2 research outputs found
A New Data-Balancing Approach Based on Generative Adversarial Network for Network Intrusion Detection System
An intrusion detection system (IDS) plays a critical role in maintaining network security by
continuously monitoring network traffic and host systems to detect any potential security breaches
or suspicious activities. With the recent surge in cyberattacks, there is a growing need for automated
and intelligent IDSs. Many of these systems are designed to learn the normal patterns of
network traffic, enabling them to identify any deviations from the norm, which can be indicative of
anomalous or malicious behavior. Machine learning methods have proven to be effective in detecting
malicious payloads in network traffic. However, the increasing volume of data generated by IDSs
poses significant security risks and emphasizes the need for stronger network security measures. The
performance of traditional machine learning methods heavily relies on the dataset and its balanced
distribution. Unfortunately, many IDS datasets suffer from imbalanced class distributions, which
hampers the effectiveness of machine learning techniques and leads to missed detection and false
alarms in conventional IDSs. To address this challenge, this paper proposes a novel model-based
generative adversarial network (GAN) called TDCGAN, which aims to improve the detection rate
of the minority class in imbalanced datasets while maintaining efficiency. The TDCGAN model
comprises a generator and three discriminators, with an election layer incorporated at the end of the
architecture. This allows for the selection of the optimal outcome from the discriminators’ outputs.
The UGR’16 dataset is employed for evaluation and benchmarking purposes. Various machine
learning algorithms are used for comparison to demonstrate the efficacy of the proposed TDCGAN
model. Experimental results reveal that TDCGAN offers an effective solution for addressing imbalanced
intrusion detection and outperforms other traditionally used oversampling techniques. By
leveraging the power of GANs and incorporating an election layer, TDCGAN demonstrates superior
performance in detecting security threats in imbalanced IDS datasets.PID2020-113462RB-I00, PID2020-115570GB-C22
and PID2020-115570GB-C21 granted by Ministerio Español de EconomÃa y CompetitividadProject TED2021-129938B-I0, granted by Ministerio Español de Ciencia e Innovació
RAD: Reinforcement Authentication DYMO Protocol for MANET
Mobile ad hoc network (MANET) does not have
fixed infrastructure or centralized server which manages
the connections between the nodes. Rather, the nodes in
MANET move randomly. Thus, it is risky to exchange data
between nodes because there is a high possibility of having
malicious node in the path. In this paper, we will describe a
new authentication technique using message digest 5
(MD5), hashing for dynamic MANET on demand protocol
(DYMO) based on reinforcement learning. In addition, we
will describe an encryption technique that can be used
without the need for a third party to distribute a secret key.
After implementing the suggested model, results showed a
remarkable enhancement in securing the path by
increasing the packet delivery ratio and average
throughput. On the other hand, there was an increase in
end to end delay due to time spent in cryptographic
operations