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首页> 外文期刊>Query: Journal of information systems >Implemetasi Jaringan Saraf Tiruan Untuk Mendeteksi Serangan DDoS Pada Forensik Jaringan
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Implemetasi Jaringan Saraf Tiruan Untuk Mendeteksi Serangan DDoS Pada Forensik Jaringan

机译:人工神经网络的实施检测网络取证的DDOS攻击

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Network attacks that are often carried out including using Distributed Denial of Service (DDoS) have caused significant financial losses and require very large recovery costs to reach double. Activities that damage, interfere with, steal data, and anything that harms the system owner of a computer network is illegal and can be legally sanctioned in court. Network forensics mechanism to find criminals in order to be ensnared by law. Investigators usually use network monitoring systems such as Intrusion Detection System (IDS) for forensics purposes. The use of IDS allows the detection of errors or changes in traffic and new types of attacks because attacks are carried out using syn packages, where the syn protocol is considered legal because it is needed in the authentication process of communication between devices in the Internet network. Signature-based detection and notification systems are also not strong enough to be used as evidence in the trial. An analysis mechanism is needed to test the accuracy of DDoS attacks that have been detected by the intrusion detection system. Testing the accuracy of DDoS attacks can be done using the neural network classification method using statistical calculations. Based on the results of the analysis and testing carried out found an accuracy value of 95.23%. These results can be used to support and strengthen the evidence of findings in the trial.
机译:通常进行的网络攻击包括使用分布式拒绝服务(DDOS)引起了重大的金融损失,并且需要非常大的恢复成本来达到双倍。损坏,干扰,窃取数据以及危害计算机网络的系统所有者的任何活动的活动是非法的,可以在法庭上法律批准。网络取证机制寻找犯罪分子,以便被法律纳入。调查人员通常使用网络监控系统,例如入侵检测系统(IDS),用于取证目的。使用ID允许检测错误或流量的变化以及新类型的攻击,因为使用SYN包执行攻击,其中SYN协议被认为是合法的,因为它需要在Internet网络中的设备之间的通信识别过程中。基于签名的检测和通知系统也不足以被用作试验中的证据。需要分析机制来测试通过入侵检测系统检测到的DDOS攻击的准确性。使用统计计算的神经网络分类方法可以测试DDOS攻击的准确性。根据分析和测试的结果,发现精度值为95.23%。这些结果可用于支持和加强试验中调查结果的证据。

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