We introduce a new simulation platform called Insight, created to design and simulate cyber-attacks against large arbitrary target scenarios. Insight has surprisingly low hardware and configuration requirements, while making the simulation a realistic experience from the attacker's standpoint. The scenarios include a crowd of simulated actors: network devices, hardware devices, software applications, protocols, users, etc. A novel characteristic of this tool is to simulate vulnerabilities (including 0-days) and exploits, allowing an attacker to compromise machines and use them as pivoting stones to continue the attack. A user can test and modify complex scenarios, with several interconnected networks, where the attacker has no initial connectivity with the objective of the attack. We give a concise description of this new technology, and its possible uses in the security research field, such as pentesting training, study of the impact of 0-days vulnerabilities, evaluation of security countermeasures, and risk assessment tool.
Other interesting approaches to solve these problems include the framework developed by @cite_20 . While they focus on distributed denial of service attacks (DDoS) and defensive IDS analysis, we focus on offensive strategies to understand the scenarios and develop countermeasures. Also @cite_8 have integrated @cite_13 and @cite_16 to create a flexible and very detailed network laboratory and simulation tool. The latter project has privileged accuracy and virtualization over scalability and performance.
@cite_16 @cite_13 @cite_20 @cite_8
2160502705 2159160833 2162133150 2170810185
Due to the dynamic, distributed, and heterogeneous nature of today's networks, intrusion detection systems (IDSs) have become a necessary addition to the security infrastructure and are widely deployed as a complementary line of defense to classical security approaches. In this paper, we address the intrusion detection problem in heterogeneous networks consisting of nodes with different noncorrelated security assets. In our study, two crucial questions are: What are the expected behaviors of rational attackers? What is the optimal strategy of the defenders (IDSs)? We answer the questions by formulating the network intrusion detection as a noncooperative game and performing an in-depth analysis on the Nash equilibrium and the engineering implications behind. Based on our game theoretical analysis, we derive the expected behaviors of rational attackers, the minimum monitor resource requirement, and the optimal strategy of the defenders. We then provide guidelines for IDS design and deployment. We also show how our game theoretical framework can be applied to configure the intrusion detection strategies in realistic scenarios via a case study. Finally, we evaluate the proposed game theoretical framework via simulations. The simulation results show both the correctness of the analytical results and the effectiveness of the proposed guidelines. Launching a denial of service (DoS) attack is trivial, but detection and response is a painfully slow and often a manual process. Automatic classification of attacks as single- or multi-source can help focus a response, but current packet-header-based approaches are susceptible to spoofing. This paper introduces a framework for classifying DoS attacks based on header content, and novel techniques such as transient ramp-up behavior and spectral analysis. Although headers are easily forged, we show that characteristics of attack ramp-up and attack spectrum are more difficult to spoof. To evaluate our framework we monitored access links of a regional ISP detecting 80 live attacks. Header analysis identified the number of attackers in 67 attacks, while the remaining 13 attacks were classified based on ramp-up and spectral analysis. We validate our results through monitoring at a second site, controlled experiments, and simulation. We use experiments and simulation to understand the underlying reasons for the characteristics observed. In addition to helping understand attack dynamics, classification mechanisms such as ours are important for the development of realistic models of DoS traffic, can be packaged as an automated tool to aid in rapid response to attacks, and can also be used to estimate the level of DoS activity on the Internet. Denial of service (DoS) attack on the Internet has become a pressing problem. In this paper, we describe and evaluate route-based distributed packet filtering (DPF), a novel approach to distributed DoS (DDoS) attack prevention. We show that DPF achieves proactiveness and scalability, and we show that there is an intimate relationship between the effectiveness of DPF at mitigating DDoS attack and power-law network topology.The salient features of this work are two-fold. First, we show that DPF is able to proactively filter out a significant fraction of spoofed packet flows and prevent attack packets from reaching their targets in the first place. The IP flows that cannot be proactively curtailed are extremely sparse so that their origin can be localized---i.e., IP traceback---to within a small, constant number of candidate sites. We show that the two proactive and reactive performance effects can be achieved by implementing route-based filtering on less than 20 of Internet autonomous system (AS) sites. Second, we show that the two complementary performance measures are dependent on the properties of the underlying AS graph. In particular, we show that the power-law structure of Internet AS topology leads to connectivity properties which are crucial in facilitating the observed performance effects. This paper presents a new distributed approach to detecting DDoS (distributed denial of services) flooding attacks at the traffic-flow level The new defense system is suitable for efficient implementation over the core networks operated by Internet service providers (ISPs). At the early stage of a DDoS attack, some traffic fluctuations are detectable at Internet routers or at the gateways of edge networks. We develop a distributed change-point detection (DCD) architecture using change aggregation trees (CAT). The idea is to detect abrupt traffic changes across multiple network domains at the earliest time. Early detection of DDoS attacks minimizes the floe cling damages to the victim systems serviced by the provider. The system is built over attack-transit routers, which work together cooperatively. Each ISP domain has a CAT server to aggregate the flooding alerts reported by the routers. CAT domain servers collaborate among themselves to make the final decision. To resolve policy conflicts at different ISP domains, a new secure infrastructure protocol (SIP) is developed to establish mutual trust or consensus. We simulated the DCD system up to 16 network domains on the Cyber Defense Technology Experimental Research (DETER) testbed, a 220-node PC cluster for Internet emulation experiments at the University of Southern California (USC) Information Science Institute. Experimental results show that four network domains are sufficient to yield a 98 percent detection accuracy with only 1 percent false-positive alarms. Based on a 2006 Internet report on autonomous system (AS) domain distribution, we prove that this DDoS defense system can scale well to cover 84 AS domains. This security coverage is wide enough to safeguard most ISP core networks from real-life DDoS flooding attacks.