keyhog-scanner 0.5.37

keyhog-scanner: high-performance SIMD-accelerated secret detection engine
Documentation 
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//! Neon optimized Shannon entropy and high-entropy heuristic checks for aarch64.

#[cfg(target_arch = "aarch64")]
use core::arch::aarch64::*;

use crate::entropy_fast::{get_log2_table, shannon_entropy_scalar};

/// AArch64 true Neon SIMD parallel histogram calculations.
/// Unrolled to process 32 bytes per iteration with vector null filtering (KH-25, KH-34).
#[cfg(target_arch = "aarch64")]
pub fn shannon_entropy_neon(data: &[u8]) -> f64 {
    let len = data.len();
    if len == 0 {
        return 0.0;
    }

    let mut c0 = [0u32; 256];
    let mut c1 = [0u32; 256];
    let mut c2 = [0u32; 256];
    let mut c3 = [0u32; 256];

    let ptr = data.as_ptr();
    let mut i = 0usize;
    let mut active_len = len;

    // Process 32 bytes per iteration with vector null filtering (KH-34)
    let end32 = len & !31;
    unsafe {
        while i < end32 {
            let v0 = vld1q_u8(ptr.add(i));
            let v1 = vld1q_u8(ptr.add(i + 16));

            // Fast null check using Neon ORR and MAX reduction
            if vmaxvq_u8(vorrq_u8(v0, v1)) == 0 {
                active_len -= 32;
                i += 32;
                continue;
            }

            c0[*ptr.add(i) as usize] += 1;
            c1[*ptr.add(i + 1) as usize] += 1;
            c2[*ptr.add(i + 2) as usize] += 1;
            c3[*ptr.add(i + 3) as usize] += 1;
            c0[*ptr.add(i + 4) as usize] += 1;
            c1[*ptr.add(i + 5) as usize] += 1;
            c2[*ptr.add(i + 6) as usize] += 1;
            c3[*ptr.add(i + 7) as usize] += 1;
            c0[*ptr.add(i + 8) as usize] += 1;
            c1[*ptr.add(i + 9) as usize] += 1;
            c2[*ptr.add(i + 10) as usize] += 1;
            c3[*ptr.add(i + 11) as usize] += 1;
            c0[*ptr.add(i + 12) as usize] += 1;
            c1[*ptr.add(i + 13) as usize] += 1;
            c2[*ptr.add(i + 14) as usize] += 1;
            c3[*ptr.add(i + 15) as usize] += 1;

            c0[*ptr.add(i + 16) as usize] += 1;
            c1[*ptr.add(i + 17) as usize] += 1;
            c2[*ptr.add(i + 18) as usize] += 1;
            c3[*ptr.add(i + 19) as usize] += 1;
            c0[*ptr.add(i + 20) as usize] += 1;
            c1[*ptr.add(i + 21) as usize] += 1;
            c2[*ptr.add(i + 22) as usize] += 1;
            c3[*ptr.add(i + 23) as usize] += 1;
            c0[*ptr.add(i + 24) as usize] += 1;
            c1[*ptr.add(i + 25) as usize] += 1;
            c2[*ptr.add(i + 26) as usize] += 1;
            c3[*ptr.add(i + 27) as usize] += 1;
            c0[*ptr.add(i + 28) as usize] += 1;
            c1[*ptr.add(i + 29) as usize] += 1;
            c2[*ptr.add(i + 30) as usize] += 1;
            c3[*ptr.add(i + 31) as usize] += 1;
            i += 32;
        }

        while i < len {
            let val = *ptr.add(i);
            if val == 0 {
                active_len -= 1;
            } else {
                c0[val as usize] += 1;
            }
            i += 1;
        }
    }

    if active_len == 0 {
        return 0.0;
    }

    // Merge the 4 histograms using Neon vector additions (KH-25)
    let mut counts = [0u32; 256];
    let mut j = 0;
    unsafe {
        while j < 256 {
            let v0 = vld1q_u32(c0[j..].as_ptr());
            let v1 = vld1q_u32(c1[j..].as_ptr());
            let v2 = vld1q_u32(c2[j..].as_ptr());
            let v3 = vld1q_u32(c3[j..].as_ptr());
            let sum01 = vaddq_u32(v0, v1);
            let sum23 = vaddq_u32(v2, v3);
            let sum = vaddq_u32(sum01, sum23);
            vst1q_u32(counts[j..].as_mut_ptr(), sum);
            j += 4;
        }
    }

