Trends Toward Real-Time Network Data Steganography

Collins, James C.; Agaian, Sos S.

doi:10.5121/ijnsa.2016.8201

Cited by 9 publications

(9 citation statements)

References 22 publications

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“…The payoff for a data thief willing to ignore the implementation difficulty is the degree of detection resistance demonstrated by a timing channel. As previously mentioned, a timing channel does not make any physical alterations to packet header values [38]. Much like a sequence channel, the assailant need only alter the transmission pattern to achieve a covert communication channel that is extremely hard to detect unless there are advanced controls in place to analyze the statistical anomalies in the arrival times of network packets [39].…”

Section: Timing Channel Data Representationmentioning

confidence: 99%

A Zero-Day Cloud Timing Channel Attack

Flowers

2022

IEEE Access

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The Intrusion Detection and Prevention System (IDPS) services of a North American cloud service provider were ineffective against a simulated network timing channel attack. During the tests, three conspiring white hat agents exchanged a total of 33,024 network packets. As the proxy based attack executed, the vendor's intrusion detection service did not generate a warning, nor did its intrusion prevention service drop packets. Throughout the experiment, 4,096 bytes of randomized data (simulating covert traffic) were exchanged over a 2.06 hour period (4.4 bits-per-second); however, the vendor's Artificial Intelligence (AI) enabled threat detection service did not issue an alert. A Wilcoxon Ranked Sum test on the beforeand-after throughput confirmed none of the vendor's countermeasures triggered/intervened to a statistically significant degree (threat intel: p = 0.703, IDPS: p = 0.998, threat intel + IDPS: p = 0.118). These results indicate those accountable for data-oriented Service Organization Control (SOC) 2/3 reports (e.g., auditors, cybersecurity executives, etc.) should carefully examine the assurances offered by cloud service providers with regard to their network steganography defenses.

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Section: Timing Channel Data Representationmentioning

confidence: 99%

A Zero-Day Cloud Timing Channel Attack

Flowers

2022

IEEE Access

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“…Most generic methods fall into two subcategories that characterize their approach: statistical or machine learning. 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.…”

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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“…Data-at-rest refers to the data stored at an endpoint (such as a computer or mobile device) [14], where steganography predominantly involves the manipulation of static multimedia files by the user [15]. When a system is infiltrated, these users can either be internal (authorised user) or external (unauthorised user) and leak sensitive/confidential data.…”

Section: Data-at-restmentioning

confidence: 99%

Human visual based perception of steganographic images

Fyffe

Wang

Duncan

2019

Journal of Cyber Security Technology

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In 2014 it was estimated that 1.8 billion images were uploaded daily to the Internet, and in 2018 it is estimated that 3.2 billion images are shared daily. Some of these uploaded images may contain hidden information that can potentially be malicious (e.g. an image that contains hidden information regarding terrorism recruitment) or may cause serious damage (e.g. an employee wishing to hide sensitive company details in an image file and exporting the image to 3rd parties). This research studied the most effective methods in manipulating images to hide information (Data Loss).Significant work has been done on computational algorithmic detection. Yet the desired output from this work was to find the point at which a human can no longer visually establish the difference between an original image and a manipulated image. This research examines the extent of use for file formats, bit depth alterations, least significant bits, message and audio concealment and watermark and filtering techniques for image steganography. The findings of this study indicated that audio insertion and picture insertion into cover image files are the strongest in deceiving the human eye. These results have been categorised for human visual perception in image based steganography.

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Trends Toward Real-Time Network Data Steganography

Abstract: Network steganography has been a well-known covert data channeling method for over three decades.

Cited by 9 publications

References 22 publications

A Zero-Day Cloud Timing Channel Attack

A Zero-Day Cloud Timing Channel Attack

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

Human visual based perception of steganographic images

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