186 research outputs found

    A FRAMEWORK FOR PERFORMANCE EVALUATION OF ASIPS IN NETWORK-BASED IDS

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    ABSTRACT Nowadays efficient usage of high-tech security tools and appliances is considered as an important criterion for security improvement of computer networks. Based on this assumption, Intrusion Detection and Prevention Systems (IDPS) have key role for applying the defense in depth strategy. In this situation, by increasing network bandwidth in addition to increasing number of threats, Network-based IDPSes have been faced with performance challenge for processing of huge traffic in the networks. A general solution for this bottleneck is exploitation of efficient hardware architectures for performance improvement of IDPS. In this paper a framework for analysis and performance evaluation of application specific instruction set processors is presented for usage in application of attack detection in Networkbased Intrusion Detection Systems(NIDS). By running this framework as a security application on V85

    FPGA-based High Throughput Regular Expression Pattern Matching for Network Intrusion Detection Systems

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    Network speeds and bandwidths have improved over time. However, the frequency of network attacks and illegal accesses have also increased as the network speeds and bandwidths improved over time. Such attacks are capable of compromising the privacy and confidentiality of network resources belonging to even the most secure networks. Currently, general-purpose processor based software solutions used for detecting network attacks have become inadequate in coping with the current network speeds. Hardware-based platforms are designed to cope with the rising network speeds measured in several gigabits per seconds (Gbps). Such hardware-based platforms are capable of detecting several attacks at once, and a good candidate is the Field-programmable Gate Array (FPGA). The FPGA is a hardware platform that can be used to perform deep packet inspection of network packet contents at high speed. As such, this thesis focused on studying designs that were implemented with Field-programmable Gate Arrays (FPGAs). Furthermore, all the FPGA-based designs studied in this thesis have attempted to sustain a more steady growth in throughput and throughput efficiency. Throughput efficiency is defined as the concurrent throughput of a regular expression matching engine circuit divided by the average number of look up tables (LUTs) utilised by each state of the engine"s automata. The implemented FPGA-based design was built upon the concept of equivalence classification. The concept helped to reduce the overall table size of the inputs needed to drive the various Nondeterministic Finite Automata (NFA) matching engines. Compared with other approaches, the design sustained a throughput of up to 11.48 Gbps, and recorded an overall reduction in the number of pattern matching engines required by up to 75%. Also, the overall memory required by the design was reduced by about 90% when synthesised on the target FPGA platform

    Fast Packet Processing on High Performance Architectures

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    The rapid growth of Internet and the fast emergence of new network applications have brought great challenges and complex issues in deploying high-speed and QoS guaranteed IP network. For this reason packet classication and network intrusion detection have assumed a key role in modern communication networks in order to provide Qos and security. In this thesis we describe a number of the most advanced solutions to these tasks. We introduce NetFPGA and Network Processors as reference platforms both for the design and the implementation of the solutions and algorithms described in this thesis. The rise in links capacity reduces the time available to network devices for packet processing. For this reason, we show different solutions which, either by heuristic and randomization or by smart construction of state machine, allow IP lookup, packet classification and deep packet inspection to be fast in real devices based on high speed platforms such as NetFPGA or Network Processors

    High-Performance Packet Processing Engines Using Set-Associative Memory Architectures

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    The emergence of new optical transmission technologies has led to ultra-high Giga bits per second (Gbps) link speeds. In addition, the switch from 32-bit long IPv4 addresses to the 128-bit long IPv6 addresses is currently progressing. Both factors make it hard for new Internet routers and firewalls to keep up with wire-speed packet-processing. By packet-processing we mean three applications: packet forwarding, packet classification and deep packet inspection. In packet forwarding (PF), the router has to match the incoming packet's IP address against the forwarding table. It then directs each packet to its next hop toward its final destination. A packet classification (PC) engine examines a packet header by matching it against a database of rules, or filters, to obtain the best matching rule. Rules are associated with either an ``action'' (e.g., firewall) or a ``flow ID'' (e.g., quality of service or QoS). The last application is deep packet inspection (DPI) where the firewall has to inspect the actual packet payload for malware or network attacks. In this case, the payload is scanned against a database of rules, where each rule is either a plain text string or a regular expression. In this thesis, we introduce a family of hardware solutions that combine the above requirements. These solutions rely on a set-associative memory architecture that is called CA-RAM (Content Addressable-Random Access Memory). CA-RAM is a hardware implementation of hash tables with the property that each bucket of a hash table can be searched in one memory cycle. However, the classic hashing downsides have to be dealt with, such as collisions that lead to overflow and worst-case memory access time. The two standard solutions to the overflow problem are either to use some predefined probing (e.g., linear or quadratic) or to use multiple hash functions. We present new hash schemes that extend both aforementioned solutions to tackle the overflow problem efficiently. We show by experimenting with real IP lookup tables, synthetic packet classification rule sets and real DPI databases that our schemes outperform other previously proposed schemes

