Abstract-Topic

Abstract-Mobile devices have evolved from simple devices, which are used for a phone call and SMS messages to smartphone d e vi c e s that can run third party applications. Nowadays, mal i c i ous software, which is also known as malware, imposes a larger threat to these mobile devices. Recently, many news items were posted about the increase of the Android malw are. There were a lot of Android applications pulled from the Android Market because they contained malw are. The vulnerabilities of those Applications or Android operating systems are being exploited by the attackers who got the capability of penetrating into the mobile systems without user authorization causing compromise the confidentiality, integrity and availability of the applications and the user. This paper, it gave an update to the work done in the project.Moreover, this paper focuses on the Android Operating System and aim to detect existing Android malware. It has a dataset that contained 104 malware samples. This Paper chooses several malware from the dataset and attempting to analyze them to understand their installation methods and activation. In addition, it evaluates the most popular existing anti-virus software to see if these 104 malware could be detected.

Emergency events arise when a serious, unexpected, and often dangerous threat affects normal life. Hence, knowing what is occurring during and after emergency events is critical to mitigate the effect of the incident on humans’ life, on the environment and our infrastructures, as well as the inherent financial consequences. Social network utilization in emergency event detection models can play an important role as information is shared and users’ status is updated once an emergency event occurs. Besides, big data proved its significance as a tool to assist and alleviate emergency events by processing an enormous amount of data over a short time interval. This paper shows that it is necessary to have an appropriate emergency event detection ensemble model (EEDEM) to respond quickly once such unfortunate events occur. Furthermore, it integrates Snapchat maps to propose a novel method to pinpoint the exact location of an emergency event. Moreover, merging social networks and big data can accelerate the emergency event detection system: social network data, such as those from Twitter and Snapchat, allow us to manage, monitor, analyze and detect emergency events. The main objective of this paper is to propose a novel and efficient big data-based EEDEM to pinpoint the exact location of emergency events by employing the collected data from social networks, such as “Twitter” and “Snapchat”, while integrating big data (BD) and machine learning (ML). Furthermore, this paper evaluates the performance of five ML base models and the proposed ensemble approach to detect emergency events. Results show that the proposed ensemble approach achieved a very high accuracy of 99.87% which outperform the other base models. Moreover, the proposed base models yields a high level of accuracy: 99.72%, 99.70% for LSTM and decision tree, respectively, with an acceptable training time.

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