Improving Performance of Cross-Domain Firewalls in Multi-Firewall System

Firewall is used to protect local network from outside untrusted public network or Internet. Every packet coming to and going out from network is inspected at Firewall. Local network policies are converted into rules and stored in firewall. It is used to restrict access of the external network into local network and vice versa. Packets are checked against the rules serially. Therefore increase in the number of rules decreases the firewall performance. The key thing in performance improvement is to reduce number of firewall rules. Optimization helps to reduce number of rules by removing anomalies and redundancies in the rule list. It is observed that only reducing number of rules is not sufficient as the major time is consumed in rule verification. Therefore to reduce time of rule checking fast verification method is used. Prior work focuses on either Intrafirewall optimization or Interfirewall optimization within single administrative domain. In cross-domain firewall optimization key thing is to keep rules secure from others as they contain confidential information which can be exploited by attackers. The proposed system implements cross-domain firewall rule optimization. For optimization multi-firewall environment is considered. Then optimized rule set is converted to Binary Tree Firewall (BTF) so as to reduce packet checking time and improve firewall performance further. DOI: 10.17762/ijritcc2321-8169.16047

Neural Packet Classification

Packet classification is a fundamental problem in computer networking. This problem exposes a hard tradeoff between the computation and state complexity, which makes it particularly challenging. To navigate this tradeoff, existing solutions rely on complex hand-tuned heuristics, which are brittle and hard to optimize. In this paper, we propose a deep reinforcement learning (RL) approach to solve the packet classification problem. There are several characteristics that make this problem a good fit for Deep RL. First, many of the existing solutions are iteratively building a decision tree by splitting nodes in the tree. Second, the effects of these actions (e.g., splitting nodes) can only be evaluated once we are done with building the tree. These two characteristics are naturally captured by the ability of RL to take actions that have sparse and delayed rewards. Third, it is computationally efficient to generate data traces and evaluate decision trees, which alleviate the notoriously high sample complexity problem of Deep RL algorithms. Our solution, NeuroCuts, uses succinct representations to encode state and action space, and efficiently explore candidate decision trees to optimize for a global objective. It produces compact decision trees optimized for a specific set of rules and a given performance metric, such as classification time, memory footprint, or a combination of the two. Evaluation on ClassBench shows that NeuroCuts outperforms existing hand-crafted algorithms in classification time by 18% at the median, and reduces both time and memory footprint by up to 3x

Optimization of delays experienced by packets due to ACLs within a domain

The infrastructure of large networks is broken down into areas that have a common security policy called a domain. Security within a domain is commonly implemented at all nodes however this has a negative effect on performance since it introduces a delay associated with packet filtering. Recommended techniques for network design imply that every packet should be checked at the first possible ingress points of the network. When access control lists (ACL's) are used within a router for this purpose then there can be a significant overhead associated with this process. The purpose of this paper is to consider the effect of delays when using router operating systems offering different levels of functionality. It considers factors which contribute to the delay particularly due to ACL. Using theoretical principles modified by practical calculation a model is created for packet delay for all nodes across a given path in a domain

A Computational Approach to Packet Classification

Multi-field packet classification is a crucial component in modern software-defined data center networks. To achieve high throughput and low latency, state-of-the-art algorithms strive to fit the rule lookup data structures into on-die caches; however, they do not scale well with the number of rules. We present a novel approach, NuevoMatch, which improves the memory scaling of existing methods. A new data structure, Range Query Recursive Model Index (RQ-RMI), is the key component that enables NuevoMatch to replace most of the accesses to main memory with model inference computations. We describe an efficient training algorithm that guarantees the correctness of the RQ-RMI-based classification. The use of RQ-RMI allows the rules to be compressed into model weights that fit into the hardware cache. Further, it takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector instructions, achieving a rate of tens of nanoseconds per lookup. Our evaluation using 500K multi-field rules from the standard ClassBench benchmark shows a geometric mean compression factor of 4.9x, 8x, and 82x, and average performance improvement of 2.4x, 2.6x, and 1.6x in throughput compared to CutSplit, NeuroCuts, and TupleMerge, all state-of-the-art algorithms.Comment: To appear in SIGCOMM 202

To Provide An Innovative Policy Anomaly Management Framework For Firewalls

- Firewalls have been widely organized on the Internet for securing private networks. A firewall checks each incoming or outgoing packet to choose whether to accept or discard the packet based on its policy. Optimizing firewall policies is vital for improving network performance. In this paper we propose the first cross-domain privacy-preserving cooperative firewall policy optimization protocol. Specifically for any two adjacent firewalls belonging to two different administrative domains our protocol can recognize in each firewall the rules that can be removed because of the other firewall. The optimization process involves cooperative computation between the two firewalls without any party disclosing its policy to the other. Firewalls are significant in securing private networks of businesses, institutions and home networks. A firewall is frequently placed at the entry between a private network and the external network so that it can ensure each incoming or outgoing packet and choose whether to accept or abandon the packet based on its policy. A firewall policy is typically specified as a sequence of rules called Access Control List (ACL) and each rule has a predicate over multiple packet header fields i.e., source IP, destination IP, source port, destination port, and protocol type and a decision i.e., accept and discard for the packets that counterpart the predicate.  In this paper we recommend the first cross-domain privacy- preserving cooperative firewall policy optimization protocol

