Many intrusion detection and prevention systems (IDPS) have been introduced to identify suspicious activities. However, since attackers are exploiting new vulnerabilities in systems and are employing more sophisticated advanced cyber-attacks, these zero-day attacks remain hidden from IDPS in most cases. These features have incentivized many researchers to propose different artificial intelligence-based techniques to prevent, detect, and respond to such advanced attacks. This has also created a new requirement for a comprehensive comparison of the existing schemes in several aspects ; after a thorough study we found that there currently exists no detailed comparative analysis of artificial intelligence-based techniques published in the last five years. Therefore, there is a need for this kind of work to be published, as there are many comparative analyses in other fields of cyber security that are available for readers to review.In this paper, we provide a comprehensive review of the latest and most recent literature, which introduces well-known machine learning and deep learning algorithms and the challenges they face in detecting zero-day attacks. Following these qualitative analyses, we present the comparative evaluation results regarding the highest accuracy, precision, recall, and F1 score compared to different datasets.

Sentiment analysis (SA) is used to extract opinions from a huge amount of data and these opinions are comprised of multiple words. Some words have different semantic meanings in different fields and we call them domain specific (DS) words. A domain is defined as a special area in which a collection of queries about a specific topic are held when user do queries in the data regarding the domain appear. But Single word can be interpreted in many ways based on its context-dependency. Demonstrate each word under its domain is extremely important because their meanings differ from each other so much in different domains that a word meaning from A in one context can change into Z in another context or domain. The purpose of this research is to discover the correct sentiment in the message or comment and evaluate it either it is positive, negative or neutral. We collected tweets dataset from different domains and analyze it to extract words that have a different definition in those specific domains as if they are used in other fields of life they would be defined differently. We analyzed 52115 words for finding their DS meaning in seven different domains. Polarity had been given to words of the dataset according to their domains and based on this polarity they have been recognized as positive negative and neutral and evaluated as domain-specific words. The automatic way is used to extract the words of the domain as we integrated and afterward the comparison to identify that either this word differs from other words as far as domain is concerned. This research contribution is a prototype that processes your data and extracts their domain-specific words automatically. This research improved the knowledge about the context-dependency and found the core-specific meanings of words in multiple fields.

A throughput is a measure of successful data transmission through a terminal in a given time. There is a significant number of surveys that have been published to measure the performance of MAC protocols and each MAC channel. There is a substantial need to perform a comparative analysis of achieved results that can provide a comprehensible result for the end-users. In our work, we have applied different methods to obtain the throughput of contention-free and contention-based protocols. We have provided a brief comparison between different protocols of the same class to accentuate the efficient protocols. Our paper incorporates collision avoidance MAC channels, as the collision avoidance techniques provide better usability of protocols than the collision detection techniques. We have also proposed an efficient way to increase the life span of the Low-energy Adaptive Clustering Hierarchy (LEACH) protocol using Time Division Multiple Access (TDMA). The goal to obtain the maximum effectiveness of a network can be achieved by maximal utilization of the throughput. In this paper, we have provided a brief comparison of different MAC protocols by using the throughput parameter, and we have also proposed a new method to increase the efficiency of LEACH protocol.

As more and more people are adopting internet services; the measure of cybersecurity issues is also increasing exponentially. Zero-day attacks (unknown attacks) are affecting the organizations badly even the large-scale organizations had become the victim of zero-days. Although there are many intrusion detection systems (IDS) and intrusion prevention systems (IPS) that are being used but still most of the zero-days remain invisible from these IDS. It is because they use new vulnerabilities in the system and previously no signature is found for those specific vulnerabilities, causing them to be misclassified by the IDS. This paper aims to discuss the challenges that Machine Learning (ML) and Deep Learning (DL) algorithms faced in protecting cyberspace by presenting literature on the detection of zero-days. The latest and up-to-date literature was also presented which can help readers to get the latest insights into algorithms and models. Finally, we concluded the results in terms of the highest accuracy, precision, recall, and F1-Score of the comparative research articles against various datasets.