Interpretation of immunofluorescence slides by deep learning techniques: anti-nuclear antibodies case study

Nowadays, diseases are increasing in numbers and severity by the hour. Immunity diseases, affecting 8\% of the world population in 2017 according to the World Health Organization (WHO), is a field in medicine worth attention due to the high rate of disease occurrence classified under this category. This work presents an up-to-date review of state-of-the-art immune diseases healthcare solutions. We focus on tackling the issue with modern solutions such as Deep Learning to detect anomalies in the early stages hence providing health practitioners with efficient tools. We rely on advanced deep learning techniques such as Convolutional Neural Networks (CNN) to fulfill our objective of providing an efficient tool while providing a proficient analysis of this solution. The proposed solution was tested and evaluated by the immunology department in the Principal Military Hospital of Instruction of Tunis, which considered it a very helpful tool

Testing deceptive honeypots

Deception can be a useful defensive technique against cyber attacks. It has the advantage of unexpectedness to attackers and offers a variety of tactics. Honeypots are a good tool for deception. They act as decoy computers to confuse attackers and exhaust their time and resources. The objective of this thesis was to test the effectiveness of some honeypot tools in real networks by varying their location and virtualization, and by adding more deception to them. We tested both a web honeypot tool and an SSH honeypot tool. We deployed the web honeypot in both a residential network and at the Naval Postgraduate School network; the NPS honeypot attracted more attackers. Results also showed that the virtual honeypots received attacks from more unique IP addresses, and that adding deception to the web honeypot generated more interest by attackers. For the purpose of comparison, we used examined log files of a legitimate website www.cmand.org. The traffic distributions for the web honeypot and the legitimate website showed similarities, but the SSH honeypot was different. It appears that both honeypot tools are useful for providing intelligence about cyber-attack methods.http://archive.org/details/testingdeceptive1094544032Captain, Tunisian Air ForceApproved for public release; distribution is unlimited

READ-IoT: Reliable Event and Anomaly Detection Framework for the Internet of Things

International audienceInternet of Things (IoT) enables a myriad of applications by interconnecting software to physical objects. The objects range from wireless sensors to robots and include surveillance cameras. The applications are often critical (e.g. physical intrusion detection, fire fighting) and latency-sensitive. On the one hand, such applications rely on specific protocols (e.g. MQTT, COAP) and the network to communicate with the objects under very tight timeframe. On the other hand, anomalies (e.g. communication noise, sensors' failures, security attacks) are likely to occur in open IoT systems and can result by sending false alerts or the failure to properly detect critical events. To address that, IoT systems have to be equipped with anomaly detection processing in addition to the required event detection capability. This is a key feature that enables reliability and efficiency in IoT. However, anomaly detection systems can be themselves object of failures and attacks, and then can easily fall short to accomplish their mission. This paper introduces a Reliable Event and Anomaly Detection Framework for the Internet of Things (READ-IoT for short). The designed framework integrates events and anomalies detection into a single and common system that centralizes the management of both concepts. To enforce its reliability, the system relies on a reputationaware provisioning of detection capabilities that takes into account the vulnerability of the deployment hosts. As for validation, READ-IoT was implemented and evaluated using two real life applications, i.e. a fire detection and an unauthorized person detection applications. Several scenarios of anomalies and events were conducted using NSL-KDD public dataset, as well as, generated data to simulate routing attacks. The obtained results and performance measurements show the efficiency of READ-IoT in terms of event detection accuracy and real-time processing

Mental Health Intent Recognition for Arabic-Speaking Patients Using the Mini International Neuropsychiatric Interview (MINI) and BERT Model

For many years, mental health has been hidden behind a veil of shame and prejudice. In 2017, studies claimed that 10.7% of the global population suffered from mental health disorders. Recently, people started seeking relaxing treatment through technology, which enhanced and expanded mental health care, especially during the COVID-19 pandemic, where the use of mental health forums, websites, and applications has increased by 95%. However, these solutions still have many limits, as existing mental health technologies are not meant for everyone. In this work, an up-to-date literature review on state-of-the-art of mental health and healthcare solutions is provided. Then, we focus on Arab-speaking patients and propose an intelligent tool for mental health intent recognition. The proposed system uses the concepts of intent recognition to make mental health diagnoses based on a bidirectional encoder representations from transformers (BERT) model and the International Neuropsychiatric Interview (MINI). Experiments are conducted using a dataset collected at the Military Hospital of Tunis in Tunisia. Results show excellent performance of the proposed system (the accuracy is over 92%, the precision, recall, and F1 scores are over 94%) in mental health patient diagnosis for five aspects (depression, suicidality, panic disorder, social phobia, and adjustment disorder). In addition, the tool was tested and evaluated by medical staff at the Military Hospital of Tunis, who found it very interesting to help decision-making and prioritizing patient appointment scheduling, especially with a high number of treated patients every day