NICE: Network Intrusion Detection and Countermeasure Selection in Virtual Network Systems

NICE: Network Intrusion Detection and Countermeasure Selection in Virtual Network Systems
Jul/Aug 2013
Cloud security is one of most important issues that has attracted a lot of research and development effort in past few years. Particularly, attackers can explore vulnerabilities of a cloud system and compromise virtual machines to deploy further large-scale Distributed Denial-of-Service (DDoS). DDoS attacks usually involve early stage actions such as multistep exploitation, low-frequency vulnerability scanning, and compromising identified vulnerable virtual machines as zombies, and finally DDoS attacks through the compromised zombies. Within the cloud system, especially the Infrastructure-as-a-Service (IaaS) clouds, the detection of zombie exploration attacks is extremely difficult. This is because cloud users may install vulnerable applications on their virtual machines. To prevent vulnerable virtual machines from being compromised in the cloud, we propose a multiphase distributed vulnerability detection, measurement, and countermeasure selection mechanism called NICE, which is built on attack graph-based analytical models and reconfigurable virtual network-based countermeasures. The proposed framework leverages OpenFlow network programming APIs to build a monitor and control plane over distributed programmable virtual switches to significantly improve attack detection and mitigate attack consequences. The system and security evaluations demonstrate the efficiency and effectiveness of the proposed solution.

 In traditional data centers, where system administrators have full control over the host machines, vulnerabilities can be detected and patched by the system administrator in a centralized manner.
However, patching known security holes in cloud data centers, where cloud users usually have the privilege to control software installed on their managed VMs, may not work effectively and can violate the Service Level Agreement (SLA).

Detecting malicious behavior
 Duan et al. focused on the detection of compromised machines that have been recruited to serve as spam zombies. Their approach, SPOT, is based on sequentially scanning outgoing messages while employing a statistical method Sequential Probability Ratio Test (SPRT), to quickly determine whether or not a host has been compromised.
 BotHunter detected compromised machines based on the fact that a thorough malware infection process has a number of well defined stages that allow correlating the intrusion alarms triggered by inbound traffic with resulting outgoing communication patterns.
 BotSniffer exploited uniform spatial-temporal behavior characteristics of compromised machines to detect zombies by grouping flows according to server connections and searching for similar behavior in the flow. An attack graph is able to represent a series of exploits, called atomic attacks, that lead to an undesirable state, for example a state where an attacker has obtained administrative access to a machine. There are many automation tools to construct attack graph.
Binary Decision Diagrams (BDDs)
 O. Sheyner et al. proposed a technique based on a modified symbolic model checking NuSMV and Binary Decision Diagrams (BDDs) to construct attack graph.
Their model can generate all possible attack paths, however, the scalability is a big issue for this solution.

Intrusion Detection System
 IDS and firewall are widely used to monitor and detect suspicious events in the network.
The false alarms and the large volume of raw alerts from IDS are two major problems for any IDS implementations.

 Many attack graph based alert correlation techniques have been proposed recently. L. Wang et al. devised an in-memory structure, called queue graph (QG), to trace alerts matching each exploit in the attack graph.
The implicit correlations in this design make it difficult to use the correlated alerts in the graph for analysis of similar attack scenarios.

 Roschke et al. proposed a modified attack-graph-based correlation algorithm to create explicit correlations only by matching alerts to specific exploitation nodes in the attack graph with multiple mapping functions, and devised an alert dependencies graph (DG) to group related alerts with multiple correlation criteria.
Attack countermeasure tree
 Roy et al. proposed an attack countermeasure tree (ACT) to consider attacks and countermeasures together in an attack tree structure. They devised several objective functions based on greedy and branch and bound techniques to minimize the number of countermeasure, reduce investment cost, and maximize the benefit from implementing a certain countermeasure set. In their design, each countermeasure optimization problem could be solved with and without probability assignments to the model.
However, their solution focuses on a static attack scenario and predefined countermeasure for each attack.
 N. Poolsappasit et al. proposed a Bayesian attack graph (BAG) to address dynamic security risk management problem and applied a genetic algorithm to solve countermeasure optimization problem.

 NICE (Network Intrusion detection and Countermeasure sElection in virtual network systems) is proposed to establish a defense-in-depth intrusion detection framework.
 For better attack detection, NICE incorporates attack graph analytical procedures into the intrusion detection processes.
 The design of NICE does not intend to improve any of the existing intrusion detection algorithms; indeed, NICE employs a reconfigurable virtual networking approach to detect and counter the attempts to compromise VMs, thus preventing zombie VMs.
 Deploy a lightweight mirroring-based network intrusion detection agent (NICE-A) on each cloud server to capture and analyze cloud traffic. A NICE-A periodically scans the virtual system vulnerabilities within a cloud server to establish Scenario Attack Graph (SAGs), and then based on the severity of identified vulnerability towards the collaborative attack goals, NICE will decide whether or not to put a VM in network inspection state.
 Once a VM enters inspection state, Deep Packet Inspection (DPI) is applied, and/or virtual network reconfigurations can be deployed to the inspecting VM to make the potential attack behaviors prominent.
 By using software switching techniques, NICE constructs a mirroring-based traffic capturing framework to minimize the interference on users’ traffic compared to traditional bump-in-the-wire (i.e., proxy-based) IDS/IPS.
 NICE enables the cloud to establish inspection and quarantine modes for suspicious VMs according to their current vulnerability state in the current SAG.
 Based on the collective behavior of VMs in the SAG, NICE can decide appropriate actions, for example DPI or traffic filtering, on the suspicious VMs. Using this approach, NICE does not need to block traffic flows of a suspicious VM in its early attack stage.


 NICE significantly advances the current network IDS/IPS solutions by employing programmable virtual networking approach that allows the system to construct a dynamic reconfigurable IDS system.
 NICE, a new multi-phase distributed network intrusion detection and prevention framework in a virtual networking environment that captures and inspects suspicious cloud traffic without interrupting users’ applications and cloud services.
 NICE incorporates a software switching solution to quarantine and inspect suspicious VMs for further investigation and protection. Through programmable network approaches, NICE can improve the attack detection probability and improve the resiliency to VM exploitation attack without interrupting existing normal cloud services.
 NICE employs a novel attack graph approach for attack detection and prevention by correlating attack behavior and also suggests effective countermeasures.
 NICE optimizes the implementation on cloud servers to minimize resource consumption. Our study shows that NICE consumes less computational overhead compared to proxy-based network intrusion detection solutions.

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