Search

The email has become one of the most efficient and cost-effective methods of communication in recent years. However, as the number of email users grows, so does the number of spam emails. Email management has become a big and rising concern for both people and companies as a consequence of its sensitivity to abuse. Spam, or the unsolicited sending of unwanted email messages, is one example of misuse. Spam is defined as unsolicited bulk email, or email sent to a large number of people without their consent. Half of the users receive 10 or more spam emails each day, while some users receive hundreds of unwanted emails per day. Online spiders are used by many spammers to discover email addresses on web pages. Because spam emails can fill up the storage space of a file server quickly, they could cause a very severe problem for many websites with thousands of users for this in this study; we present a method for spam filtering using some machine learning techniques to predict whether an email is spam or no.

While the prevalent cloud storage platforms are offering convenient services in support of diverse data-driven applications for clients, various security concerns raise in terms of data confidentiality, availability, and retrievability. Among them, servers’ dishonesty on the location-specific data backup becomes a serious concern when the data stands out clients’ control, considering the strict regulations imposed by many governments and organizations on data storage location. This article studies location-aware data backup verification for the data stored in clouds and aims to design a secure framework, named as ReliableBox, enabling the clients to verify if their data have been backed up on the remote servers with specific geolocation. In the design of ReliableBox, we leverage the prominent proof-of-storage techniques for data possession proof, and take advantage of multilateration geolocation and Intel SGX for the precise communication delay measurement and trust computing delay measurement, respectively. In ReliableBox, a client first computes integrity tags for the files and then outsources both the files and tags to the cloud storage server. In the later attestation, with the precise network delay and distance measurement from location-known verifiers, the client verifies that the outsourced files are intact and backed-up to hosts at the specific geolocation. With the customized design, ReliableBox can support the security needs in terms of both data integrity and backup location verification for clients, even when there exists potential dishonest cloud service providers who may manipulate the network delays or forge verification proofs. We provide security analysis to show the security property of ReliableBox in terms of data access, confidentiality, and verifications. In the end, we implement the system prototype and deploy it into several prevalent and commercial cloud platforms for performance evaluation. The experimental results demonstrate that ReliableBox is secure in support of data integrity checking and location-aware backup auditing, while it is robust to the data possession and location spoofing attacks.

This paper aims to develop an intelligent autonomous surveillance system for the safety surveillance of indoor environments with the integration of techniques such as sensory information fusion, internet of things (IoT), and artificial intelligence (AI). We have developed the following four subsystems to implement the intelligent autonomous surveillance system: (1) an autonomous surveillance vehicle (ASV) to be placed on the track to detect environmental sensory information in indoor environments (such as environmental temperature, flame, CO concentration, and liquefied petroleum gas (LPG) concentration) and the movement information of the ASV (such as position coordinates and movement speed); (2) an intelligent fire detection algorithm to be used for identification of disaster statuses in indoor environments; (3) a visible human-machine interface to instantaneously display the environmental sensory information and ASV movement information, and show the environmental disaster information (such as disaster statuses, disaster images, image capture times, and disaster positions); (4) a remote server composed of a database server, file servers, a web server, and responsive web pages to store and display the recorded environmental sensory information, ASV movement information, and environmental disaster information. Furthermore, a 3D experimental track testbed was constructed to verify the effectiveness and feasibility of the intelligent autonomous surveillance system. Our experimental results have successfully validated that the intelligent autonomous surveillance system can detect disaster events in a variety of conditions.

In this paper, a file popularity and size-aware (PSA) caching scheme is proposed for a hybrid heterogeneous network (HetNet) comprising a file server, macro base stations (BSs) operating in the microwave frequency bands, and pico BSs operating in the millimeter wave frequency wave bands. Different than the state-of-the-art size-weighted popularity (SWP) and popularity-based caching schemes that assume equal file size, the file PSA caching scheme utilizes knowledge of the file popularity and size distributions to maximize the network average cache hit probability and the network average success probability subject to the available cache capacity. Moreover, the maximization of the network average success probability is shown to be non-convex and dynamic programming and branch and bound algorithms are employed to circumvent its convexity. New expressions for the optimal file caching probabilities that maximize the network average success probability in noise-limited and interference-limited HetNet scenarios are provided which are evaluated using numerical techniques. The results demonstrate that the proposed file PSA caching scheme provides up to 7% gain in the network average success probability compared to the SWP-based scheme. Lastly, the combination of coded caching with cooperative transmission achieves up to 40% gain compared to a non-cooperative transmission scheme without coded caching.

