Marine network protocols are domain-specific network protocols that aim to incorporate particular features within the specialized marine context that devices are implemented in. Devices implemented in such vessels involve critical equipment; however, limited research exists for marine network protocol security. In this paper, we provide an analysis of several marine network protocols used in today’s vessels and provide a classification of attack risks. Several protocols involve known security limitations, such as Automated Identification System (AIS) and National Marine Electronic Association (NMEA) 0183, while newer protocols, such as OneNet provide more security hardiness. We further identify several challenges and opportunities for future implementations of such protocols.

Identity deception in social media applications has negatively impacted online communities and it is likely to increase as the social media user population grows. The ease of generating new accounts on social media has exacerbated the issue. Many previous studies have been posited that focused on both verbal, non-verbal, and network data produced by users in an attempt to detect identity deception. However, although these methods produced a high accuracy, they are mainly reactive to the issue of identity deception. This paper proposes a proactive approach that leverages social network data and it is focused on identity deception prevention for online sub-communities, communities that exist within larger communities (e.g., Facebook groups or Subreddits). The method can be applied to various types of social media applications and produces high accuracy in identifying deceptive accounts at the time of attempted entry to a sub-community. Performance results as well as limitations for the method are presented. A discussion follows on the identification of possible implications of this paper for social media applications and future directions on deception prevention are proposed.

In the last few years, we have witnessed an explosive growth of fake content on the Internet which has significantly affected the veracity of information on many social platforms. Much of this disruption has been caused by the proliferation of advanced machine and deep learning methods. In turn, social platforms have been using the same technological methods in order to detect fake content. However, there is understanding of the strengths and weaknesses of these detection methods. We describe examples of machine and deep learning approaches that can be used to detect different types of fake content. We also discuss the characteristics and the potential for adversarial attacks on these methods that could reduce the accuracy of fake content detection. Finally, we identify and discuss some future research challenges in this area.

Network anomaly detection solutions are being used as defense against several attacks, especially those related to data exfiltration. Several methods exist in the literature, such as clustering or neural networks. However, these methods often focus on local and global network indicators instead of network structural properties, such as understanding which devices typically communicate with other devices. To address this literature gap, we propose a method that uses exponential random graph modeling to integrate network topology structure statistics in anomaly detection. We demonstrate the effectiveness of our method using real-world examples as a baseline for experiments on domain name system (DNS) data exfiltration scenarios. We highlight how our method provides better insight into how network traffic may alter network graph structure and how this can assist cybersecurity analysts in making better decisions in conjunction with existing intrusion detection systems. Finally, we compare and contrast the accuracy, false positive rate and computational overhead of our method with other methods.