mirror of
https://github.com/vel21ripn/nDPI.git
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129 lines
4 KiB
C++
129 lines
4 KiB
C++
#include "ndpi_api.h"
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#include "fuzz_common_code.h"
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#include <stdint.h>
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#include <stdio.h>
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#include "fuzzer/FuzzedDataProvider.h"
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extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {
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FuzzedDataProvider fuzzed_data(data, size);
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u_int16_t i, num_values, num_learning_values, max_series_len;
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struct ndpi_hw_struct hw;
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struct ndpi_rsi_struct rsi;
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struct ndpi_analyze_struct *a;
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int rc_hw, rc_rsi;
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u_int16_t num_periods;
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u_int8_t additive_seeasonal;
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double alpha, beta, gamma, forecast, confidence_band;
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float significance, lower, upper, threshold;
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u_int32_t *values, *values2, predict_periods;
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u_int32_t prediction;
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u_int32_t value_to_check;
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bool *outliers;
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/* Use the same (integral) dataset to peform: RSI, Data analysis, HW, outliers,
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linear regression, Pearson correlation */
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/* Just to have some data */
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if(fuzzed_data.remaining_bytes() < 1024)
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return -1;
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/* To allow memory allocation failures */
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fuzz_set_alloc_callbacks_and_seed(size);
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/* Data set */
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num_values = fuzzed_data.ConsumeIntegral<u_int8_t>();
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values = (u_int32_t *)ndpi_malloc(sizeof(u_int32_t) * num_values);
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values2 = (u_int32_t *)ndpi_malloc(sizeof(u_int32_t) * num_values);
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outliers = (bool *)ndpi_malloc(sizeof(bool) * num_values);
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if (!values || !values2 || !outliers) {
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ndpi_free(values);
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ndpi_free(values2);
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ndpi_free(outliers);
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return -1;
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}
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for (i = 0; i < num_values; i++) {
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values[i] = fuzzed_data.ConsumeIntegral<u_int32_t>();
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values2[i] = fuzzed_data.ConsumeIntegral<u_int32_t>();
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}
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/* Init HW */
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num_periods = fuzzed_data.ConsumeIntegral<u_int16_t>();
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additive_seeasonal = fuzzed_data.ConsumeBool();
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alpha = fuzzed_data.ConsumeFloatingPointInRange<double>(0, 1);
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beta = fuzzed_data.ConsumeFloatingPointInRange<double>(0, 1);
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gamma = fuzzed_data.ConsumeFloatingPointInRange<double>(0, 1);
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significance = fuzzed_data.ConsumeFloatingPointInRange<float>(0, 1.1);
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rc_hw = ndpi_hw_init(&hw, num_periods, additive_seeasonal,
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alpha, beta, gamma, significance);
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/* Init RSI */
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num_learning_values = fuzzed_data.ConsumeIntegral<u_int8_t>();
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rc_rsi = ndpi_alloc_rsi(&rsi, num_learning_values);
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/* Init Data Analysis */
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max_series_len = fuzzed_data.ConsumeIntegral<u_int16_t>();
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if(fuzzed_data.ConsumeBool()) {
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a = ndpi_alloc_data_analysis(max_series_len);
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for (i = 0; i < num_values; i++) {
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ndpi_data_add_value(a, values[i]);
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}
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} else {
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a = ndpi_alloc_data_analysis_from_series(values, num_values);
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}
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/* Init Linear Regression */
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predict_periods = fuzzed_data.ConsumeIntegral<u_int8_t>();
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/* Calculate! */
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for (i = 0; i < num_values; i++) {
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if (rc_hw == 0)
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ndpi_hw_add_value(&hw, values[i], &forecast, &confidence_band);
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if (rc_rsi == 0)
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ndpi_rsi_add_value(&rsi, values[i]);
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}
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ndpi_find_outliers(values, outliers, num_values);
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ndpi_predict_linear(values, num_values, predict_periods, &prediction);
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/* Data analysis stuff */
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ndpi_data_print_window_values(a);
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ndpi_data_average(a);
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ndpi_data_mean(a);
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ndpi_data_variance(a);
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ndpi_data_stddev(a);
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ndpi_data_min(a);
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ndpi_data_max(a);
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ndpi_data_jitter(a);
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ndpi_data_last(a);
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ndpi_data_window_average(a);
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ndpi_data_window_variance(a);
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ndpi_data_window_stddev(a);
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ndpi_data_entropy(a);
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ndpi_reset_data_analysis(a);
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ndpi_hw_reset(&hw);
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/* Data ratio */
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if (num_values > 1)
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ndpi_data_ratio2str(ndpi_data_ratio(values[0], values[1]));
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/* Outlier */
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value_to_check = fuzzed_data.ConsumeIntegral<u_int32_t>();
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threshold = fuzzed_data.ConsumeFloatingPointInRange<float>(0, 2^32 - 1);
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ndpi_is_outlier(values, num_values, value_to_check, threshold,
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&lower, &upper);
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/* Pearson correlation */
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ndpi_pearson_correlation(values, values2, num_values);
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/* Done. Free */
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if (rc_hw == 0)
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ndpi_hw_free(&hw);
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if (rc_rsi == 0)
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ndpi_free_rsi(&rsi);
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ndpi_free_data_analysis(a, 1);
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ndpi_free(values);
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ndpi_free(values2);
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ndpi_free(outliers);
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return 0;
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}
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