The aviation community is employing various air traffic control and mobile communication technologies, such as ubiquitous data links, wireless communication architectures and protocols. Recently, software-defined networking (SDN) based architectures (i.e., cockpit network communications environment testing (COMET)) have been proposed for Air-Ground communication. However, an evil can break the communication between a pilot and air traffic control, resulting in a hazardous (or life-threatening) situation up in the air or failure of ground equipment. This paper proposes an efficient evil detection and prevention mechanism (called DoEF) for the COMET architecture. The proposed DoEF utilizes a deep learning-based approach, i.e., long-short term memory (LSTM), to detect the evil flies and provide possible countermeasures. Our preliminary results show that the proposed scheme reduces the detection time and increases the detection accuracy of distributed denial of service (DDoS) attacks for the aviation network.

Summary Social media plays a vital role in analyzing the actual emotions of people after and during a disaster. Sentiment analysis is a method to detect a pattern from the emotions and feedback of the user. The main objective of the proposed work is to perform sentiment analysis on the tweets on a specific disaster context for a particular location at different intervals of time. LSTM network with word embedding algorithm is used to derive keywords based on the history of tweets and the context of the tweets. The proposed algorithm risk assessment sentiment analysis (RASA) uses the keywords generated from the network to classify the tweets and sentiment score for each location is identified. The model is validated with various state‐of‐art algorithms, namely, support vector machine, Naive‐Bayes, maximum entropy, logistic regression, random forest, XGBoost, stochastic gradient descent, and convolution neural networks in 2‐fold scenario: one for binary class and the other multiclass with three target classes. The results infer that the proposed RASA performs better in a binary class scenario with an increase of 1% when compared with XGBoost and 30% in multiclass scenario on an average when compared with all the other techniques. The model helps the government to take preventive measures to manage the posteffect of the disaster event in a location.

Summary Smartphones are equipped with precise hardware sensors including accelerometer, gyroscope, and magnetometer. These devices provide real‐time semantic data that can be used to recognize daily life physical activities for personalized smart health assessment. Existing studies focus on the recognition of simple physical activities but they lacked in providing accurate recognition of physical activities having complex interclass variations. Therefore, this research focuses on the accurate recognition of physical activities having complex interclass variations. We propose a two‐layered approach calledPARCIVthat first clusters similar activities based on semantic data and then recognize them using a machine learning classifier. Our two‐layered approach first bounds the highly indistinguishable activities in clusters to avoid misclassification with other distinguishable activities and thereafter recognize them on a fine‐grained level within each cluster. To evaluate our approach, we make an android application that collects labeled data by using smartphone sensors from 10 participants, while performing activities.PARCIVrecognizes distinguishable as well as indistinguishable activities with high accuracy of 99% on the self‐collected dataset. Furthermore,PARCIVachieve 95% accuracy on the publicly available dataset used by state‐of‐the‐art studies.PARCIVoutperforms various state‐of‐the‐art studies by 8%‐17% for simple activities as well as complex activities.

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.