A survey on deep learning for cybersecurity: Progress, challenges, and opportunities
As the number of Internet-connected systems rises, cyber analysts find it increasingly difficult
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
to effectively monitor the produced volume of data, its velocity and diversity. Signature-based …
A survey on intrusion detection systems for fog and cloud computing
The rapid advancement of internet technologies has dramatically increased the number of
connected devices. This has created a huge attack surface that requires the deployment of …
connected devices. This has created a huge attack surface that requires the deployment of …
Deep learning-based intrusion detection for distributed denial of service attack in agriculture 4.0
Smart Agriculture or Agricultural Internet of things, consists of integrating advanced
technologies (eg, NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm …
technologies (eg, NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm …
A new DDoS attacks intrusion detection model based on deep learning for cybersecurity
The data is exposed to many attacks during communication in the network environment. It is
becoming increasingly essential to identify intrusions into network communications …
becoming increasingly essential to identify intrusions into network communications …
Transfer learning approach to IDS on cloud IoT devices using optimized CNN
Data centralization can potentially increase Internet of Things (IoT) usage. The trend is to
move IoT devices to a centralized server with higher memory capacity and a more robust …
move IoT devices to a centralized server with higher memory capacity and a more robust …
Deep Q-network-based heuristic intrusion detection against edge-based SIoT zero-day attacks
S Shen, C Cai, Z Li, Y Shen, G Wu, S Yu - Applied Soft Computing, 2024 - Elsevier
How to process and classify zero-day attacks due to their huge damage to social Internet of
Things (SIoT) systems has become a hot research issue. To solve this issue, we propose a …
Things (SIoT) systems has become a hot research issue. To solve this issue, we propose a …
Synthetic attack data generation model applying generative adversarial network for intrusion detection
Detecting a large number of attack classes accurately applying machine learning (ML) and
deep learning (DL) techniques depends on the number of representative samples available …
deep learning (DL) techniques depends on the number of representative samples available …
Efficient intelligent intrusion detection system for heterogeneous internet of things (HetIoT)
Moving towards a more digital and intelligent world equipped with internet-of-thing (IoT)
devices creates many security issues. A distributed denial of service (DDoS) attack is one of …
devices creates many security issues. A distributed denial of service (DDoS) attack is one of …
BoostedEnML: Efficient technique for detecting cyberattacks in IoT systems using boosted ensemble machine learning
Following the recent advances in wireless communication leading to increased Internet of
Things (IoT) systems, many security threats are currently ravaging IoT systems, causing …
Things (IoT) systems, many security threats are currently ravaging IoT systems, causing …
Detection and prevention of man-in-the-middle attack in iot network using regression modeling
N Sivasankari, S Kamalakkannan - Advances in Engineering software, 2022 - Elsevier
Security is the primary concern in any IoT application or network. Due to the rapid increase
in the usage of IoT devices, data privacy becomes one of the most challenging issue to the …
in the usage of IoT devices, data privacy becomes one of the most challenging issue to the …