    Validation and verification of the interconnection of hardware intellectual property blocks for FPGA-based packet processing systems

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    As networks become more versatile, the computational requirement for supporting additional functionality increases. The increasing demands of these networks can be met by Field Programmable Gate Arrays (FPGA), which are an increasingly popular technology for implementing packet processing systems. The fine-grained parallelism and density of these devices can be exploited to meet the computational requirements and implement complex systems on a single chip. However, the increasing complexity of FPGA-based systems makes them susceptible to errors and difficult to test and debug. To tackle the complexity of modern designs, system-level languages have been developed to provide abstractions suited to the domain of the target system. Unfortunately, the lack of formality in these languages can give rise to errors that are not caught until late in the design cycle. This thesis presents three techniques for verifying and validating FPGA-based packet processing systems described in a system-level description language. First, a type system is applied to the system description language to detect errors before implementation. Second, system-level transaction monitoring is used to observe high-level events on-chip following implementation. Third, the high-level information embodied in the system description language is exploited to allow the system to be automatically instrumented for on-chip monitoring. This thesis demonstrates that these techniques catch errors which are undetected by traditional verification and validation tools. The locations of faults are specified and errors are caught earlier in the design flow, which saves time by reducing synthesis iterations

    Energy Efficient Hardware Accelerators for Packet Classification and String Matching

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    This thesis focuses on the design of new algorithms and energy efficient high throughput hardware accelerators that implement packet classification and fixed string matching. These computationally heavy and memory intensive tasks are used by networking equipment to inspect all packets at wire speed. The constant growth in Internet usage has made them increasingly difficult to implement at core network line speeds. Packet classification is used to sort packets into different flows by comparing their headers to a list of rules. A flow is used to decide a packet’s priority and the manner in which it is processed. Fixed string matching is used to inspect a packet’s payload to check if it contains any strings associated with known viruses, attacks or other harmful activities. The contributions of this thesis towards the area of packet classification are hardware accelerators that allow packet classification to be implemented at core network line speeds when classifying packets using rulesets containing tens of thousands of rules. The hardware accelerators use modified versions of the HyperCuts packet classification algorithm. An adaptive clocking unit is also presented that dynamically adjusts the clock speed of a packet classification hardware accelerator so that its processing capacity matches the processing needs of the network traffic. This keeps dynamic power consumption to a minimum. Contributions made towards the area of fixed string matching include a new algorithm that builds a state machine that is used to search for strings with the aid of default transition pointers. The use of default transition pointers keep memory consumption low, allowing state machines capable of searching for thousands of strings to be small enough to fit in the on-chip memory of devices such as FPGAs. A hardware accelerator is also presented that uses these state machines to search through the payloads of packets for strings at core network line speeds

    A Modular Approach to Adaptive Reactive Streaming Systems

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    The latest generations of FPGA devices offer large resource counts that provide the headroom to implement large-scale and complex systems. However, there are increasing challenges for the designer, not just because of pure size and complexity, but also in harnessing effectively the flexibility and programmability of the FPGA. A central issue is the need to integrate modules from diverse sources to promote modular design and reuse. Further, the capability to perform dynamic partial reconfiguration (DPR) of FPGA devices means that implemented systems can be made reconfigurable, allowing components to be changed during operation. However, use of DPR typically requires low-level planning of the system implementation, adding to the design challenge. This dissertation presents ReShape: a high-level approach for designing systems by interconnecting modules, which gives a ‘plug and play’ look and feel to the designer, is supported by tools that carry out implementation and verification functions, and is carried through to support system reconfiguration during operation. The emphasis is on the inter-module connections and abstracting the communication patterns that are typical between modules – for example, the streaming of data that is common in many FPGA-based systems, or the reading and writing of data to and from memory modules. ShapeUp is also presented as the static precursor to ReShape. In both, the details of wiring and signaling are hidden from view, via metadata associated with individual modules. ReShape allows system reconfiguration at the module level, by supporting type checking of replacement modules and by managing the overall system implementation, via metadata associated with its FPGA floorplan. The methodology and tools have been implemented in a prototype for a broad domain-specific setting – networking systems – and have been validated on real telecommunications design projects
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