Exploring Artificial Intelligence Solutions to Enhance Cybersecurity in Dynamic Network Environments and IoT

The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.

Internet of Things (IoT) security is becoming important with the growing popularity of IoT devices and their wide applications. Recent network security reports revealed a sharp increase in the type, frequency, sophistication, and impact of distributed denial of service (DDoS) attacks on IoT systems, making DDoS one of the most challenging threats. DDoS is used to commit actual, effective, and profitable cybercrimes. The current machine learning-based IoT DDoS attack detection systems use batch learning techniques, and hence are unable to maintain their performance over time in a dynamic environment. The dynamicity of heterogeneous IoT data causes concept drift issues that result in performance degradation and automation difficulties in detecting DDoS. In this study, we propose an adaptive online DDoS attack detection framework that detects and adapts to concept drifts in streaming data using a number of features often used in DDoS attack detection. This paper also proposes a novel accuracy update weighted probability averaging ensemble (AUWPAE) approach to detect concept drift and optimize zero-day DDoS detection. We evaluated the proposed framework using IoTID20 and CICIoT2023 dataset containing benign and DDoS traffic data. The results show that the proposed adaptive online DDoS attack detection framework is able to detect DDoS attacks with an accuracy of 99.54% and 99.33% for the respective datasets.

Greater complexity in current computer networks, introduced in response to cloud computing, Internet of Things (IoT), and 5G technologies, has made complex approaches towards managing, optimizing, and securing network systems prominent.The traditional network management techniques, rooted primarily in strict rules and human intervention, are unable to cope with the amount of data and dynamics of current networks.Therefore, the use of Artificial Intelligence (AI) in networking is becoming a game-changer.Artificial Intelligence (AI) through such technologies as machine learning (ML), deep learning (DL), reinforcement learning (RL), and natural language processing (NLP) can indeed assist in enhancing the design, management, and security of the network system.There are a number of ways by which AI can optimize the networks.Traffic patterns for smart traffic control as well as resource allocation can be forecast using machine learning algorithms.The anomaly detection capabilities of machine learning-based systems also provide real-time security attack detection, hence mitigating the impact of attack vectors such as Distributed Denial of Service (DDoS) or malware attacks.Lastly but not the least, AI is capable of offering self-healing networks that automatically detect faults and heal themselves as required without human intervention, a business of unimaginable value in enormous systems.Reinforcement learning is very beneficial for dynamic routing and load balancing through constant adjustment of network parameters to changing conditions.Other applications of AI in the networks include optimization of Quality of Service (QoS), where applications with high priority such as video streaming or gaming are assigned the bandwidth, they require to function efficiently.In addition, with edge computing and 5G networks, the work of AI is ensuring that network resources are optimally distributed to edge devices for maximum scalability and performance.However, there are some limitations in the use of AI for networking.It needs to be extensively tested with data privacy issues, interpretability of the model, and computational complexity of the AI model.The requirement for high real-time performance puts constraints in processing the network, which can be itself a limiting factor for the use of AI in some applications.Even with these challenges, the potential of AI to revolutionize networking cannot be overstated, and work on network systems with AI at its foundation will probably yield more intelligent, more autonomous, and more secure networking choices.

Along with the growing threat of cyberattacks, cybersecurity has become one of the most important areas of the Internet of Things (IoT). The purpose of IoT cybersecurity is to reduce cybersecurity risk for organizations and users through the protection of IoT assets and privacy. New cybersecurity technologies and tools provide potential for better IoT security management. However, there is a lack of effective IoT cyber risk management frameworks for managers. This paper reviews IoT cybersecurity technologies and cyber risk management frameworks. Then, this paper presents a four-layer IoT cyber risk management framework. This paper also applies a linear programming method for the allocation of financial resources to multiple IoT cybersecurity projects. An illustration is provided as a proof of concept.

