Biblio

The 911 emergency service belongs to one of the 16 critical infrastructure sectors in the United States. Distributed denial of service (DDoS) attacks launched from a mobile phone botnet pose a significant threat to the availability of this vital service. In this paper we show how attackers can exploit the cellular network protocols in order to launch an anonymized DDoS attack on 911. The current FCC regulations require that all emergency calls be immediately routed regardless of the caller's identifiers (e.g., IMSI and IMEI). A rootkit placed within the baseband firmware of a mobile phone can mask and randomize all cellular identifiers, causing the device to have no genuine identification within the cellular network. Such anonymized phones can issue repeated emergency calls that cannot be blocked by the network or the emergency call centers, technically or legally. We explore the 911 infrastructure and discuss why it is susceptible to this kind of attack. We then implement different forms of the attack and test our implementation on a small cellular network. Finally, we simulate and analyze anonymous attacks on a model of current 911 infrastructure in order to measure the severity of their impact. We found that with less than 6K bots (or \$100K hardware), attackers can block emergency services in an entire state (e.g., North Carolina) for days. We believe that this paper will assist the respective organizations, lawmakers, and security professionals in understanding the scope of this issue in order to prevent possible 911-DDoS attacks in the future.

Ensuring system survivability in the wake of advanced persistent threats is a big challenge that the security community is facing to ensure critical infrastructure protection. In this paper, we define metrics and models for the assessment of coordinated massive malware campaigns targeting critical infrastructure sectors. First, we develop an analytical model that allows us to capture the effect of neighborhood on different metrics (infection probability and contagion probability). Then, we assess the impact of putting operational but possibly infected nodes into quarantine. Finally, we study the implications of scanning nodes for early detection of malware (e.g., worms), accounting for false positives and false negatives. Evaluating our methodology using a small four-node topology, we find that malware infections can be effectively contained by using quarantine and appropriate rates of scanning for soft impacts.