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The mortality rate of cancer is among the highest in the world. One death occurs every six in the world. Both machine learning (ML) and deep learning (DL) have been used by scientists to predict cancer. In addition, DL can analyze a huge amount of healthcare data in a short period of time to study the chances of recurrence, progression and patient survival. An accurate and quick framework for improving cancer prognosis prediction is presented in this study. A fast and accurate optimizer is necessary to predict both critical and non-critical cases, so a modified binary version of the Whale Optimization Algorithm (WOA) is proposed. Based on sigmoid transfer functions, this version identifies the subset of features that is minimally optimal while maximizing classification accuracy. This framework is composed of an optimized parameter Long-Short Term Memory (LSTM) Neural Network, with the input being the optimal set of feature selection layer. The proposed framework performs better than previous frameworks having an average accuracy of 100% and an execution time of 4113 seconds.

Throughout the past few years, the Internet of Things (IoT) has grown in popularity because of its ease of use and flexibility. Cyber criminals are interested in IoT because it offers a variety of benefits for users, but it still poses many types of threats. The most common form of attack against IoT is Distributed Denial of Service (DDoS). The growth of preventive processes against DDoS attacks has prompted IoT professionals and security experts to focus on this topic. Due to the increasing prevalence of DDoS attacks, some methods for distinguishing different types of DDoS attacks based on individual network features have become hard to implement. Additionally, monitoring traffic pattern changes and detecting DDoS attacks with accuracy are urgent and necessary. In this paper, using Modified Whale Optimization Algorithm (MWOA) feature extraction and Hybrid Long Short Term Memory (LSTM), shown that DDoS attack detection methods can be developed and tested on various datasets. The MWOA technique, which is used to optimize the weights of the LSTM neural network to reduce prediction errors in the hybrid LSTM algorithm, is used. Additionally, MWOA can optimally extract IP packet features and identify DDoS attacks with the support of MWOA-LSTM model. The proposed MWOA-LSTM framework outperforms standard support vector machines (SVM) and Genetic Algorithm (GA) as well as standard methods for detecting attacks based on precision, recall and accuracy measurements.

I-INTRODUCTIONhere is no doubt that many DDoS attacks have been reported daily all over the world, these attacks aim to hinder legitimate users from accessing a corporation services or resources, resulting in a revenue loss. The attackers rely on the fact that Internet routing infrastructure is mainly concerned by scalability rather security, since routers neither validate source IP address nor log information regarding the forwarded packets [1]. DDoS attack methods could be classified according to the number of attacking packets into flooding and vulnerability attack [2, 3]. In flooding attack, which is the most common, the attacker sends a huge number

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