Detecting Parallel Covert Data Transmission Channels in Video Conferencing Using Machine Learning

Covert communication channels are a concept in which a policy-breaking method is used in order to covertly transmit data from inside an organization to an external or accessible point. VoIP and Video systems are exposed to such attacks on different layers, such as the underlying real-time transport protocol (RTP) which uses Transmission Control Protocol (TCP) or User Datagram Protocol (UDP) packet streams to punch a hole through Network address translation (NAT). This paper presents different innovative attack methods utilizing covert communication and RTP channels to spread malware or to create a data leak channel between different organizations. The demonstrated attacks are based on a UDP punch hole created using Skype peer-to-peer video conferencing communication. The different attack methods were successfully able to transmit a small text file in an undetectable manner by observing the communication channel, and without causing interruption to the audio/video channels or creating a noticeable disturbance to the quality. While these attacks are hard to detect by the eye, we show that applying classical Machine Learning algorithms to detect these covert channels on statistical features sampled from the communication channel is effective for one type of attack.

A robust tracking system for COVID-19 like pandemic using advanced hybrid technologies

In order to track patients in coronavirus (COVID-19) like pandemic, this paper proposes a novel model based on hybrid advance technologies, which is capable to trace and track COVID-19 affectees with high accuracy. The hybrid technologies include, cellular, cyber and low range wireless technologies. This technique is capable to trace patients through call data record using cellular technology, voice over Internet protocol calls using cyber technology and physical contact without having a call history using low range wireless technologies. The proposed model is also capable to trace COVID-19 suspects. In addition to tracking, the proposed model is capable to provide surveillance capability as well by geo tagging the patients. In case of any violation by the patients an alert is sent to the concerned department. The proposed model is cost effective and privacy preserved as the entire process is carried out under the umbrella of a concerned government department. The potential outcomes of the proposed model are tracking of COVID-19 patients, monitoring of isolated patients, tracking of suspected ones and inform the mass about the safest path to use. [ABSTRACT FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When Evil Calls: Targeted Adversarial Voice over IP Network

As the COVID-19 pandemic fundamentally reshaped the remote life and working styles, Voice over IP (VoIP) telephony and video conferencing have become a primary method of connecting communities together. However, little has been done to understand the feasibility and limitations of delivering adversarial voice samples via such communication channels. In this paper, we propose TAINT-Targeted Adversarial Voice over IP Network, the first targeted, query-efficient, hard label black-box, adversarial attack on commercial speech recognition platforms over VoIP. The unique channel characteristics of VoIP pose significant new challenges, such as signal degradation, random channel noise, frequency selectivity, etc. To address these challenges, we systematically analyze the structure and channel characteristics of VoIP through reverse engineering. A noise-resilient efficient gradient estimation method is then developed to ensure a steady and fast convergence of the adversarial sample generation process. We demonstrate our attack in both over-the-air and over-the-line settings on four commercial automatic speech recognition (ASR) systems over the five most popular VoIP Conferencing Software (VCS). We show that TAINT can achieve performance that is comparable to the existing methods even with the addition of VoIP channel. Even in the most challenging scenario where there is an active speaker in Zoom, TAINT can still succeed within 10 attempts while staying out of the speaker focus of the video conference. © 2022 Owner/Author.

Forensic Analysis of Microsoft Teams: Investigating Memory, Disk and Network

Videoconferencing applications have seen a jump in their userbase owing to the COVID-19 pandemic. The security of these applications has certainly been a hot topic since millions of VoIP users’ data is involved. However, research pertaining to VoIP forensics is still limited to Skype and Zoom. This paper presents a detailed forensic analysis of Microsoft Teams, one of the top 3 videoconferencing applications, in the areas of memory, disk-space and network forensics. Extracted artifacts include critical user data, such as emails, user account information, profile photos, exchanged (including deleted) messages, exchanged text/media files, timestamps and Advanced Encryption Standard encryption keys. The encrypted network traffic is investigated to reconstruct client-server connections involved in a Microsoft Teams meeting with IP addresses, timestamps and digital certificates. The conducted analysis demonstrates that, with strong security mechanisms in place, user data can still be extracted from a client’s desktop. The artifacts also serve as digital evidence in the court of Law, in addition to providing forensic analysts a reference for cases involving Microsoft Teams. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.