A Context-Aware System for Ambient Assisted Living

De Paola A. et al. (2017) A Context-Aware System for Ambient Assisted Living. In: Ochoa S., Singh P., Bravo J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science, vol 10586. Springer, Cha

A Behavior-Based Intrusion Detection System Using Ensemble Learning Techniques

Intrusion Detection Systems (IDSs) play a key role in modern ICT security. Attacks detected and reported by IDSs are often analyzed by administrators who are tasked with countering the attack and minimizing its damage. Consequently, it is important that the alerts generated by the IDS are as detailed as possible. In this paper, we present a multi-layered behavior-based IDS using ensemble learning techniques for the classification of network attacks. Three widely adopted and appreciated models, i.e., Decision Trees, Random Forests, and Artificial Neural Networks, have been chosen to build the ensemble. To reduce the system response time, our solution is designed to immediately filter out traffic detected as benign without further analysis, while suspicious events are investigated to achieve a more fine-grained classification. Experimental evaluation performed on the CIC-IDS2017 public dataset shows that the system is able to detect nine categories of attacks with high performances, according to all the considered metrics

A review of abnormal behavior detection in activities of daily living

Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend