GRADIENT BOOSTING ALGORITHM FOR EARLY DETECTION OF UNKNOWN INTERNET OF THINGS DEVICES

The pervasive availability of the Internet of Things (IoT) markets lures targets for cyber-attacks since most manufactured IoT devices are usually resource-constrained devices. The first powerful line of IoT network protection from these vulnerabilities is detecting IoT devices especially the unauthorized ones by utilizing machine learning (ML) algorithms. Actually, it is so difficult or even impossible to find individual unknown IoT devices during the setup phase but, knowing their manufacturers is a matter to be deliberate. In this paper, a new method based fingerprints generation is introduced to detect the connected devices in the setup phase. Fingerprints for 21 different IoT devices are generated using devices’ network traffic. The whole produced fingerprints of devices are divided into four groups according to their manufacturers or fingerprints similarity proportion. Gradient Boosting Algorithm is applied to achieve the identified purposes. The proposed method is considered as a preparatory study for early detection of unauthorized. The performance evaluation for the proposed method was calculated based on two metrics: Identification accuracy and F1-score. The average identification accuracy rate was around 98.65%, while the average F1-score was about 99%.

Privacy preserving model-based authentication and data security in cloud computing

Purpose Cloud computing plays a significant role in the initialization of secure communication between users. The advanced technology directs to offer several services, such as platform, resources, and accessing the network. Furthermore, cloud computing is a broader technology of communication convergence. In cloud computing architecture, data security and authentication are the main significant concerns. Design/methodology/approach The purpose of this study is to design and develop authentication and data security model in cloud computing. This method includes six various units, such as cloud server, data owner, cloud user, inspection authority, attribute authority, and central certified authority. The developed privacy preservation method includes several stages, namely setup phase, key generation phase, authentication phase and data sharing phase. Initially, the setup phase is performed through the owner, where the input is security attributes, whereas the system master key and the public parameter are produced in the key generation stage. After that, the authentication process is performed to identify the security controls of the information system. Finally, the data is decrypted in the data sharing phase for sharing data and for achieving data privacy for confidential data. Additionally, dynamic splicing is utilized, and the security functions, such as hashing, Elliptic Curve Cryptography (ECC), Data Encryption Standard-3 (3DES), interpolation, polynomial kernel, and XOR are employed for providing security to sensitive data. Findings The effectiveness of the developed privacy preservation method is estimated based on other approaches and displayed efficient outcomes with better privacy factor and detection rate of 0.83 and 0.65, and time is highly reduced by 2815ms using the Cleveland dataset. Originality/value This paper presents the privacy preservation technique for initiating authenticated encrypted access in clouds, which is designed for mutual authentication of requester and data owner in the system.

Geometrical Optimization of Centerless Grinding Process by Profiled Workrest

This work presents a method to optimize centerless grinding geometrical configuration. It is based on the regulation of the horizontal axes of operating and rubbing wheels of the machine machine coupled with an opportune blade profile, allowing a continuous selection of workrest angle and workpiece height, without requiring blade substitution and/or manual interventions. The regulation of workrest angle and height cannot be done independently: their relationship is defined during blade profile design. In machines with two independent axes for regulating wheel and blade horizontal displacement, this regulation can be performed in process or in the setup phase and does not require manual blade adjustment/replacement. This results in optimal processing parameter choice leading to improved quality and shorter time of processing as well as reduced setup time.

Self-Processing Private Sensor Data via Garbled Encryption

We introduce garbled encryption, a relaxation of secret-key multi-input functional encryption (MiFE) where a function key can be used to jointly compute upon only a particular subset of all possible tuples of ciphertexts. We construct garbled encryption for general functionalities based on one-way functions.We show that garbled encryption can be used to build a self-processing private sensor data system where after a one-time trusted setup phase, sensors deployed in the field can periodically broadcast encrypted readings of private data that can be computed upon by anyone holding function keys to learn processed output, without any interaction. Such a system can be used to periodically check, e.g., whether a cluster of servers are in an “alarm” state.We implement our garbled encryption scheme and find that it performs quite well, with function evaluations in the microseconds. The performance of our scheme was tested on a standard commodity laptop.