A blockchain-enabled Model integrates blockchain technology with Intrusion Detection Systems to enhance the security of Internet of Things (IoT) networks. It ensures data integrity, decentralization, and tamper-proof logging of intrusion detection. The approach improves trust, transparency, and real-time threat detection in distributed IoT environments. The existing blockchain-based IDS approaches, Blockchain Enabled (BCE-IoT), uniquely integrate blockchain consensus with federated-style local training, lightweight cryptography, and Shapley Additive Explanations (SHAP)-based explainability, ensuring both security and interpretability in IoT environments. The proposed work combines Blockchain technology with explainable artificial intelligence solutions to create a new cybersecurity Model that strengthens intrusion detection within IoT networks. The proposed model enhances transparency in tracking cyberattacks by combining blockchain security storage capabilities with SHAP, an explainable AI. This research utilises machine learning and artificial intelligence to detect threats in real-time, countering Distributed Denial of Service (DDoS), Denial of Service (DoS), scanning, Cross-Site Scripting (XSS), injection, password, and backdoor attacks. BCE-IoT delivers more precise security by combining blockchain’s permanent data features and AI anomaly detectors, thereby reducing security alert mistakes. The performance effectiveness of Blockchain-Enabled IoT surpasses that of the Content Integrity Detection System. It combines Blockchain and Software-Defined Networking to enhance security in network environments, utilising blockchain-based mutual confirmation for software-defined networking to detect and block cyber threats. The evaluation establishes BCE-IoT as an effective IoT network security solution that delivers strong cybersecurity features, is adaptable to modern connected environments, and offers interpretable security solutions. The performance evaluations demonstrate that BCE-IoT provides a robust, flexible, and interpretable cybersecurity solution suitable for modern IoT environments.

DDoS (Distributed Denial of Service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a key building block in the development of an integrated defense system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers apply machine learning (ML) and deep learning (DL) to the detection of DDoS attacks and have achieved satisfactory accomplishments. The complexity and sophistication of DDoS attacks have kept increasing and evolving since the first DDoS attack was reported in 1996. Regarding the headways in this problem, a new type of DDoS attack, named adversarial DDoS attack, is investigated in this study. The generating adversarial DDoS traffic is carried out using a symmetric Generative Adversarial Networks (GAN) architecture called Cycle-GAN to demonstrate the severe impact of adversarial DDoS attacks. Experiment results reveal that the synthesized traffic can easily penetrate ML-based detection systems, including Random Forest, KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and Naïve Bayes. These alarming results intimate the urgent need for countermeasures against adversary DDoS attacks.

Throughout the past few years, the Internet of Things (IoT) has grown in popularity because of its ease of use and flexibility. Cyber criminals are interested in IoT because it offers a variety of benefits for users, but it still poses many types of threats. The most common form of attack against IoT is Distributed Denial of Service (DDoS). The growth of preventive processes against DDoS attacks has prompted IoT professionals and security experts to focus on this topic. Due to the increasing prevalence of DDoS attacks, some methods for distinguishing different types of DDoS attacks based on individual network features have become hard to implement. Additionally, monitoring traffic pattern changes and detecting DDoS attacks with accuracy are urgent and necessary. In this paper, using Modified Whale Optimization Algorithm (MWOA) feature extraction and Hybrid Long Short Term Memory (LSTM), shown that DDoS attack detection methods can be developed and tested on various datasets. The MWOA technique, which is used to optimize the weights of the LSTM neural network to reduce prediction errors in the hybrid LSTM algorithm, is used. Additionally, MWOA can optimally extract IP packet features and identify DDoS attacks with the support of MWOA-LSTM model. The proposed MWOA-LSTM framework outperforms standard support vector machines (SVM) and Genetic Algorithm (GA) as well as standard methods for detecting attacks based on precision, recall and accuracy measurements.

The paper aims to provide a summary with a reference to the Internet of Things (IoT) of the distributed denial of service (DDoS). The network research community has known about the denial of service (DoS), which is intended to stop legitimate users from accessing a particular network tool since the early 1980s. The aim of the paper is to define DDoS scope, classifications and opportunities for assailants who use IoT to conduct such operations and to use IoT in healthcare. The research approach is to analyze DDoS, IoT and the use of IoT literature in the health sector. The study found that DDOS attacks on IoT-specific devices have become a very easy phenomenon because network attacks are much simpler due to limited security protocols. IOT distribution companies have made limited efforts to enable security of these devices and protecting the data, thus making them susceptible to vulnerabilities. Due to weak security layers, the confidentiality, authenticity and integrity of private data collected by IoT devices are threatened by attackers. The current research identified the gap and importance for further research to provide a mechanism for detection and prevention of DDOS attack in the application layer of IoT healthcare devices in India. Considering the major challenges in IOTs in defending against external attacks and hacking, it is important that they have inherent frameworks warding off such attacks.

