Securing and managing home networks has recently become an increasingly challenging task due to the rapid growth of devices, applications and traffic in these networks. This paper presents a novel object-oriented big data security analytics for making sense of traffic data collection from home networks. We extract the source IP addresses from unwanted traffic towards real home networks as objects of interest, and subsequently characterize these objects with heterogeneous and streaming data sources including intrusion detection logs provided from distributed firewalls, Internet routing table snapshots from BGP routers, active probing results from open DNS resolver scanning, and IP-togeographical mapping database. Our preliminary results have revealed a number of important findings and correlations on the objects of interests from these diverse and massive data-sets. To the best of our knowledge, this position paper is the first effort to introduce object-oriented perspective to perform security analytics on home network traffic.