifiable sources such as the Center for Strategic and International Studies (CSIS), Federal Aviation Administration, EUROCONTROL, European Union Aviation Safety Agency (EASA), European Union Agency for Cybersecurity (ENISA) and KonBriefing. The findings of this study revealed that recent years have seen an increase in the number of distributed denial-of-service (DDoS) and ransomware cyberattacks at airports by foreign countries motivated by political and economic reasons, diplomatic espionage or even as part of a cyber war. This is particularly worrying, because the most influential international organisations and countries are recognising the existence of a cyber war in political, espionage, terrorism, safety, financial and commercial terms. The new contribution of this research lies in the fact that many uncertainties surround the cyberattacks that airport operators and commercial airlines face on a daily basis. Cyberattacks in the aviation industry are more common than most people realise, and the issue is that sometimes this information is silenced by governments, airport and airline operators to avoid unnecessary social alarm.

CapsRule: Explainable Deep Learning for Classifying Network Attacks.

Despite the potential deep learning (DL) algorithms have shown, their lack of transparency hinders their widespread application. Extracting if-then rules from deep neural networks is a powerful explanation method to capture nonlinear local behaviors. However, existing rule extraction methods suffer from inefficiency, incomprehensibility, infidelity, and not scaling well. Concerning security applications, they are not optimized regarding the decision boundary, data types and ranges, classification tasks, and dataset size. In this article, we propose CapsRule, an effective and efficient rule-based DL explanation method dedicated to classifying network attacks. It extracts high-fidelity rules from the feed-forward capsule network that explains how an input sample is classified. Using precomputed coupling coefficients, the training phase overlaps the rule extraction process to increase efficiency. The activation vector of a capsule can represent semantic intelligence about the attributes of the input sample. The rules extracted from CapsRule address the major concerns of network attack detection. The rules: 1) approximate the nonlinear decision boundary of the underlying data; 2) reduce the number of false positives significantly; 3) increase transparency; and 4) help find errors and noise in the data. We evaluate CapsRule on the CICDDoS2019 dataset that contains over a million of the most advanced Distributed Denial-of-Service (DDoS) attacks. The extensive evaluation shows that it generates accurate, high-fidelity, and comprehensible rules. CapsRule achieves an average accuracy of 99.0% and a false positive rate of 0.70% for reflection- and exploitation-based attacks. We verify that the learned features from the rulesets match our domain-specific knowledge. They also help find flaws in the dataset generation process and erroneous patterns caused by attack simulators.

Empirical analysis of the cyberattacks of the Russian–Ukrainian war

This research study aims to empirically analyze the cyberattacks that occurred in the context of the Russian–Ukrainian conflict between 2022 and 2023, with a specific focus on the impact of these attacks on civilian infrastructure and institutions. The data collection for this study is based on publicly available sources from the Cyber- Peace Institute, taking into account various types of incidents such as malware, distributed denial of service (DDoS) attacks, spam, information operations, and website defacements. The study employs a network theory approach to examine the structure and dynamics of incidents and campaigns, while additional statistical methods and trend analysis are used to assess sector-specific and geographic patterns, as well as changes in attack frequency and severity. The research aims to contribute to the existing literature on cyber warfare and to provide valuable insights into the cyber threats faced by civilian infrastructure and institutions during times of conflict.

Vulnerabilities and Threats to AIS Security Systems

This research explores vulnerabilities and threats to Accounting Information Systems (AIS) and evaluates effective mitigation measures to address these issues. Using both qualitative and quantitative approaches, this research involved literature studies, surveys, expert interviews, and case analysis to identify and analyze the different types of vulnerabilities and threats faced by AIS. The results showed that human error, software flaws, and system integration complexity are the main sources of vulnerabilities in AIS. The most common threats are cyberattacks, insider threats, and Distributed Denial of Service (DDoS) attacks. The impact of these threats includes financial loss, reputational damage, and significant legal implications.Mitigation strategies identified include the development of robust security policies, the use of cutting-edge security technologies, and the conduct of regular audits and monitoring. The findings emphasize the importance of a layered approach to improving AIS security and reliability. This research provides an in-depth insight into how organizations can identify, evaluate and manage vulnerabilities and threats to their AIS, which is essential for maintaining the integrity, confidentiality and availability of accounting data in an increasingly complex digital age.

