No abstract

Security is one of the ever-rising provinces in about every field of society and computers are no freak. The system on the network can be attacked if it is easy to break its security or it is vulnerable. Security issues that exist in connection to a machine on network are system security and application security. For ensuring security of personal computer regular security audits of the system needs to be done. One main objective of auditing is to ensure that systems are safe or not. Digital auditing can be manual or automated. Systems audit leads to check that the vulnerability of system to different attacks that can be done on it. Similarly, a website running on the system can also be exploited for any vulnerability in it. This work investigates the methods of system and application auditing to identify the weakness at system and application level.

No abstract

A large number of online public domain comments are usually constructive, but a significant proportion is toxic. The comments include several errors that allow the machine-learning algorithm to train the data set by processing dataset with numerous variety of tasks, in the method of conversion of raw comments previously feeding it to Classification models using a ML method. In this study, we have proposed classification of toxic comments using a ML approach on a multilinguistic toxic comment dataset. The logistic regression method is applied to classify processed dataset, which will distinguish toxic comments from non-toxic comments. The multi-headed model comprises toxicity (obscene, insult, severe toxic, threat, & identity-hate) or Nontoxicity Estimation. We have implemented four models (LSTM, GRU RNN, and BiLSTM) and detected the toxic comments. In Python 3, all models have a simple structure that can adapt to the resolution of other tasks. The classification problem resolution findings are presented with the aid of the proposed models. It has been concluded that all models solve the challenge effectively, but the BiLSTM is the most effective to ensure the best practicable accuracy.