A composable approach to design of newer techniques for large-scale denial-of-service attack attribution

Since its early days, the Internet has witnessed not only a phenomenal growth, but also a large number of security attacks, and in recent years, denial-of-service (DoS) attacks have emerged as one of the top threats. The stateless and destination-oriented Internet routing combined with the ability to harness a large number of compromised machines and the relative ease and low costs of launching such attacks has made this a hard problem to address. Additionally, the myriad requirements of scalability, incremental deployment, adequate user privacy protections, and appropriate economic incentives has further complicated the design of DDoS defense mechanisms. While the many research proposals to date have focussed differently on prevention, mitigation, or traceback of DDoS attacks, the lack of a comprehensive approach satisfying the different design criteria for successful attack attribution is indeed disturbing. Our first contribution here has been the design of a composable data model that has helped us represent the various dimensions of the attack attribution problem, particularly the performance attributes of accuracy, effectiveness, speed and overhead, as orthogonal and mutually independent design considerations. We have then designed custom optimizations along each of these dimensions, and have further integrated them into a single composite model, to provide strong performance guarantees. Thus, the proposed model has given us a single framework that can not only address the individual shortcomings of the various known attack attribution techniques, but also provide a more wholesome counter-measure against DDoS attacks. Our second contribution here has been a concrete implementation based on the proposed composable data model, having adopted a graph-theoretic approach to identify and subsequently stitch together individual edge fragments in the Internet graph to reveal the true routing path of any network data packet. The proposed approach has been analyzed through theoretical and experimental evaluation across multiple metrics, including scalability, incremental deployment, speed and efficiency of the distributed algorithm, and finally the total overhead associated with its deployment. We have thereby shown that it is realistically feasible to provide strong performance and scalability guarantees for Internet-wide attack attribution. Our third contribution here has further advanced the state of the art by directly identifying individual path fragments in the Internet graph, having adopted a distributed divide-and-conquer approach employing simple recurrence relations as individual building blocks. A detailed analysis of the proposed approach on real-life Internet topologies with respect to network storage and traffic overhead, has provided a more realistic characterization. Thus, not only does the proposed approach lend well for simplified operations at scale but can also provide robust network-wide performance and security guarantees for Internet-wide attack attribution. Our final contribution here has introduced the notion of anonymity in the overall attack attribution process to significantly broaden its scope. The highly invasive nature of wide-spread data gathering for network traceback continues to violate one of the key principles of Internet use today - the ability to stay anonymous and operate freely without retribution. In this regard, we have successfully reconciled these mutually divergent requirements to make it not only economically feasible and politically viable but also socially acceptable. This work opens up several directions for future research - analysis of existing attack attribution techniques to identify further scope for improvements, incorporation of newer attributes into the design framework of the composable data model abstraction, and finally design of newer attack attribution techniques that comprehensively integrate the various attack prevention, mitigation and traceback techniques in an efficient manner

In-packet Bloom filters: Design and networking applications

The Bloom filter (BF) is a well-known space-efficient data structure that answers set membership queries with some probability of false positives. In an attempt to solve many of the limitations of current inter-networking architectures, some recent proposals rely on including small BFs in packet headers for routing, security, accountability or other purposes that move application states into the packets themselves. In this paper, we consider the design of such in-packet Bloom filters (iBF). Our main contributions are exploring the design space and the evaluation of a series of extensions (1) to increase the practicality and performance of iBFs, (2) to enable false-negative-free element deletion, and (3) to provide security enhancements. In addition to the theoretical estimates, extensive simulations of the multiple design parameters and implementation alternatives validate the usefulness of the extensions, providing for enhanced and novel iBF networking applications.Comment: 15 pages, 11 figures, preprint submitted to Elsevier COMNET Journa

On mitigating distributed denial of service attacks

Denial of service (DoS) attacks and distributed denial of service (DDoS) attacks are probably the most ferocious threats in the Internet, resulting in tremendous economic and social implications/impacts on our daily lives that are increasingly depending on the wellbeing of the Internet. How to mitigate these attacks effectively and efficiently has become an active research area. The critical issues here include 1) IP spoofing, i.e., forged source lIP addresses are routinely employed to conceal the identities of the attack sources and deter the efforts of detection, defense, and tracing; 2) the distributed nature, that is, hundreds or thousands of compromised hosts are orchestrated to attack the victim synchronously. Other related issues are scalability, lack of incentives to deploy a new scheme, and the effectiveness under partial deployment. This dissertation investigates and proposes effective schemes to mitigate DDoS attacks. It is comprised of three parts. The first part introduces the classification of DDoS attacks and the evaluation of previous schemes. The second part presents the proposed IP traceback scheme, namely, autonomous system-based edge marking (ASEM). ASEM enhances probabilistic packet marking (PPM) in several aspects: (1) ASEM is capable of addressing large-scale DDoS attacks efficiently; (2) ASEM is capable of handling spoofed marking from the attacker and spurious marking incurred by subverted routers, which is a unique and critical feature; (3) ASEM can significantly reduce the number of marked packets required for path reconstruction and suppress false positives as well. The third part presents the proposed DDoS defense mechanisms, including the four-color-theorem based path marking, and a comprehensive framework for DDoS defense. The salient features of the framework include (1) it is designed to tackle a wide spectrum of DDoS attacks rather than a specified one, and (2) it can differentiate malicious traffic from normal ones. The receiver-center design avoids several related issues such as scalability, and lack of incentives to deploy a new scheme. Finally, conclusions are drawn and future works are discussed

