Intrusion Detection System for Internet of Things with Deep Learning

RÉSUMÉ: Les dernières technologies de connectivité ont entraîné un développement massive et une forte croissance de l’Internet des objets (Internet of Things : IoT), qui est un écosystème complet d’appareils interdépendants. L’IoT est utilisé dans de nombreux domaines tels que l’agriculture, l’industrie, les entreprises, la santé, le contrôle industriel, la défense et la sécurité, permettant ainsi une grande amélioration des fonctions quotidiennes. La sécurité des données est un problème important et si celles ci ne sont pas sécurisées, cela constitue une menace majeure aux réseaux IoT et leurs appareils associés. Étant donné que les appareils IoT sont généralement toujours connectés à Internet, la probabilité de sécuriser les données doit plus élevée que sur des machines autonomes. À cette fin, les appareils de l’IoT doivent utiliser des IDS avancés afin d’améliorer la sécurité des appareils. Les solutions IDS actuelles ne tiennent pas compte de l’hétérogénéité des réseaux IoT pour les différentes applications et appareils impliqués. Ainsi, il est très difficile à un unique IDS de détecter toutes les attaques existantes, car il a une connaissance limitée des schémas d’attaque et de leurs implications. De plus, l’IDS doit tenir compte de grande quantité de données générées par les réseaux IoT et nécessite d’analyser chaque paquet de trafic entrant et sortant en temps réel. Les contraintes de ressources des appareils IoT rendent aussi difficile les defenses aux cyberattaques et limitent l’utilisation de mécanismes avancés tels que les modèles d’apprentissage en profondeur à la détection des attaques. Les modèles d’apprentissage en profondeur sont difficiles à implémenter dans les IDS, car ils nécessitent beaucoup de ressources, en particulier dans des environnements IoT. ABSTRACT: The massive transformation of the latest connectivity technologies resulted in the development and growth of the concept of the Internet of Things (IoT) which is a complete ecosystem of interrelated devices. IoT has been applied in many areas such as agriculture, industry, enterprises, healthcare, industrial control, defense and security, improving day-to-day functions in these areas. One prominent issue which has lately emerged as a major threat to IoT networks by adversely affecting IoT networks and associated devices is data security. Since IoT devices are generally always connected to the internet, the probability of subverting their security over the internet is higher than normal standalone machines. For that purpose, devices in the IoT environment need to utilize advanced IDS to make it more secure. The problem with the existing IDS solutions is that they overlook the heterogeneity of IoT networks as to the various applications and devices involved. Thus, it is becoming very hard for a single IDS to detect all existing attacks due to limited knowledge about such attack patterns and implications. Moreover, applying the traditional IDS in the field of IoT is challenging because of the large amount of data generated by IoT networks and the requirement of analyzing every packet of ingress and egress traffic in real time. Additionally, the resource constraints of the IoT devices make it harder for them to show resilience against cyber attacks and limit the use of advanced mechanisms like IDS-based deep learning approaches for attack detection. Such an approach is hard to use because it is very resourcing intensive, especially when deployed in IoT environments

Evaluation and Selection Models for Ensemble Intrusion Detection Systems in IoT

Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic areas for researchers to protect network infrastructure from adversary activities. Several network systems suffer from drawbacks that allow intruders to use malicious traffic to obtain unauthorized access. Attacks such as Distributed Denial of Service attacks (DDoS), Denial of Service attacks (DoS), and Service Scans demand a unique automatic system capable of identifying traffic abnormality at the earliest stage to avoid system damage. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not only the issue with current Intrusion Detection Systems (IDS), but the efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. Thus, this study concentrates on constructing an ensemble classifier using the proposed Integrated Evaluation Metrics (IEM) to determine the best performance of IDS models. The automated Ranking and Best Selection Method (RBSM) is performed using the proposed IEM to select the best model for the ensemble classifier to detect highly accurate attacks using machine learning and deep learning techniques. Three datasets of real IoT traffic were merged to extend the proposed approach’s ability to detect attack traffic from heterogeneous IoT networks. The results show that the performance of the proposed model achieved the highest accuracy of 99.45% and 97.81% for binary and multi-classification, respectively

Controlling Polarization in Personalization: An Algorithmic Framework

Personalization is pervasive in the online space as it leads to higher efficiency for the user and higher revenue for the platform by individualizing the most relevant content for each user. However, recent studies suggest that such personalization can learn and propagate systemic biases and polarize opinions; this has led to calls for regulatory mechanisms and algorithms that are constrained to combat bias and the resulting echo-chamber effect. We propose a versatile framework that allows for the possibility to reduce polarization in personalized systems by allowing the user to constrain the distribution from which content is selected. We then present a scalable algorithm with provable guarantees that satisfies the given constraints on the types of the content that can be displayed to a user, but- subject to these constraints- will continue to learn and personalize the content in order to maximize utility. We illustrate this framework on a curated dataset of online news articles that are conservative or liberal, show that it can control polarization, and examine the trade-off between decreasing polarization and the resulting loss to revenue. We further exhibit thefl exibility and scalability of our approach by framing the problem in terms of the more general diverse content selection problem and test it empirically on both a News dataset and the MovieLens dataset

Exploring interprofessional communication and collaboration among pharmacists, nurses, and laboratories enhancing patient safety and healthcare outcomes

Background: The efficiency of healthcare delivery is closely connected to the quality of interprofessional communication and cooperation among healthcare workers. The purpose of this research is to examine the diverse effects of interprofessional cooperation including pharmacists, nurses, and laboratory experts on patient safety and healthcare outcomes. Aim: This extensive study aims to consolidate current literature, empirical data, and theoretical models to provide a clear comprehension of the importance of efficient interprofessional communication and cooperation in healthcare environments. The objective of the evaluation is to assess the influence of cohesive teamwork, communication, and cooperation among healthcare professionals on several aspects of healthcare, including patient safety, medication management, care coordination, diagnostic accuracy, and overall healthcare quality. Method: A methodical search technique was used to locate relevant studies in electronic databases, such as PubMed, MEDLINE, and Cochrane Library. The inclusion criteria include research that provide insights into the influence of interprofessional cooperation on patient safety, healthcare outcomes, and the involvement of pharmacists, nurses, and laboratory experts in improving healthcare delivery. Results: The analysis emphasizes the crucial significance of pharmacists, nurses, and laboratory experts in improving patient safety and healthcare results by means of efficient interprofessional communication and cooperation.