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An Optical Bust Switching (OBS) network is vulnerable to a range of issues. One of the most significant issues is Burst Header Packet (BHP) flooding attacks, which can negatively impact on the Quality of Service (QoS) and create more urgent issues such as Denial of Service (DoS). Existing techniques for countering BHP flood attacks usually display a low accuracy in detecting misbehaving nodes leading to BHP attacks. By contrast, Machine Learning (ML) is a widely adopted and powerful data analysis technique which has showed a high degree of predictive performance in multiple application domains due to its ability to discover beneficial knowledge for decision-making. This study investigates the use of predictive ML to counter the risk of BHP flooding attacks experienced in OBS networks, proposing a decision tree-based architecture as an appropriate solution. This contains a learning algorithm that extracts novel rules from tree models using data processed from several simulation runs. The results show that the rules derived from our learning algorithm will accurately classify 93% of the BHP flooding attacks into either Behaving (B) or Misbehaving (M) classes. Moreover, the rules can further classify the Misbehaving edge nodes into four sub-class labels with 87% accuracy, including: Misbehaving-Block (Block), Behaving-No Block (No Block), Misbehaving-No Block (M-No Block), and Misbehaving-Wait (M-Wait). The results clearly show that our proposed decision tree model is a viable solution compared to decisions undertaken by expert domains or human network administrators.

To improve the flow of quality information and combat fake news on social media, it is essential to identify the origins and evolution patterns of false information. However, scholarship dedicated to this area is lacking. Using a recent development in the field of computational network science (i.e., evolution tree analysis), this study examined this issue in the context of the 2016 US presidential election. By retrieving 307,738 tweets about 30 fake and 30 real news stories, we examined the root content, producers of original source, and evolution patterns. The findings revealed that root tweets about fake news were mostly generated by accounts from ordinary users, but they often included a link to non-credible news websites. Additionally, we observed significant differences between real and fake news stories in terms of evolution patterns. In our evolution tree analysis, tweets about real news showed wider breadth and shorter depth than tweets about fake news. The results also indicated that tweets about real news spread widely and quickly, but tweets about fake news underwent a greater number of modifications in content over the spreading process.

A variety of anti-malware scanners have been developed for malware detection. Previous research has indicated that combining multiple different scanners can achieve better result compared to any single scanner. However, given the diversity in detection rates and accuracy of different anti-malware scanners, how to determine the best possible outcome of multi-scanner systems in terms of accuracy and how to achieve this best outcome remain formidable tasks. In this paper, we propose three models to capture the combined output of different combinations of anti-malware scanners based on the limited amount of historical information available. These models enable us to predict the accuracy level of each combination, which helps us to determine the optimal configuration of the multi-scanner detection system to achieve maximum accuracy. We also introduce two methods to identify a near-optimal subset of scanners that can help reduce scanning cost while under time constraint. From simulations over randomly generated hypothetical datasets and experiments conducted with real world malware and goodware datasets and anti-virus scanners, we found that our models perform well in predicting the optimal configuration and can achieve an accuracy as high as within 1% of true maximum.

Routing is one of the most challenging development issues in Delay-Tolerant Networks (DTNs) because of lack of continuous connection. Existing routing schemes for DTNs provide best effort service, but are unable to optimize QoS and support message priority. In this paper, we present a Look-Ahead Routing and Message Scheduling approach (ALARMS) which exploits more accurate knowledge about various parameters regarding routing to achieve better QoS in the DTN. We assume a variation of the well-known ferry model, in which there are ferry nodes moving along pre-defined routes to exchange messages with the gateway node of each region on the route and also pass to the gateway nodes look-ahead routing information about when it will arrive at each gateway node on the route in the next two rounds and how long it will stay. The gateway nodes use this information to estimate the delivery delay of each message when being delivered by different ferries, and schedule the message to be delivered by the ferry which arrives earliest at the destination. Simulation results show that ALARMS outperforms three existing routing protocols: epidemic routing, Spray-and-Wait, and Spray-and-Focus, in terms of delay time, delivery ratio, and overhead. We also discuss five enhancement strategies on ALARMS and how ALARMS can support message prioritization.

Traditional certificates are designed to establish and document characteristics belonging to a specific individual, be it an identification number (i.e., social security number, driver's license number), a level of achievement (i.e., college degree, license to practice a profession), or membership status (i.e., company ID, trade union card). The digital certificate extends this concept into the electronic world, identifying and linking the certificate holder to a public encryption key that is subsequently used as a means of identification. Current identity certificates provide unique identification and tracking, however it is exactly these characteristics that have led to concerns over identity theft and privacy of personal information. The veiled certificate introduced in this paper addresses these issues by providing means of linking certificates from multiple certifying authorities while masking the user's identity from non-authorized individuals and satisfying the regulatory need of unique, explicit identification. With the ability to be implemented within existing X.509 standards, veiled certification extends traditional digital certificates with features useful in combating identity theft and invasion of privacy.

We propose an architecture for securely resolving IP addresses into hardware addresses over an Ethernet. The proposed architecture consists of a secure server connected to the Ethernet and two protocols: an invite-accept protocol and a request-reply protocol. Each computer connected to the Ethernet can use the invite-accept protocol to periodically record its IP address and its hardware address in the database of the secure server. Each computer can later use the request-reply protocol, to obtain the hardware address of any other computer connected to the Ethernet from the database of the secure server. These two protocols are designed to overcome the actions of any adversary that can lose sent messages, arbitrarily modify the fields of sent messages, and replay old messages.