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- W4327499860 abstract "The development of Internet has brought a lot of conveniences to people and facilitated people's life greatly. However, with the gradual development of network, a variety of computer viruses such as Trojan virus are threating our network security and our use of accounts. Any careless move will cause the loss of the account or even our properties, which sounded the alarm on network security. In previous network security defence practices, simple firewall was used as an important defence method against virus. However, in recent virus attacks, it is found that simple firewall can no longer meet the demands of defence against computer virus. Hence, the combination of firewall and IPS is surely an important method for future network security protection. Regarding the researches on firewall and IPS interaction technology, the research levels abroad are prior to that of domestic researches. Especially, Network ICE Company possesses a world-class research level in both intrusion detection system (IDS) and intrusion prevention system (IPS). Intrusion detection is the most common way of recognizing assaults on PC frameworks and making ready for interruptions, interruptions, and other PC related takes advantage of. Be that as it may, the development of Internet-based gadgets confounds the location interaction and requires computerized frameworks to recognize assaults. In light of this, this paper proposes a programmed interruption location strategy utilizing the Brainstorm-Crow Search-based Actor Critic Neural Network (BCS-ACNN) classifier. Information grouping bunches the information utilizing the proposed scanty fluffy C-implies (Probabilistic Sparse FCM) grouping calculation. The group is exposed to a two-level characterization performed utilizing the proposed streamlining calculation, and the second degree of order identifies information interruptions. The Brain Storm-Crow Search (BCS) calculation, which is a reconciliation of Brain Storm Optimization (BSO) and Crow Search Algorithm (CSA), ideally changes the loads of the Actor Critic Neural Network. Also, the probabilistic inadequate FCM calculation incorporates likelihood hypothesis into scanty FCM. Exploring different avenues regarding the technique proposed in the KDD-Cup dataset yields a precision of 0.7682, a genuine positive pace of 0.7984 (TPR), and a bogus positive pace of 0.4580 (FPR)." @default.
- W4327499860 created "2023-03-17" @default.
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- W4327499860 date "2022-12-26" @default.
- W4327499860 modified "2023-09-27" @default.
- W4327499860 title "An Enhanced Intrusion Prevention System Using Neural Network Classifier" @default.
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- W4327499860 doi "https://doi.org/10.1109/icerect56837.2022.10060128" @default.
- W4327499860 hasPublicationYear "2022" @default.
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