Presentation is loading. Please wait.

Presentation is loading. Please wait.

Using Cell Processors for Intrusion Detection through Regular Expression Matching with Speculation Author: C˘at˘alin Radu, C˘at˘alin Leordeanu, Valentin.

Similar presentations

Presentation on theme: "Using Cell Processors for Intrusion Detection through Regular Expression Matching with Speculation Author: C˘at˘alin Radu, C˘at˘alin Leordeanu, Valentin."— Presentation transcript:

1 Using Cell Processors for Intrusion Detection through Regular Expression Matching with Speculation Author: C˘at˘alin Radu, C˘at˘alin Leordeanu, Valentin Cristea Publisher: 2011 International Conference on Complex, Intelligent, and Software Intensive Systems Presenter: Ye-Zhi Chen Date: 2011/9/7 1

2 Introduction Main purpose :determine whether incoming network traffic matches known attack signatures Bottleneck : existing signature matching algorithms can scan only one byte at a time Intrusion Detection System (IDS) : an effective way to provide a degree of security to computers connected to network based on string matching. An Internet worm in an incoming network packet is usually identified by a string representing the executable program’s name in the packet payload 2

3 Introduction Hardware based solutions : FPGA implement specific string matching algorithms, making use of the high parallelism available Ex : Bloom filters, DFA Run an adapted Speculative Parallel Pattern Matching(SPPM) Algorithm on IBM Cell Broadband Engine (Cell BE) 3

4 Intrusion Detection System Three methodologies :signature-based 、 anomaly-based 、 stateful protocol analysis A. DFA matching : 1. Most signature databases contain several regular expressions which can be combined together into a single large DFA 2. DFAs for distinct signatures are combined into a single DFA that simultaneously represents all the signatures 3. A DFA is a quintuple (Σ; S; s 0 ; δ ; F) : Σ is the input alphabet ; S is a finite set of states; s 0 is the initial state belonging to S ; δ is the transition function ; F is the set of final or accepting states. If an accepting state has been reached then an attack signature has been found. 4

5 Intrusion Detection System 5 01 S1S1 S2S1 S2S2 S2

6 Intrusion Detection System 6 In the algorithm, the memory access to read the value at for a certain input character for a certain current state would take several processor cycles In the worst case, when the entire input string is scanned, the performance of the serial algorithm is at least M * | I | cycles, where| I | is the length of the input string and M is the number of processor cycles needed to read an input character multi-byte matching methods : In the ideal case, consuming B bytes of the input string at a time can result in a performance of M * | I | / B

7 Intrusion Detection System 7 B. Regular Expression Matching with Speculation 1. The main idea behind SPPM is to divide the input string into several chunks of the same size and process them in parallel 2. Initialization stage : the input string is split into two chunks and the state variables for the Primary and Secondary threads are initialized. 3. Parallel processing stage : they scan their private chunks in lockstep. If a match is found by either one of them then the algorithm terminates 4. Validation stage : the Primary continues to scan the Secondary’s chunk

8 Intrusion Detection System 8 Three possible outcomes arise: 1. A match is found and the algorithm returns success 2. Coupling occurs before the end of the second chunk 3. The entire second chunk is traversed again and no match is found Found at Parallel processing stage Found at Validation processing stage Not Found

9 Intrusion Detection System 9 This paper adapted the SPPM algorithm to make use of parallel hardware, using all the processing units available. The most favorable case : speedup factor would be K, which K is total number of processing units (in parallel stage) If a match is not found in the parallel processing stage, then a possible speedup gain could occur in the validation stage if the coupling between two right neighbors occurs. The least favorable case : when a match is not found and the entire input buffer is scanned, the complexity of the SPPM algorithm is the same with the one of the serial algorithm.

10 Intrusion Detection System 10

11 Cell Intrusion Detection 11 Cell processor can be split into four components: 1. External input and output structures 2. Power Processing Element (PPE) : main processor 3. Synergistic Processing Elements (SPEs) : Eight coprocessors 4. Element Interconnect Bus (EIB) :A specialized high bandwidth circular data bus connecting the PPE, input / output elements and the SPEs

12 Cell Intrusion Detection 12 PPE : A 64 bit PowerPC architecture based microprocessor It runs at a clock speed of 3.2 GHz. Running the O.S and coordinating the SPEs It has 32KB L1 cache 512KB L2 cache

13 Cell Intrusion Detection 13 SPE : Each SPE contains a Synergistic Processing Unit (SPU), memory flow controller, a memory management unit, a bus interface and an atomic unit RISC processor Each SPE has 128 128-bits registers Support for Single Instruction Multiple Data (SIMD) instructions Suitable for efficient loop unrolling and instruction scheduling. Each SPE has 256 KB of local store memory (LS), which the SPU can access it directly Use DMA transfers, because SPEs can’t access directly the main memory of the PPE.

14 Cell Intrusion Detection 14 Three different programs to perform DFA matching : 1. single-threaded DFA 2. Using the speculative parallel pattern matching solution (2 SPEs) 3. Using the speculative parallel pattern matching solution(8 SPEs)

15 Cell Intrusion Detection 15

16 Cell Intrusion Detection 16 Implement Step 1 :Scan and Parse the input file and then bring the DFA Step 2 :Divide input string into several chunks of a specified length by an input string divider Step 3 :These chunks are then matched through the DFA

17 Cell Intrusion Detection 17 if the state is an accepting one, that fact is shown by the presence of the string a() after the state number

18 Cell Intrusion Detection 18 The parser uses three buffers to scan and parse the input file : The first one is used to store an entire line from the file. The second buffer is used to hold the state transition part of the line read The third buffer is used to hold each element of this state transition array and we store this value in the corresponding position in the DFA data structure.

19 Cell Intrusion Detection 19 DFA data structure : four main fields : 1. States 2. Final : an array of STATES_NO rows and SYMBOLS_NO_MIN columns 3. Start : starting state of DFA 4. STATES_NO : total number of states Additional field dummy :Because the DFA has a size greater than one maximum DMA transfer (16KB), we choose this field to have the remaining number of bytes to make the entire size of the structure multiple of 16KB

20 Cell Intrusion Detection 20 DFA matching for 2 Cell SPUs : 1. PPU waits for strings to process and divides them into two chunks 2. PPU passes the two chunks to the two SPUs(called Primary and Secondary) 3. SPUs run DFA matching algorithm and return the results to PPU. 4. Based on the result, PPU decides whether the Primary SPU should begin the validation stage. Parallel approach for 8 processing units : 1. divide the eight SPUs into four pairs of two which run the two-threaded speculative algorithm 2. Do the same thing described above

21 Cell Intrusion Detection 21 A DFA with more than 1500 states won’t fit into the local store of the SPUs Solution for large DFAs : 1. Made several input files containing smaller DFAs (550 states is sufficient) 2. By combining together these smaller DFAs, we obtain the large DFA 3. Used the double-buffering technique which consists in issuing a DMA transfer and not waiting for its completion

22 Result 22

23 Result 23

Download ppt "Using Cell Processors for Intrusion Detection through Regular Expression Matching with Speculation Author: C˘at˘alin Radu, C˘at˘alin Leordeanu, Valentin."

Similar presentations

Ads by Google