The rapid expansion of the Internet of Things (IoT) ecosystem has transformed industries but also exposed significant cybersecurity vulnerabilities. Traditional centralized methods for securing IoT networks struggle to balance privacy preservation with real-time threat detection. This study presents a Federated Learning-Driven Cybersecurity Framework designed for IoT environments, enabling decentralized data processing through local model training on edge devices to ensure data privacy. Secure aggregation using homomorphic encryption supports collaborative learning without exposing sensitive information. The framework employs GRU-based recurrent neural networks (RNNs) for anomaly detection, optimized for resource-constrained IoT networks. Experimental results demonstrate over 98% accuracy in detecting threats such as distributed denial-of-service (DDoS) attacks, with a 20% reduction in energy consumption and a 30% reduction in communication overhead, showcasing the framework’s efficiency over traditional centralized approaches. This work addresses critical gaps in IoT cybersecurity by integrating federated learning with advanced threat detection techniques. It offers a scalable, privacy-preserving solution for diverse IoT applications, with future directions including blockchain integration for model aggregation traceability and quantum-resistant cryptography to enhance security.

With the emergence of smart devices and the declining market value of sensing systems, the implementation of the internet of things (IoT) is gaining momentum. IoT has become an integral part of many smart systems such as smart buildings, smart cities, and smart heath, etc. Despite many advantages, from its inception, IoT system is vulnerable to many threats. Such systems are more likely to be attacked and breached, by denial of service (DoS) or distributed denial of service (DDoS) attacks. DDoS attacks are one of the Internet security's most disruptive threats. The towering volume of smart devices with a greater level of heterogeneity increases the severity of this attack. To date, there is no protocol that guarantees complete protection of IoT systems. To enable the resiliency, continuous monitoring, along with an adaptive decision-making system, is essential. These challenges can be addressed with the help of software-defined networking (SDN), which can effectively handle the IoT devices. Moreover, the centralized provisioning of SDN enables a secured and adaptive IoT environment that can effectively handle the DDoS attack. The software-defined internet of things (SDIoT) framework based on SDx paradigm consists of a controller pool, SD-IoT switches integrated with 190an IoT gateway, and IoT devices. In this chapter, a broad range of various DDoS attacks and detection methods for the IoT environment used in the SDN paradigm have been analyzed. Finally, different future scopes of this evolving field have been discussed.

Internet of Things (IoT) and Distributed Denial of Service (DDoS) is the most growing emergence catchword that has a deep relation to each other. The lack of securities in IoT devices creates a loophole to hijack those devices and use them for a cybercrime. Recently many security breaches on IoT Environment along with high volume attacks have been reported. So, securing the IoT Environment to prevent cyber-attacks like DDoS on crucial infrastructure is the first priority. Moreover, detecting DDoS attacks is also harder as sometimes DDoS attack traffic is alike normal traffic. Here, we tried to describe the long-ranging impact of DDoS attacks on IoT Environment. Some recommended security solutions and guidelines have been proposed to detect and protect IoT devices against DDoS attacks. Thus, the deployment of this security solution will keep the IoT Environment cyber threat free.

