HLD-DDoSDN: High and low-rates dataset-based DDoS attacks against SDN.

Software Defined Network (SDN) has alleviated traditional network limitations but faces a significant challenge due to the risk of Distributed Denial of Service (DDoS) attacks against an SDN controller, with current detection methods lacking evaluation on unrealistic SDN datasets and standard DDoS attacks (i.e., high-rate DDoS attack).

Meta-Learner-Based Approach for Detecting Attacks on Internet of Things Networks.

The significant surge in Internet of Things (IoT) devices presents substantial challenges to network security. Hackers are afforded a larger attack surface to exploit as more devices become interconnected. Furthermore, the sheer volume of data these devices generate can overwhelm conventional security systems, compromising their detection capabilities.

Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models.

The Internet of Things (IoT) has transformed our interaction with technology and introduced security challenges. The growing number of IoT attacks poses a significant threat to organizations and individuals. This paper proposes an approach for detecting attacks on IoT networks using ensemble feature selection and deep learning models.

CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks.

The Internet of Things (IoT) has brought significant advancements that have connected our world more closely than ever before. However, the growing number of connected devices has also increased the vulnerability of IoT networks to several types of attacks. In this paper, we present an approach for detecting attacks on IoT networks using a combination of two convolutional neural networks (CNN-CNN).

Conditional Tabular Generative Adversarial Based Intrusion Detection System for Detecting Ddos and Dos Attacks on the Internet of Things Networks.

The increasing use of Internet of Things (IoT) devices has led to a rise in Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks on these networks. These attacks can have severe consequences, resulting in the unavailability of critical services and financial losses. In this paper, we propose an Intrusion Detection System (IDS) based on a Conditional Tabular Generative Adversarial Network (CTGAN) for detecting DDoS and DoS attacks on IoT networks.

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking.

Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge.

Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming Combiner.

Concept drift (CD) in data streaming scenarios such as networking intrusion detection systems (IDS) refers to the change in the statistical distribution of the data over time. There are five principal variants related to CD: incremental, gradual, recurrent, sudden, and blip. Genetic programming combiner (GPC) classification is an effective core candidate for data stream classification for IDS.

Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images.

Several reviews have been conducted regarding artificial intelligence (AI) techniques to improve pregnancy outcomes. But they are not focusing on ultrasound images. This survey aims to explore how AI can assist with fetal growth monitoring via ultrasound image.

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things.

The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN.

Integrated Approach to Achieve a Sustainable Organic Waste Management System in Saudi Arabia.

Organic waste management (OWM) has always been a fundamental aspect of human populations. Approaches to OWM must be matched to the characteristics of a certain population. In this consideration, the Kingdom of Saudi Arabia (KSA) is no exception.

A Secure Pseudonym-Based Conditional Privacy-Preservation Authentication Scheme in Vehicular Ad Hoc Networks.

Existing identity-based schemes utilized in Vehicular Ad hoc Networks (VANETs) rely on roadside units to offer conditional privacy-preservation authentication and are vulnerable to insider attacks. Achieving rapid message signing and verification for authentication is challenging due to complex operations, such as bilinear pairs. This paper proposes a secure pseudonym-based conditional privacy-persevering authentication scheme for communication security in VANETs.

Efficient models for enhancing the link adaptation performance of LTE/LTE-A networks.

Link adaptation (LA) is the ability to adapt the modulation scheme (MS) and the coding rate of the error correction in accordance with the quality of the radio link. The MS plays an important role in enhancing the performance of LTE/LTE-A, which is typically dependent on the received signal to noise ratio (SNR). However, using the SNR to select the proper MSs is not enough given that adaptive MSs are sensitive to error.

SE-CPPA: A Secure and Efficient Conditional Privacy-Preserving Authentication Scheme in Vehicular Ad-Hoc Networks.

Communications between nodes in Vehicular Ad-Hoc Networks (VANETs) are inherently vulnerable to security attacks, which may mean disruption to the system. Therefore, the security and privacy issues in VANETs are entitled to be the most important. To address these issues, the existing Conditional Privacy-Preserving Authentication (CPPA) schemes based on either public key infrastructure, group signature, or identity have been proposed.

Hybrid rule-based botnet detection approach using machine learning for analysing DNS traffic.

Botnets can simultaneously control millions of Internet-connected devices to launch damaging cyber-attacks that pose significant threats to the Internet. In a botnet, bot-masters communicate with the command and control server using various communication protocols. One of the widely used communication protocols is the 'Domain Name System' (DNS) service, an essential Internet service.

Mechanism to prevent the abuse of IPv6 fragmentation in OpenFlow networks.

OpenFlow makes a network highly flexible and fast-evolving by separating control and data planes. The control plane thus becomes responsive to changes in topology and load balancing requirements. OpenFlow also offers a new approach to handle security threats accurately and responsively.

DAD-match; Security technique to prevent denial of service attack on duplicate address detection process in IPv6 link-local network.

An efficiently unlimited address space is provided by Internet Protocol version 6 (IPv6). It aims to accommodate thousands of hundreds of unique devices on a similar link. This can be achieved through the Duplicate Address Detection (DAD) process.