Object Detection from Cloud to Edge

This thesis studies the differences of object detection between cloud computing and edge computing systems. The objective is to create a view on how fast and accurately object detection can be done using novel edge computing technologies, compared to cloud computing approaches. The research uses literature review to create a view of the current state of cloud computing and edge computing paradigms and real-time object detection. Then, an empirical test is performed to test the performance of a selected edge hardware using real-world data. The perks of the test system are further discussed together with the findings of the empirical test. First in the literature review, cloud computing and edge computing paradigms are studied. Their basic components and logic are presented, together with the main challenges emerging from them. Then, object detection as an application of machine learning and computer vision is reviewed. The research then briefly represents the functioning logic of two distinct real-time object detection models, that are later used in the empirical test of this work. Finally, ways to measure object detection performance are explored. In the empirical test, two selected object detection models are implemented into the selected edge hardware. Testing data is then applied to the machine by emulating an IP camera stream with a record of traffic video. Metrics and measurements, such as inference times and CPU utilization are then sent to a cloud server, giving insight into the performance of the device using each model. The architecture includes an edge hardware, that has an integrated cloud service, through which the application and device management is done. In the light of the empirical study’s results, it is found that new edge computing hardware can do object detection in real-time, still resulting in slower inference times than cloud solutions that have practically unlimited computing resources. However, when considering for example the data transfer latencies when using cloud instances, the overall object detection process performed at the edge can compete with the cloud solutions. The empirical test set up followed a new approach to the edge paradigm, using an integrated service between the cloud and edge. When comparing this process further to the obstacles found in cloud and edge computing technologies, some improvements and partly solutions can be identified. These include improvements in security, latency, and delivery processes

A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a "Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named "Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.Comment: in 18th International Conference on Extending Database Technology (EDBT) (2015

PeerHunter: Detecting Peer-to-Peer Botnets through Community Behavior Analysis

Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the infrastructure that responsible for various of cyber-crimes. Though a few existing work claimed to detect traditional botnets effectively, the problem of detecting P2P botnets involves more challenges. In this paper, we present PeerHunter, a community behavior analysis based method, which is capable of detecting botnets that communicate via a P2P structure. PeerHunter starts from a P2P hosts detection component. Then, it uses mutual contacts as the main feature to cluster bots into communities. Finally, it uses community behavior analysis to detect potential botnet communities and further identify bot candidates. Through extensive experiments with real and simulated network traces, PeerHunter can achieve very high detection rate and low false positives.Comment: 8 pages, 2 figures, 11 tables, 2017 IEEE Conference on Dependable and Secure Computin