MoveSteg: A Method of Network Steganography Detection

Szczypiorski, Krzysztof; Tyl, Tomasz

doi:10.1515/eletel-2016-0046

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…The studies [3,4,5,6,7,8] presented several studies and proposals for detecting network steganography based on the abnormal behavior analysis technique and the ruleset database. However, noticed that these approaches have two problems [1,2,9,10,11,12]: using the available dataset and focusing on detecting only one steganography technique. Therefore, although these studies brought very high efficiency on experimental datasets, when applied in reality, they did not bring the desired result.…”

Section: A the Problemmentioning

confidence: 99%

A New Approach for Network Steganography Detection based on Deep Learning Techniques

Xuan¹,

Duong²

2021

IJACSA

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One of the techniques that current cyber-attack methods often use to steal and transmit data out is to hide secret data in packets. This is the network steganography technique. Because millions of packets are sent and received every hour in internet activity, so it is very difficult to detect the theft and transmission of system data out using this form. Recent approaches often seek ways to compute and extract abnormal behaviors of packets to detect a steganography protocol or technique. However, such methods have the difficult problem of not being able to detect abnormal packets when an attacker uses other steganography techniques. To solve the above problem, this paper proposes a network steganography detection method using deep learning techniques. The highlight of this study is some new proposed features based on different components of the packet. By combining these many components, this proposal will not only provide the ability to detect many steganography techniques in the network, but also improve the ability to accurately detect abnormal packets. Besides, this study proposes to use deep learning for the task of detecting normal and abnormal packets. The authors want to take advantage of the big data analysis and processing capabilities of deep learning models in order to improve the ability to analyze and detect network steganography techniques. The experimental results in Section IVD have proved the effectiveness of this proposed method compared with other approaches.

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Section: A the Problemmentioning

confidence: 99%

A New Approach for Network Steganography Detection based on Deep Learning Techniques

Xuan¹,

Duong²

2021

IJACSA

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“…end for 20: end procedure 21 We assign a cluster number to all air routes according to the minimum distance from centers of clusters. Lastly, we order all air routes by increasing complexity of prediction.…”

Section: Algorithm 1 Point Initializing and Reordering Algorithmmentioning

confidence: 99%

“…However, this difference must be kept to a minimum to make the existence of steganography practically undetectable. For this reason, we evaluated this positional variation between the actual coordinates and modified coordinates, as in a study by Szczypiorski and Tyl [21]. We simulated 1021 air routes 30 times; hence, 30,630 positions were evaluated.…”

Section: Evaluation Of Effectivenessmentioning

confidence: 99%

StegoGIS: A new steganography method using the geospatial domain

Kurtuldu¹,

Demirci²

2019

Turk J Elec Eng & Comp Sci

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Geographic data are used on a variety of computing devices for many different applications including navigation, tracking, location planning, and marketing. The prevalence of geographic data makes it possible to envision new useful applications. In this paper, we propose using geographic data as a medium for secret communication, or steganography. We develop a method called StegoGIS for hiding messages in geographic coordinates in the well-known binary of fast-moving objects and transmitting them secretly. We show that discovering this secret communication is practically impossible for third parties. We also show that a large amount of secret data can be transmitted this way, and that the receiver can easily extract the hidden message. We argue that StegoGIS offers a novel and practical secret communication method with minimal overhead.common media files such as images [3,4], torrent files [5], text [6], geographical objects [7], or network packets [8].Geographical objects are composed of raster and vector data. Whereas raster data are based on a tessellation of image cells, vector data are based on vertices and paths. They can be categorized into three geometrical elements [9], which are points, lines, and polygons. Vector data are formed by spatial data, or in other words a sequence of coordinates describing the geographical locations of map objects, attribution data, and additional data. Vector data represent real-world features within a geographic information system (GIS) environment. GIS is used for processing geographical data as well as storing both the geometry and the attributes of these data.The rapid development of the Internet makes it easy to exchange information via networks. In particular, emerging web-based applications make information available to both public and private clients. In addition, web services are developed to support interoperable machine-to-machine interaction over a network. Based on web

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“…Cybercrime first received serious world attention in 2011 when a cyberattack occurred on the Iranian nuclear reactor facility, Stuxnet (Harianja, Arianto, & Setiawan, 2022). Szczypiorski (2020) stated that cyberspace and security systems are inseparable units. It is not only a place for exchanging information but also a new place to socialize.…”

Section: Introductionmentioning

confidence: 99%

United States National Strategy for Defending Vital Infrastructure from Cyberattacks

Tristantri,

Prasodjo

2023

Pertahanan

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The expanding information and communication technology landscape has led to complex security challenges, marked by dynamic and evolving cyber threats including hacking, malware, phishing, data theft, fraud, and Distributed Denial of Service (DDoS). Notably, the United States has faced the highest incidence of cybercrime cases from 2017 to 2021, affecting 2.9 billion victims and resulting in a cumulative material loss of US$18.7 billion during this period. This research centers on the United States, acknowledged as the primary target for global cybercriminals, comprising 38% of cybercrime targets. This research aims to find out the strategic efforts of the United States in overcoming cybercrime that befell its country so that it becomes the best country in cases of alleviating and controlling cases of cyberattacks even though it is also the country with the highest cases of cyberattacks in the world. Utilizing a qualitative research method, the study demonstrates that the United States collaborates systematically and synergistically with state governments, employing a centralized approach to delegate specific tasks and authorities. The study concludes that the United States has implemented the National Cybersecurity Strategy (NCS) 2023 program, aiming to establish a strategic environment and ecosystem by maintaining vital infrastructure and centralized integration.

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MoveSteg: A Method of Network Steganography Detection

Cited by 6 publications

References 6 publications

A New Approach for Network Steganography Detection based on Deep Learning Techniques

A New Approach for Network Steganography Detection based on Deep Learning Techniques

StegoGIS: A new steganography method using the geospatial domain

United States National Strategy for Defending Vital Infrastructure from Cyberattacks

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