A novel deep learning-based intrusion detection system for IoT DDoS security

Intrusion detection systems (IDS) for IoT devices are critical for protecting against a wide range of possible attacks when dealing with Distributed Denial of Service (DDoS) attacks. These attacks have become a primary concern for IoT networks. Intelligent decision-making techniques are required for DDoS attacks, which pose serious threats. The range of devices connected to the IoT ecosystem is growing, and the data traffic they generate is continually changing; the need for models more resistant to new attack types and existing attacks is of research interest. Motivated by this gap, this paper provides an effective IDS powered by deep learning models for IoT networks based on the recently published CICIoT2023 dataset. In this work, we improved the detection and mitigation of potential security threats in IoT networks. To increase performance, we performed preprocessing operations on the dataset, such as random subset selection, feature elimination, duplication removal, and normalization. A two-level IDS using deep-learning models containing binary and multiclass classifiers has been designed to identify DDoS attacks in IoT networks. The effectiveness of several deep-learning models in real-time and detection performance has been evaluated. We trained fully connected, convolutional, and LSTM-based deep learning models for detecting DDoS attacks and sub-classes. According to the results on a partially balanced sub-dataset, two staged models performed better than baseline models such as DNN (Deep Neural Networks), CNN (Convolutional Neural Networks), LSTM (Long Short Term Memory), RNN (Recurrent Neural Network).

A DDoS Tracking Scheme Utilizing Adaptive Beam Search with Unmanned Aerial Vehicles in Smart Grid

As IoT technology advances, the smart grid (SG) has become crucial to industrial infrastructure. However, SG faces security challenges, particularly from distributed denial of service (DDoS) attacks, due to inadequate security mechanisms for IoT devices. Moreover, the extensive deployment of SG exposes communication links to attacks, potentially disrupting communications and power supply. Link flooding attacks (LFAs) targeting congested backbone links have increasingly become a focal point of DDoS attacks. To address LFAs, we propose integrating unmanned aerial vehicles (UAVs) into the Smart Grid (SG) to offer a three-dimensional defense perspective. This strategy includes enhancing the speed and accuracy of attack path tracking as well as alleviating communication congestion. Therefore, our new DDoS tracking scheme leverages UAV mobility and employs beam search with adaptive beam width to reconstruct attack paths and pinpoint attack sources. This scheme features a threshold iterative update mechanism that refines the threshold each round based on prior results, improving attack path reconstruction accuracy. An adaptive beam width method evaluates the number of abnormal nodes based on the current threshold, enabling precise tracking of multiple attack paths and enhancing scheme automation. Additionally, our path-checking and merging method optimizes path reconstruction by merging overlapping paths and excluding previously searched nodes, thus avoiding redundant searches and infinite loops. Simulation results on the Keysight Ixia platform demonstrate a 98.89% attack path coverage with a minimal error tracking rate of 2.05%. Furthermore, simulations on the NS-3 platform show that drone integration not only bolsters security but also significantly enhances network performance, with communication effectiveness improving by 88.05% and recovering to 82.70% of normal levels under attack conditions.

Optimized Feature Selection for DDoS Attack Recognition and Mitigation in SD-VANETs

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures.

Beyond signatures and thresholds: Intelligent DDoS detection using Convolutional Neural Networks

Distributed Denial of Service (DDoS) attacks have evolved into sophisticated threats against digital infrastructure. This research investigates Convolutional Neural Networks (CNNs) for DDoS detection using the CIC-IDS2017 dataset. A CNN model was developed, leveraging convolutional and pooling layers for hierarchical feature extraction. Preprocessing ensured dataset consistency and generalization. The model achieved 99.978% test accuracy, demonstrating proficiency in recognizing DDoS patterns. Comparatively, a Deep Neural Network (DNN) benchmark obtained 99.9512% accuracy, indicating CNNs superiority. However, CNN training time was longer. While results highlight deep learning's potential against DDoS attacks, optimizations for real-time deployment warrant exploration. This empirical evaluation and comparative analysis enriches the discourse on utilizing machine learning for robust cybersecurity.