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Due to the rapid advancement of social media, a huge amount of data is generated daily. Due to this great spread and expansion of the data at the social or professional level, the risks of securing the information become a challenging job. In this regard, we conducted an in-depth interview to gather specific information about how infected users may be provided with information about recovering their hacked social networking accounts. Further, we have introduced a complete solution to help social network users to improve the idea of using different applications from one appropriate platform. In order to build this secure platform for accessing the security applications such as bank accounts, etc., we set various security methods to access social network websites, such as sending an OTP to their respective mobile devices, email or by fingerprint. Further, we also added a camera to identify the wrong or fake registration process of an intruder. The camera captures an image of the intruder registering to a social network website using the legitimate user’s information. In addition, the application also has a solution for forgetting the password or security questions that are sent to the user via email. Finally, the application saves the password, which can be recovered when the user forgets it.

Childhood obesity has become one of the major health issues in the global population. The increasing prevalence of childhood obesity is associated with serious health issues and comorbidities related to obesity. Several studies mentioned that childhood obesity became even worse recently due to the effect of COVID-19 and the consequent policies and regulations. For that reason, Internet of Things (IoT) technologies should be utilized to overcome the challenges related to obesity management and provide care from a distance to improve the health care services for obesity. However, IoT by itself is a limited resource and it is important to consider other artificial intelligent (AI) components. Thus, this paper contributes into the literature of child obesity management by introducing a comprehensive survey for obesity management covering clinical work measuring the association between sleep disturbances and childhood obesity alongside physical activity and diet and comparatively analyzing the emerging technologies used to prevent childhood obesity. It further contributes to the literature by proposing an interactive smart framework that combines clinical and emerging AI/telehealth technologies to manage child obesity. The proposed framework can be used to reduce children obesity and improve their quality of life using Machine Learning (ML). It utilizes IoT devices to integrate information from different sources and complement it with a mobile application and web-based platform to connect parents and physicians with their child.

Tom Ginsburg's concept of “authoritarian international law” (AIL) is as important as the one it references, Thomas Franck's “right to democratic governance.” It underlines how the promise carried by Franck was betrayed in the bitter turn of history that ended the emerging hope for democracy ruling all nations in the world after 1989. This hope had developed by fits and starts as the slow fulfilment of the Kantian project for “perpetual peace” amongst a world federation of democratic republics on which the League of Nations and the United Nations were built. To the now-universal acknowledgment of the grave domestic setbacks to human rights and democracy, Ginsburg's article adds an account of the international setbacks which followed. Its chief importance is in raising the question of the emergent authoritarian traits of international law in the wake of these setbacks. With my appreciation of Ginsburg's formidable treatment, including a title that will mark, like Franck's, an important moment in the field, I will challenge some of his conclusions and offer counterpoints in the present essay. In particular, I will (1) suggest the irrelevance of the three “evils of AIL”; (2) highlight the significance of 2006 as the date when AIL started rising; (3) emphasize the importance of U.S. isolationism in the rise of AIL; and (4) argue that the better investment to counter that rise is in nonviolence.

The Internet of Things (IoT) has become an integral part of our daily life as it is growing in many fields, such as engineering, e-health, smart homes, smart buildings, agriculture, weather forecasting, etc. However, the growing number of IoT devices and their weak configuration raise many security challenges such as designing protocols to protect these devices from various types of attacks such as using them as bots for DDoS attacks on target servers. In order to protect IoT devices from enslavement as bots in a home environment, we develop a lightweight security model consisting of various security countermeasures. The working mechanism of the proposed security model is presented in a two-part experimental scenario. Firstly, we describe the working mechanism of how an attacker infects an IoT device and then spreads the infection to the entire network. Secondly, we propose a set of mechanisms consisting of filtration, detection of abnormal traffic generated from IoT devices, screening, and publishing the abnormal traffic patterns to the rest of the home routers on the network. We tested the proposed scheme by infecting an IoT device with malicious code. The infected device then infects the rest of the IoT devices in its network and launches a DDoS attack by receiving attack-triggering commands from the botmaster. Finally, the proposed detection mechanism is used to detect the abnormal traffic and block the connection of infected devices in the network. The results reveal that the proposed system blocks abnormal traffic if the packets from an IoT device exceeded a threshold of 50 packets. Similarly, the network packet statistics show that, in the event of an unwanted situation, the detection mechanism runs smoothly and avoids any possible delays in the network.

Effective security has become a vital part of our daily routine, for example securing cash, jewelry, documents, and other valuables. One way is to protect these items by keeping them in a safe box. However, current safe boxes are vulnerable to a number of security attacks such as physical, repudiation, anonymously modifying, etc. In this regard, this paper presents a robust smart safe box using the blockchain solution to ensure each transaction and secure it from unauthorized access. Also, it traces back the local safe items by reading back to blockchain data transactions and protecting them from thieves by tracking the burglar with a real-time tracking location Global Positioning System (GPS) tracker and capturing a picture and video recording through the external camera. Similarly, with more security enhancement using the TensorFlow framework to analyze optical detection and identify things inside the box using an internal camera. At the same time, each transaction is stored by using Write Once and Read Many (WORM) patterns saved in local private blockchain data block logs. Finally, it uses three-way authentication mechanisms to unlock the safe box that authenticates the users, (1) face recognition, (2) fingerprint and (3) the keypad password. The performance of the proposed smart safe box is tested by a number of hardware and blockchain tests. The results reveal that the proposed smart safe box ensures safe and authenticated access to the box and records each transaction using blockchain technology. Further, the performance evaluation of the private blockchain significantly improved the integrity and safeness of the proposed smart safe box.