Biblio

One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behavior. The most important component used to detect cyber attacks or malicious activities is the Intrusion Detection System (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In trendy days, artificial intelligence algorithms are rising as a brand new computing technique which will be applied to actual time issues. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defense dataset (CSE-CIC-IDS2018), the very latest Intrusion Detection Dataset created in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services). The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area under ROC (Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.001. The proposed system using artificial intelligence of botnet attack detection is powerful, more accurate and precise. The novel proposed system can be implemented in n machines to conventional network traffic analysis, cyber-physical system traffic data and also to the real-time network traffic analysis.

As a problem solving method, neural networks have shown broad applicability from medical applications, speech recognition, and natural language processing. This success has even led to implementation of neural network algorithms into hardware. In this paper, we explore two questions: (a) to what extent microelectronic variations affects the quality of results by neural networks; and (b) if the answer to first question represents an opportunity to optimize the implementation of neural network algorithms. Regarding first question, variations are now increasingly common in aggressive process nodes and typically manifest as an increased frequency of timing errors. Combating variations - due to process and/or operating conditions - usually results in increased guardbands in circuit and architectural design, thus reducing the gains from process technology advances. Given the inherent resilience of neural networks due to adaptation of their learning parameters, one would expect the quality of results produced by neural networks to be relatively insensitive to the rising timing error rates caused by increased variations. On the contrary, using two frequently used neural networks (MLP and CNN), our results show that variations can significantly affect the inference accuracy. This paper outlines our assessment methodology and use of a cross-layer evaluation approach that extracts hardware-level errors from twenty different operating conditions and then inject such errors back to the software layer in an attempt to answer the second question posed above.