Algorithmes efficaces de gestion des règles dans les réseaux définis par logiciel

In software-defined networks (SDN), the filtering requirements for critical applications often vary according to flow changes and security policies. SDN addresses this issue with a flexible software abstraction, allowing simultaneous and convenient modification and implementation of a network policy on flow-based switches.With the increase in the number of entries in the ruleset and the size of data that traverses the network each second, it remains crucial to minimize the number of entries and accelerate the lookup process. On the other hand, attacks on Internet have reached a high level. The number keeps increasing, which increases the size of blacklists and the number of rules in firewalls. The limited storage capacity requires efficient management of that space. In the first part of this thesis, our primary goal is to find a simple representation of filtering rules that enables more compact rule tables and thus is easier to manage while keeping their semantics unchanged. The construction of rules should be obtained with reasonably efficient algorithms too. This new representation can add flexibility and efficiency in deploying security policies since the generated rules are easier to manage. A complementary approach to rule compression would be to use multiple smaller switch tables to enforce access-control policies in the network. However, most of them have a significant rules replication, or even they modify the packet's header to avoid matching a rule by a packet in the next switch. The second part of this thesis introduces new techniques to decompose and distribute filtering rule sets over a given network topology. We also introduce an update strategy to handle the changes in network policy and topology. In addition, we also exploit the structure of a series-parallel graph to efficiently resolve the rule placement problem for all-sized networks intractable time.Au sein des réseaux définis par logiciel (SDN), les exigences de filtrage pour les applications critiques varient souvent en fonction des changements de flux et des politiques de sécurité. SDN résout ce problème avec une abstraction logicielle flexible, permettant la modification et la mise en \oe{}uvre simultanées et pratiques d'une politique réseau sur les routeurs.Avec l'augmentation du nombre de règles de filtrage et la taille des données qui traversent le réseau chaque seconde, il est crucial de minimiser le nombre d'entrées et d'accélérer le processus de recherche. D'autre part, l'accroissement du nombre d'attaques sur Internet s'accompagne d'une augmentation de la taille des listes noires et du nombre de règles des pare-feux. Leur capacité de stockage limitée nécessite une gestion efficace de l'espace. Dans la première partie de cette thèse, nous proposons une représentation compacte des règles de filtrage tout en préservant leur sémantique. La construction de cette représentation est obtenue par des algorithmes raisonnablement efficaces. Cette technique permet flexibilité et efficacité dans le déploiement des politiques de sécurité puisque les règles engendrées sont plus faciles à gérer.Des approches complémentaires à la compression de règles consistent à décomposer et répartir les tables de règles, pour implémenter, par exemple, des politiques de contrôle d'accès distribué.Cependant, la plupart d'entre elles nécessitent une réplication importante de règles, voire la modification des en-têtes de paquets. La deuxième partie de cette thèse présente de nouvelles techniques pour décomposer et distribuer des ensembles de règles de filtrage sur une topologie de réseau donnée. Nous introduisons également une stratégie de mise à jour pour gérer les changements de politique et de topologie du réseau. De plus, nous exploitons également la structure de graphe série-parallèle pour résoudre efficacement le problème de placement de règles

Efficient algorithms and abstract data types for local inconsistency isolation in firewall ACLS

Writing and managing firewall ACLs are hard, tedious, time-consuming and error-prone tasks for a wide range of reasons. During these tasks, inconsistent rules can be introduced. An inconsistent firewall ACL implies in general a design fault, and indicates that the firewall is accepting traffic that should be denied or vice versa. This can result in severe problems such as unwanted accesses to services, denial of service, overflows, etc. However, the administrator is who ultimately decides if an inconsistent rule is a fault or not. Although many algorithms to detect and manage inconsistencies in firewall ACLs have been proposed, they have different drawbacks regarding different aspects of the consistency diagnosis problem, which can prevent their use in a wide range of real-life situations. In this paper, we review these algorithms along with their drawbacks, and propose a new divide and conquer based algorithm, which uses specialized abstract data types. The proposed algorithm returns consistency results over the original ACL. Its computational complexity is better than the current best algorithm for inconsistency isolation, as experimental results will also show.Ministerio de Educación y Ciencia DIP2006-15476-C02-0