IP TRACEBACK Scenarios

Internet Protocol (IP) trace back is the enabling technology to control Internet crime. In this paper, we present novel and practical IP traceback systems which provide a defense system with the ability to find out the real sources of attacking packets that traverse through the network. IP traceback is to find the origin of an IP packet on the Internet without relying on the source IP address field. Due to the trusting nature of the IP protocol, the source IP address of a packet is not authenticated. As a result, the source address in an IP packet can be falsified (IP address spoofing). Spoof IP packets can be used for different attacks. The problem of finding the source of a packet is called the IP traceback problem. IP Traceback is a critical ability for identifying sources of attacks and instituting protection measures for the Internet. Most existing approaches to this problem have been tailored toward DDoS attack detection

Topology dependence of PPM-based Internet Protocol traceback schemes

Multiple schemes that utilize probabilistic packet marking (PPM) have been proposed to deal with Distributed Denial of Service (DDoS) attacks by reconstructing their attack graphs and identifying the attack sources. In the first part of this dissertation, we present our contribution to the family of PPM-based schemes for Internet Protocol (IP) traceback. Our proposed approach, Prediction-Based Scheme (PBS), consists of marking and traceback algorithms that reduce scheme convergence times by dealing with the problems of data loss and incomplete attack graphs exhibited by previous PPM-based schemes. Compared to previous PPM-based schemes, the PBS marking algorithm ensures that traceback is possible with about 54% as many total network packets, while the traceback algorithm takes about 33% as many marked packets for complete attack path construction. In the second part of this dissertation, we tackle the problem of scheme evaluation and comparison across discrepant network topologies. Previous research in this area has overlooked the influence of network topology on scheme performance and often utilized disparate and simplistic network abstractions to evaluate and compare these schemes. Our approach to this problem involves the evaluation of selected PPM-based schemes across a set of 60 Internet-like topologies and the adaptation of the network motif approach to provide a common ground for comparing the schemes\u27 performances in different network topologies. This approach allows us to determine the level of structural similarity between network topologies and consequently enables the comparison of scheme performance even when the schemes are implemented on different topologies. Furthermore, we identify three network-dependent factors that affect different PPM-based schemes uniquely causing a variation in, and discrepancy between, scheme performance from one network to another. Results indicate that scheme performance is dependent on the network upon which it is implemented, i.e. the value of the PPM-based schemes\u27 convergence times and their rankings vary depending on the underlying network topology. We show how the identified network factors contribute, individually and collectively, to the scheme performance in large-scale networks. Additionally, we identify five superfamilies from the 60 considered networks and find that networks within a superfamily also exhibit similar PPM-based scheme performance. To complement our results, we present an analytical model showing a link between scheme performance in any superfamily, and the motifs exhibited by the networks in that superfamily. Our work highlights a need for multiple network evaluation of network protocols. To this end, we demonstrate a method of identifying structurally similar network topologies among which protocol performance is potentially comparable. Our work also presents an effective way of comparing general network protocol performance in which the protocol is evaluated on specific representative networks instead of an entire set of networks

FAIR: Forwarding Accountability for Internet Reputability

This paper presents FAIR, a forwarding accountability mechanism that incentivizes ISPs to apply stricter security policies to their customers. The Autonomous System (AS) of the receiver specifies a traffic profile that the sender AS must adhere to. Transit ASes on the path mark packets. In case of traffic profile violations, the marked packets are used as a proof of misbehavior. FAIR introduces low bandwidth overhead and requires no per-packet and no per-flow state for forwarding. We describe integration with IP and demonstrate a software switch running on commodity hardware that can switch packets at a line rate of 120 Gbps, and can forward 140M minimum-sized packets per second, limited by the hardware I/O subsystem. Moreover, this paper proposes a "suspicious bit" for packet headers - an application that builds on top of FAIR's proofs of misbehavior and flags packets to warn other entities in the network.Comment: 16 pages, 12 figure

Towards Loop-Free Forwarding of Anonymous Internet Datagrams that Enforce Provenance

The way in which addressing and forwarding are implemented in the Internet constitutes one of its biggest privacy and security challenges. The fact that source addresses in Internet datagrams cannot be trusted makes the IP Internet inherently vulnerable to DoS and DDoS attacks. The Internet forwarding plane is open to attacks to the privacy of datagram sources, because source addresses in Internet datagrams have global scope. The fact an Internet datagrams are forwarded based solely on the destination addresses stated in datagram headers and the next hops stored in the forwarding information bases (FIB) of relaying routers allows Internet datagrams to traverse loops, which wastes resources and leaves the Internet open to further attacks. We introduce PEAR (Provenance Enforcement through Addressing and Routing), a new approach for addressing and forwarding of Internet datagrams that enables anonymous forwarding of Internet datagrams, eliminates many of the existing DDoS attacks on the IP Internet, and prevents Internet datagrams from looping, even in the presence of routing-table loops.Comment: Proceedings of IEEE Globecom 2016, 4-8 December 2016, Washington, D.C., US