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A survey on representation learning efforts in cybersecurity domain

- Usman, Muhammad, Jan, Mian, He, Xiangjian, Chen, Jinjun

  • Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Chen, Jinjun
  • Date: 2020
  • Type: Text , Journal article
  • Relation: ACM computing surveys Vol. 52, no. 6 (2020), p. 1-28
  • Full Text: false
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  • Description: In this technology-based era, network-based systems are facing new cyber-attacks on daily bases. Traditional cybersecurity approaches are based on old threat-knowledge databases and need to be updated on a daily basis to stand against new generation of cyber-threats and protect underlying network-based systems. Along with updating threat-knowledge databases, there is a need for proper management and processing of data generated by sensitive real-time applications. In recent years, various computing platforms based on representation learning algorithms have emerged as a useful resource to manage and exploit the generated data to extract meaningful information. If these platforms are properly utilized, then strong cybersecurity systems can be developed to protect the underlying network-based systems and support sensitive real-time applications. In this survey, we highlight various cyber-threats, real-life examples, and initiatives taken by various international organizations. We discuss various computing platforms based on representation learning algorithms to process and analyze the generated data. We highlight various popular datasets introduced by well-known global organizations that can be used to train the representation learning algorithms to predict and detect threats. We also provide an in-depth analysis of research efforts based on representation learning algorithms made in recent years to protect the underlying network-based systems against current cyber-threats. Finally, we highlight various limitations and challenges in these efforts and available datasets that need to be considered when using them to build cybersecurity systems.

Error concealment for cloud-based and scalable video coding of hd videos

- Usman, Muhammad, He, Xiangjian, Lam, Kin-Man, Xu, Min, Bokhari, Syed, Chen, Jinjun, Jan, Mian

  • Authors: Usman, Muhammad , He, Xiangjian , Lam, Kin-Man , Xu, Min , Bokhari, Syed , Chen, Jinjun , Jan, Mian
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE transactions on cloud computing Vol. 7, no. 4 (2019), p. 975-987
  • Full Text: false
  • Reviewed:
  • Description: The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.
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