En el presente artículo se realiza la instalación y configuración inicial del servidor nethserver, mediante el cual vamos a poder configurar diferentes servicios que se pueden utilizar en una empresa. Los servicios que se van a configurar son un proxy SSL transparente que nos va a permitir filtrar el tráfico generado por los clientes de las zonas verde y azul, y bloquear sitios de internet por contenido o por dominios específicos, se configura un firewall para aplicar reglas para la restricción de sitios o portales de entretenimiento o redes sociales, se implementa un file server y print server para poder acceder a través del controlador de dominio LDAP a los servicios de carpetas compartidas e impresoras, por último, se configura una VPN para establecer conexión mediante un túnel privado entre una estación de trabajo y el servidor para acceder a contenidos o aplicaciones de forma segura. In this article, the installation and initial configuration of the nethserver server, through which we will be able to configure different services that can be used in a company. The services to be configured are an SSL proxy transparent that will allow us to filter the traffic generated by customers in the green and blue zones, and block internet sites by content or by domains specific, a firewall is configured to apply rules for the restriction of entertainment sites or portals or social networks, a file server is implemented and prints server to be able to access through the controller LDAP domain to shared folder services and printers, finally, a VPN is configured to established a connection through a private tunnel between a workstation and server to access content or applications securely.

With the in-depth application of new technologies such as big data in education fields, the storage and sharing model of student education records data still faces many challenges in terms of privacy protection and efficient transmission. In this paper, we propose a data security storage and sharing scheme based on consortium blockchain, which is a credible search scheme without verification. In our scheme, the implementation of data security storage is using the blockchain and storage server together. In detail, the smart contract provides protection for data keywords, the storage server stores data after data masking, and the blockchain ensures the traceability of query transactions. The need for precise privacy data is achieved by constructing a dictionary. Cryptographic techniques such as AES and RSA are used for encrypted storage of data, keywords, and digital signatures. Security analysis and performance evaluation shows that the availability, high efficiency, and privacy-preserving can be achieved. Meanwhile, this scheme has better robustness compared to other educational records data sharing models.

In recent years there are many researchers conducted regarding with cloud storage benefits and its efficiency improvements, but all are raising a question regarding the effectiveness and privacy. The effectiveness of the cloud storage system purely depends on the storage capacity and responsiveness of the server, in which the size of the data is large automatically the responsiveness of the storage server goes down. To avoid this issue many researchers found a lots of solution such as multiple cloud server placements, the local cloud farm fixation and so on. But all are coming too struck with the privacy issues and the security threats are more in the case of such things. These issues need to be resolved with one powerful mechanism as well as providing a good storage mechanism without any security threats. In this paper, a healthcare application is taken into consideration and introduce a new fog based cloud storage system is designed such as Intelligent Fog based Cloud Strategy using Edge Devices (IFCSED), in which this Fog Computing process provides an efficient health data storage structure to the cloud server to maintain the high priority records without considering on regular or non-prioritized records. This proposed strategy follows Edge-based Fog assistance to identify the healthcare data priority utilizing analyzing the records, identify the priority level and classify those priority records from the data and pass that to the remote cloud server as well as keep the remaining non-prioritized records into the local fog server. The fog server data can be back up with every point of interval using data backup logics. These backup assures the data protection and the integrity on the storage medium as well as the proposed approach of IFCSED eliminates the processing delay by using time complexity estimations. The data which is coming from Internet of Things (IoT) based real-world health record will be acquired by using controllers and other related devices, which will be delivered to the Edge Devices for manipulation. In this Edge processing device accumulates the incoming health data and classifies that based on the prioritization logic. The health sensor data which is coming in regular interval with normal sensor values are considered to be the regular non-prioritized data and the health sensor data coming up with some abnormal contents such as mismatched heart rate, increased pressure level and so on are considered as a the prioritized record. The sensor assisted health data will low-priority will be moved to the fog server, which is locally maintained into the environment itself and the sensor assisted health data coming up with high priority will be moved into the remote cloud server. The proposed approach assures the time efficiency, reduction in data loss, data integrity and the storage efficiency, as well as these things, will be proved over the resulting sections with proper graphical results.