Models, Algorithms, and Architectures for Scalable Packet Classification

The growth and diversification of the Internet imposes increasing demands on the performance and functionality of network infrastructure. Routers, the devices responsible for the switch-ing and directing of traffic in the Internet, are being called upon to not only handle increased volumes of traffic at higher speeds, but also impose tighter security policies and provide support for a richer set of network services. This dissertation addresses the searching tasks performed by Internet routers in order to forward packets and apply network services to packets belonging to defined traffic flows. As these searching tasks must be performed for each packet traversing the router, the speed and scalability of the solutions to the route lookup and packet classification problems largely determine the realizable performance of the router, and hence the Internet as a whole. Despite the energetic attention of the academic and corporate research communities, there remains a need for search engines that scale to support faster communication links, larger route tables and filter sets and increasingly complex filters. The major contributions of this work include the design and analysis of a scalable hardware implementation of a Longest Prefix Matching (LPM) search engine for route lookup, a survey and taxonomy of packet classification techniques, a thorough analysis of packet classification filter sets, the design and analysis of a suite of performance evaluation tools for packet classification algorithms and devices, and a new packet classification algorithm that scales to support high-speed links and large filter sets classifying on additional packet fields

MINNIE: an SDN World with Few Compressed Forwarding Rules

Software Defined Networking (SDN) is gaining momentum with the support of major manufacturers. While it brings flexibility in the management of flows within the data center fabric, this flexibility comes at the cost of smaller routing table capacities. Indeed, the Ternary Content Addressable Memory (TCAM) needed by SDN devices has smaller capacities than CAMs used in legacy hardware. In this paper, we investigate compression techniques to maximize the utility of SDN switches forwarding tables. We validate our algorithm, called \algo, with intensive simulations for well-known data center topologies, to study its efficiency and compression ratio for a large number of forwarding rules. Our results indicate that \algo scales well, being able to deal with around a million of different flows with less than 1000 forwarding entry per SDN switch, requiring negligible computation time. To assess the operational viability of MINNIE in real networks, we deployed a testbed able to emulate a k=4 fat-tree data center topology. We demonstrate on one hand, that even with a small number of clients, the limit in terms of number of rules is reached if no compression is performed, increasing the delay of new incoming flows. MINNIE, on the other hand, reduces drastically the number of rules that need to be stored, with no packet losses, nor detectable extra delays if routing lookups are done in ASICs.Hence, both simulations and experimental results suggest that \algo can be safely deployed in real networks, providing compression ratios between 70% and 99%

Theory and practice of firewall outsourcing

A firewall system is a packet filter that is placed at the entry point of an enterprise network in the Internet. Packets that attempt to enter the enterprise network through this entry point are examined, one by one, against the rules of some underlying firewall F of the firewall system. Each rule in F has a decision which is either “accept” or “reject”. For any incoming packet p, the firewall system identifies the first rule (in the sequence of rules in F) that matches p. If the decision of this rule is “accept”, then the firewall system forwards p to the enterprise network. Otherwise the decision of this rule is “reject” and packet p is discarded and prevented from entering the network. Each firewall system consists of two units: a rule matching unit and a decision unit. Both units are usually executed in the firewall system. To simplify the task of managing the firewall system, we identify a special class of firewall systems, called the outsourced system, where the rule matching unit is executed in a public cloud. Unfortunately, public clouds are usually unreliable and execution of the rule matching unit in a public cloud can be vulnerable to two types of attacks: verifiability attacks and privacy attacks. The main objective of this dissertation is to discuss how to execute the rule matching unit of an outsourced system in a public cloud such that verifiability and privacy attacks are prevented from occurring. The main contribution of this dissertation is three-fold. First, we discuss how to design outsourced firewall system such that execution of the designed system in the public clouds prevents the occurrence of verifiability and privacy attacks. The resulting system, called the private system, make use of two public clouds. We show that this private system prevents verifiability and privacy attacks under the assumption that the two public clouds used in this system are both “sensible” and “non-colluding”. Second, we identify a special class of firewalls, called the partially specified firewall, where a firewall is called partially specified when the decisions of some of the rules in the firewall are not specified as “accept” or “reject”. We show that for every partially specified firewall PF, there is a (fully specified) firewall F such that PF and F are equivalent. We discuss how to design an outsourced system whose underlying firewall is a partially specified firewall PF such that the designed system prevents both verifiability and privacy attacks. We achieve this outsourced system by obtaining an equivalent firewall F from PF and designing a private system for F. Third, we present a generalization of firewalls called firewall expressions. A firewall expression is specified using one or more component firewalls and three firewall operators: “not”, “and”, and “or”. For example, the firewall expression (G and H) consists of two component firewalls G and H and one firewall operator “and”. This firewall expression accepts a packet p iff both firewalls G and H accept p. For any underlying firewall expression FE, we define an Expression System as a generalization of firewall systems that takes as input any packet p and determines whether the underlying firewall expression FE accepts or rejects packet p. We design an outsourced expression system for any underlying firewall expression FE. We achieve this outsourced expression system by using a private system for each component firewall of FE and combining these private systems through an overall decision unit to determine whether any packet is accepted or rejected according to the firewall expression FEComputer Science