Interactive three-dimensional visualization of network intrusion detection data for machine learning

Zong, Wei; Chow, Yang-Wai; Susilo, Willy

doi:10.1016/j.future.2019.07.045

Cited by 56 publications

(22 citation statements)

References 20 publications

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“…The work presented in [22] provides an interactive method of visualizing network intrusion detection data in three-dimensions. The objective was to facilitate the understanding of network-intrusion-detection data using a visual representation to reflect the geometric relationship between various categories of network traffic.…”

Section: Related Workmentioning

confidence: 99%

Analysis, Design, and Comparison of Machine-Learning Techniques for Networking Intrusion Detection

Dini

Saponara

2021

Designs

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The use of machine-learning techniques is becoming more and more frequent in solving all those problems where it is difficult to rationally interpret the process of interest. Intrusion detection in networked systems is a problem in which, although it is not fundamental to interpret the measures that one is able to obtain from a process, it is important to obtain an answer from a classification algorithm if the network traffic is characterized by anomalies (and hence, there is a high probability of an intrusion) or not. Due to the increased adoption of SW-defined autonomous systems that are distributed and interconnected, the probability of a cyber attack is increased, as well as its consequence in terms of system reliability, availability, and even safety. In this work, we present the application of different machine-learning models to the problem of anomaly classification in the context of local area network (LAN) traffic analysis. In particular, we present the application of a K-nearest neighbors (KNN) and of an artificial neural network (ANN) to realize an algorithm for intrusion detection systems (IDS). The dataset used in this work is representative of the communication traffic in common LAN networks in military application in particular typical US Air Force LAN. This work presents a training phase of the different models based on a multidimensional-scaling preprocessing procedure, based on different metrics, to provide higher performance and generalization with respect to model prediction capability. The obtained results of KNN and ANN classifiers are compared with respect to a commonly used index of performance for classifiers evaluation.

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Section: Related Workmentioning

confidence: 99%

Analysis, Design, and Comparison of Machine-Learning Techniques for Networking Intrusion Detection

Dini

Saponara

2021

Designs

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“…The proposed method uses a CNN-based deep learning algorithm [29,30], which is one of the machine learning [31,32] methods to accurately detect only the facial region representing personal information from the human skin region extracted in the immediately preceding step. Usually CNN is one of the most popular deep learning structures because of its excellent performance in pattern recognition and image processing [33].…”

Section: Detection Of Human Facial Regionmentioning

confidence: 99%

Efficient covering of target areas using a location prediction-based algorithm

Jang

2020

J Supercomput

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Due to the rapid development of the high-speed wired and wireless Internet, image contents including objects with exposed personal information are being distributed freely, which is a social problem. In this paper, we introduce a method of robustly detecting a target object with facial region exposed from an image that is quickly entered using skin color and a deep learning algorithm and effectively covering the detected target object through prediction. The proposed method in this paper accurately detects the target object containing facial region exposed from the image entered by applying an image adaptive skin color model and a CNN-based deep learning algorithm. Subsequently, the location prediction algorithm is used to quickly track the detected object. A mosaic is overlapped over the target object area to effectively protect the object area where the facial region is exposed. The experimental results show that the proposed approach accurately detects the target object including the facial region exposed from the continuously entered video, and efficiently covers the detected object through mosaic processing while quickly tracking it using a prediction-based tracking algorithm. The tracking-based target covering method proposed in this study is expected to be useful in various practical applications related to pattern recognition and image security, such as content-based image retrieval, real-time surveillance, human-computer interaction, and face detection.

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“…In this study, a convolutional neural network (CNN)-based deep learning technique [35][36][37][38], one of the significant machine learning algorithms [39][40][41][42] in the artificial intelligence field, is utilized to robustly detect only a human facial region from the extracted skin color distribution region. Generally, the CNN is one of the most popular structures in the deep learning research field due to its excellent image processing and pattern recognition performance.…”

Section: Detection Of Target Regionmentioning

confidence: 99%

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Jang

Lee

2020

Sustainability

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High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects' regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking.

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Interactive three-dimensional visualization of network intrusion detection data for machine learning

Cited by 56 publications

References 20 publications

Analysis, Design, and Comparison of Machine-Learning Techniques for Networking Intrusion Detection

Analysis, Design, and Comparison of Machine-Learning Techniques for Networking Intrusion Detection

Efficient covering of target areas using a location prediction-based algorithm

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

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