    // Log2 Table Lookup optimization for small active length (KH-28)
    if active_len <= 255 {
        let table = get_log2_table();
        let mut sum = 0.0;
        for &count in &counts {
            if count > 0 {
                sum += table[count as usize];
            }
        }
        return (active_len as f64).log2() - sum / (active_len as f64);
    }

    let len_f = active_len as f64;
    let mut entropy = 0.0;
    for &count in &counts {
        if count > 0 {
            let p = count as f64 / len_f;
            entropy -= p * p.log2();
        }
    }

    entropy
}

/// Vectorized unique character checks using Neon (KH-21, KH-30)
#[cfg(target_arch = "aarch64")]
#[allow(unsafe_op_in_unsafe_fn)]
pub(crate) unsafe fn has_high_entropy_fast_neon(data: &[u8], threshold: f64) -> bool {
    let len = data.len();
    if len < 16 {
        return shannon_entropy_scalar(data) >= threshold;
    }

    let mid = len / 2;
    let ptr = data.as_ptr().add(mid.saturating_sub(8));
    let v = vld1q_u8(ptr);

    // Vectorized min/max to compute spread
    let mut min_v = v;
    let mut max_v = v;

    let shuf1 = vextq_u8(min_v, min_v, 8);
    min_v = vminq_u8(min_v, shuf1);
    let shuf1_max = vextq_u8(max_v, max_v, 8);
    max_v = vmaxq_u8(max_v, shuf1_max);

    let shuf2 = vextq_u8(min_v, min_v, 4);
    min_v = vminq_u8(min_v, shuf2);
    let shuf2_max = vextq_u8(max_v, max_v, 4);
    max_v = vmaxq_u8(max_v, shuf2_max);

    let shuf3 = vextq_u8(min_v, min_v, 2);
    min_v = vminq_u8(min_v, shuf3);
    let shuf3_max = vextq_u8(max_v, max_v, 2);
    max_v = vmaxq_u8(max_v, shuf3_max);

    let shuf4 = vextq_u8(min_v, min_v, 1);
    min_v = vminq_u8(min_v, shuf4);
    let shuf4_max = vextq_u8(max_v, max_v, 1);
    max_v = vmaxq_u8(max_v, shuf4_max);

    let sample_min = vgetq_lane_u8(min_v, 0);
    let sample_max = vgetq_lane_u8(max_v, 0);
    let spread = sample_max.saturating_sub(sample_min);

    // Vectorized unique character check using shifts and standard mask tricks
    let mut dup_mask = 0u32;
    let shift_mask = vld1q_u8([1, 2, 4, 8, 16, 32, 64, 128, 1, 2, 4, 8, 16, 32, 64, 128].as_ptr());
    let zero = vdupq_n_u8(0);

    for shift in 1..16 {
        let rotated = match shift {
            1 => vextq_u8(v, zero, 1),
            2 => vextq_u8(v, zero, 2),
            3 => vextq_u8(v, zero, 3),
            4 => vextq_u8(v, zero, 4),
            5 => vextq_u8(v, zero, 5),
            6 => vextq_u8(v, zero, 6),
            7 => vextq_u8(v, zero, 7),
            8 => vextq_u8(v, zero, 8),
            9 => vextq_u8(v, zero, 9),
            10 => vextq_u8(v, zero, 10),
            11 => vextq_u8(v, zero, 11),
            12 => vextq_u8(v, zero, 12),
            13 => vextq_u8(v, zero, 13),
            14 => vextq_u8(v, zero, 14),
            15 => vextq_u8(v, zero, 15),
            _ => zero,
        };
        let cmp = vceqq_u8(v, rotated);
        let and_mask = vandq_u8(cmp, shift_mask);
        let sums = vpaddq_u8(vpaddq_u8(vpaddq_u8(and_mask, and_mask), and_mask), and_mask);
        let low = vgetq_lane_u8(sums, 0) as u32;
        let high = vgetq_lane_u8(sums, 8) as u32;
        let mask = low | (high << 8);

        let valid_mask = (1u32 << (16 - shift)) - 1;
        dup_mask |= mask & valid_mask;
    }
    let unique = 16 - dup_mask.count_ones();

    // Early exit (KH-26)
    if (unique < 4 && threshold >= 1.585) || (unique < 4 && spread < 16 && threshold >= 2.0) {
        return false;
    }

    crate::entropy_fast::shannon_entropy_simd(data) >= threshold
}