The Internet of Things (IoT) is emerging as a new trend mainly employed in developing numerous vital applications. These applications endure on a federal storage framework primarily concerned with multiple issues. Blockchain technology (BC) is one of the supportive methods for developing IoT-based applications. It is employed to solve the problems encountered in IoT applications. The attack Distributed Denial of Service (DDoS) is one of the leading security attacks in IoT systems. Attackers can effortlessly develop the exposures of IoT gadgets and restrain them as fragments of botnets to commence DDoS threats. The IoT devices are said to be resource-constrained with computing resources and restricted memory. As a developing technology, BC holds the possibility of resolving security problems in IoT. This paper proposes the Metaheuristic-Optimized Blockchain Framework for Attack Detection using a Deep Learning Model (MOBCF-ADDLM) method. The main intention of the MOBCF-ADDLM method is to deliver an effective method for detecting DDoS threats in an IoT environment using advanced techniques. The BC technology is initially applied to mitigate DDoS attacks by presenting decentralized security solutions. Furthermore, data preprocessing utilizes the min-max scaling method to convert input data into a beneficial format. Additionally, feature selection (FS) is performed using the Aquila optimizer (AO) technique to recognize the most relevant features from input data. The attack classification process employs the deep belief network (DBN) technique. Finally, the red panda optimizer (RPO) model modifies the hyper-parameter values of the DBN model optimally and results in higher classification performance. A wide range of experiments with the MOBCF-ADDLM approach is performed under the BoT-IoT Binary and Multiclass datasets. The performance validation of the MOBCF-ADDLM approach portrayed a superior accuracy value of 99.22% over existing models.

The Internet of Things (IoT) has gained remarkable acceptance from millions of individuals. This is evident in the extensive use of intelligent devices such as smartphones, smart television, speakers, air conditioning, lighting, and high-speed networks. The general application area of IoT includes industries, hospitals, schools, homes, sports, oil and gas, automobile, and entertainment, to mention a few. However, because of the unbounded connection of IoT devices and the lack of a specific method for overseeing communication, security concerns such as distributed denial of service (DDoS), denial of service (DoS), replay, botnet, social engineering, man-in-the-middle, and brute force attacks have posed enormous challenges in the IoT environment. Regarding these enormous challenges, this study focuses on DDoS and DoS attacks. These two attacks have the most severe consequences in the IoT environment. The solution proposed in this study can also help future researchers tackle the expansion of IoT security threats. Moreover, the study conducts rigorous experiments to assess the efficiency of the proposed approach. In summary, the experimental results show that the proposed hybrid approach mitigates data exfiltration caused by DDoS and DoS attacks by 95.4%, with average network lifetime, energy consumption, and throughput improvements of 15%, 25%, and 60%, respectively.

In this era of digitization, more and more technologies are spurring up but are failing to make a successful impact due to the security and privacy issues. Internet of Things (IoT) has made its mark recently and being end-consumer oriented, makes it more prone towards being exploited. There are many types of security attacks in the IoT which can compromise the end user data and services. The most common and devastating security attack in the IoT is a Denial of Service (DoS)/Distributed Denial of Service (DDoS) attack. In this paper, the contributions are: we provide a structured & comprehensive overview of the existing research on DDoS Attacks, its security & countermeasures in the context of IoT. We survey latest vulnerabilities and their security solutions over the period 2014-2019. With elaborate discussion on IoT architecture and underlying security threats impacting each layer in the IoT, we have grouped existing approaches and derived taxonomy. With this concise overview, the paper seeks to provide an understanding of the pertaining models adopted by different researchers.

Fiber optic networks (FONs) are considered the backbone of telecom companies worldwide. However, the network elements of FONs are scattered over a wide area and managed through a centralized controller based on intelligent devices and the internet of things (IoT), with actuators used to perform specific tasks at remote locations. During the COVID-19 pandemic, many telecom companies advised their employees to manage the network using the public internet (e.g., working from home while connected to an IoT network). Theses IoT devices mostly have weak security algorithms that are easily taken-over by hackers, and therefore can generate Distributed Denial of Service (DDoS) attacks in FONs. A DDoS attack is one of the most severe cyberattack types, and can negatively affect the stability and quality of managing networks. Nowadays, software-defined networks (SDN) constitute a new approach that simplifies how the network can be managed through a centralized controller. Moreover, machine learning algorithms allow the detection of incoming malicious traffic with high accuracy. Therefore, combining SDN and ML approaches can lead to detecting and stopping DDoS attacks quickly and efficiently, especially compared to traditional methods. In this paper, we evaluated six ML models: Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest. The accuracy reached 100% while detecting DDoS attacks in FON with two approaches: (1) using SVM with three features (SOS, SSIP, and RPF) and (2) using Random Forest with five features (SOS, SSIP, RPF, SDFP, and SDFB). The training time for the first approach was 14.3 s, whereas the second approach only requires 0.18 s; hence, the second approach was utilized for deployment.