Tsaro na Bayanai na Girgije na Tushen Transformer tare da Haɗin Blockchain

Teburin Abubuwan Ciki

• 1. Gabatarwa
• 2. Hanyar Bincike
  • 2.1 Tsarin Hanyar Neuronal Transformer
  • 2.2 Haɗin Blockchain don Tabbataccen Bayanai
  • 2.3 Aiwartar Tsarin Neuronal na Bincike (ANP)
• 3. Aiwartar Fasaha
  • 3.1 Tsarin Lissafi
  • 3.2 Aiwartar Lambar
• 4. Sakamakon Gwaji
  • 4.1 Ma'aunin Aiki
  • 4.2 Binciken Kwatance
• 5. Aiwatar da Gaba & Ci Gaba
• 6. Nassoshi
• 7. Bincike Mai Zurfi

1. Gabatarwa

Faɗaɗa saurin ayyukan girgije da kayan aikin dijital ya haifar da ƙalubalen tsaro da ba a taɓa ganin irinsu ba ga cibiyoyin bayanai da tsarin IoT. Ya zuwa 2025, ana hasashen yawan bayanai na duniya zai kai ZB 163, haɓaka daga ZB 16.1 a 2016, wanda ke haifar da manyan wuraren kai hari ga barazanar yanar gizo. Tasirin tattalin arziƙin katsewar cibiyar bayanai ya kai dalar Amurka 8,851 a kowace minti, yana nuna mahimmancin buƙatar ingantattun tsare-tsaren tsaro.

2. Hanyar Bincike

2.1 Tsarin Hanyar Neuronal Transformer

Tsarin da aka tsara yana amfani da Hanyoyin Neuronal Transformer (TNN) don gano hare-haren yanar gizo na ainihi a cikin yanayin girgije. Tsarin yana sarrafa bayanai masu bi da bi daga na'urori masu auna lafiya da na'urorin IoT, yana gano ƙirar mugunta kafin su isa Layer na hazo.

2.2 Haɗin Blockchain don Tabbataccen Bayanai

Fasahar Blockchain tana ba da tsarin da ba shi da cibiya ga tsarin kiwon lafiya, tana kawar da wuraren gazawa guda ɗaya. Kowane ma'amala na bayanai yana da tsaro ta hanyar sirri kuma an rubuta shi ba zai iya canzawa ba, yana hana canje-canjen da ba a ba da izini ba.

2.3 Aiwartar Tsarin Neuronal na Bincike (ANP)

ANP yana haɗa hanyoyin neuronal tare da ƙirar yuwuwar don gano ɓatattun bayanai da kuma gane ƙirar mugunta a cikin ma'aunin na'urar auna lafiya. Tsarin yana daidaitawa da ƙirar barazana masu tasowa ta hanyar ci gaba da koyo.

3. Aiwartar Fasaha

3.1 Tsarin Lissafi

Hanyar Transformer attention an ayyana ta ta:

$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$

Inda $Q$, $K$, $V$ suka wakilci matrices na tambaya, maɓalli, da ƙima, kuma $d_k$ shine girman maɓallan maɓalli.

Multi-head attention yana faɗaɗa wannan ra'ayi:

$\text{MultiHead}(Q, K, V) = \text{Concat}(\text{head}_1, ..., \text{head}_h)W^O$

inda $\text{head}_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)$

3.2 Aiwartar Lambar

import torch
import torch.nn as nn
import torch.nn.functional as F

class TransformerSecurityModel(nn.Module):
    def __init__(self, d_model=512, nhead=8, num_layers=6):
        super().__init__()
        self.encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model, nhead=nhead
        )
        self.transformer_encoder = nn.TransformerEncoder(
            self.encoder_layer, num_layers=num_layers
        )
        self.classifier = nn.Linear(d_model, 2)  # Benign vs Malicious
    
    def forward(self, x):
        x = self.transformer_encoder(x)
        x = self.classifier(x[:, -1, :])  # Use last token for classification
        return F.softmax(x, dim=-1)

# Blockchain integration pseudocode
class BlockchainSecurity:
    def validate_transaction(self, data, signature):
        if verify_signature(data, signature):
            block = create_block(data, previous_hash)
            add_to_chain(block)
            return True
        return False

4. Sakamakon Gwaji

4.1 Ma'aunin Aiki

Hanyar Neuronal Transformer ta sami nasara mai ban mamaki tare da daidaiton kashi 99.99% bisa ga ma'aunin maki F1. Tsarin ya nuna ƙarfin gano inganci a cikin nau'ikan hanyoyin kai hare-haren yanar gizo daban-daban ciki har da DDoS, allurar malware, da yunƙurin ɓarna bayanai.

4.2 Binciken Kwatance

Idan aka kwatanta da hanyoyin tsaro na gargajiya, tsarin na tushen transformer ya nuna haɓaka kashi 45% cikin saurin gano kuma an rage kashi 67% na kuskuren gano abin da ba daidai ba. Haɗin blockchain ya tabbatar da cewa babu keta bayanai a lokacin gwajin.

5. Aiwatar da Gaba & Ci Gaba

Haɗin tsaron tushen transformer tare da fasahar blockchain yana da babbar yuwuwar a cikin IoT na kiwon lafiya, tsarin kuɗi, da kare muhimman abubuwan more rayuwa. Ci gaban gaba ya haɗa da koyon haɗin gwiwa don horar da ƙirar kiyaye sirri da kuma algorithms na blockchain masu jure wa ƙididdigewa don tsaron dogon lokaci.

Muhimman wuraren ci gaba:

• Ingantaccen ƙididdigewa na gefe don sarrafa ainihi
• Haɗin kai na blockchain a tsakanin dandamali
• Raba bayanan barazana masu daidaitawa
• AI mai bayyanawa don bayyana yanke shawara na tsaro

6. Nassoshi

1. Praneetha et al. (2024). Ƙalubalen Tsaron Girgije a Canjin Dijital. Jaridar Tsaron Cibiyar Sadarwa.
2. Almalki et al. (2022). Ci Gaban Abubuwan More Rayuwa a cikin Tattalin Arzikin Dijital na Bayan COVID. IEEE Transactions akan Ƙididdigar Girgije.
3. Kumar & Sharma (2022). Blockchain don Tsarin Kiwon Lafiya: Cikakken Bita. Springer Healthcare Informatics.
4. Vaswani et al. (2017). Hankali Shine Kowa Bukatar ku. Ci gaba a cikin Tsarin Bayanai na Neuronal.
5. Zhang et al. (2022). Tasirin Tattalin Arziki na Keta Tsaron Cibiyar Bayanai. Binciken Kwatancen ACM.

7. Bincike Mai Zurfi