Attribution of cybercrimes is significant in limiting the rate of crime as well as in preparing the required level of response. Motivated by this significance, we introduce a level-based approach for achieving attribution. In our proposed approach, attribution consists of three steps: (1) identification of the cyberweapon used; (2) determination of the origin of the attack; and (3) identification of the actual attacker. We conduct an in-depth analysis of recently proposed attribution techniques. Our analysis reveals that indirect methods of attribution are particularly effective when attributing cybercrimes; many of them remain unattributed. We also discuss some of the legal issues pertaining to attribution, and we argue that well-defined international laws for cyberspace along with strong cooperation among governments are needed to track down and punish cybercriminals.

Optimizing the total power consumption within a data center is an NP-hard problem. Project nano data center was started with the promise of reducing power and cooling cost. This paper is focused on reducing the total power consumption within a nano data center by introducing power-aware server selection. We studied effects of several attributes such as power consumption of servers, size of request, and power consumed by routers, in reducing overall power within a nano-data center. Inspired by our study, we introduced two heuristics for the selection of nano servers as well as intermediate routers. Our heuristics are based on selecting the shortest path with respect to number of intermediate routers and using the least recently used server to serve a request. Through GNS-3 based experiments, we confirmed that our proposed heuristics reduce the overall power consumption in a data center while avoiding the time complexity of the optimal solution.