Efficient Data Access Control for Cloud Computing With Large Universe and Traceable Attribute-Based Encryption

Ciphertext-policy attribute-based encryption (CP-ABE) schemes provide fine-grained access control for the data stored in cloud computers. However, commercial CP-ABE applications need a new encryption scheme for providing two properties such as: supporting large universe attribute and traceability. First, a large universe attribute allows the attribute authority to use any number of attributes in the system. i.e., the attribute universe is dynamic, and it is not fixed at the setup phase. Second, traceable CP-ABE systems trace the dishonest users who intentionally leak the private key for their profit. In this article, a large universe CP-ABE system with white box traceability has been proposed. The attribute universe of the proposed technique is exponentially larger, and it is polynomially unbound. Further, this technique will trace the identity of users who involve in malicious activities. In addition, the proposed scheme can express any kind of monotonic tree access policies into linear secret sharing structure (LSSS). Compared with the existing schemes that are presented to achieve the same property, proposed scheme has achieved better experimental results and so it is applicable for commercial applications.

“Sulcus Sink”: A Compact Binary and Semi-Automated Inverse Dijkstra-based System for Describing Sulcal Trajectories

We present a description of a system that uses a compact binary representation to describe and trace sulci on a reconstructed human cortical surface, based on a set of human-generated targets. The inputs to the system were manually created on a training set of 20 normal subjects (11 females, 9 males) with ages 22 – 40 years. T1 weighted MPRAGE images were collected on a Siemens 3T Trio scanner, with TR/TE = 2530/3.3, matrix = 256×256, FOV = 256mm, slice thickness 1.33mm. The resultant images were reconstructed with Freesurfer, and 10 sulci on each hemisphere were traced by an expert human operator and independently assessed for accuracy. Presented with these input trajectories in its training phase, the system attempted to determine a compact binary feature vector of each sulcus on each subject using as descriptor a binary parametrized function of several surface-geometry variables (such as mean curvature, sulcal depth, edge length, etc.). This function was optimized in a supervised learning fashion using a Dijkstra-based graph theory formulation, in which the binary weights were used to define graph edge costs. In the setup phase, the system was presented with sulcal trajectories already defined on surfaces, and then adjusted its parametrized weights in a binary fashion to minimize differences between the training input path and its Dijkstra-generated output path. Once the setup phase was complete and sulci had been described in a per-sulcus, per-subject manner, we generalized the per-sulcus description across all the subjects to construct a template binary word for each specific sulcus. The performance of the system for each subject and each sulcus, and for each template sulcus group was measured against the original human reference in both a quantitative and qualitative manner. Individual subjects generally showed very good optimization to their manually traced training samples across all sulci, with 91% average overlap within 4mm of the human target. Generalized group results, as expected, showed less overlap with the original human targets, but still performed on average with 80% overlap. Quantitatively, the group results were nonetheless for the most part quite acceptable to an independent human evaluator. The parametrized binary weight description that drives the Dijkstra path optimization is presented as a mechanism to succinctly and compactly describe individual human sulci and groups of sulci.

SLEC: A Novel Serverless RFID Authentication Protocol Based on Elliptic Curve Cryptography

Internet of Things (IoT) is a new paradigm that has been evolving into the wireless sensor networks to expand the scope of networked devices (or things). This evolution drives communication engineers to design secure and reliable communication at a low cost for many network applications such as radio frequency identification (RFID). In the RFID system, servers, readers, and tags communicate wirelessly. Therefore, mutual authentication is necessary to ensure secure communication. Normally, a central server supports the authentication of readers and tags by distributing and managing the credentials. Recent lightweight RFID authentication protocols have been proposed to satisfy the security features of RFID networks. Using a serverless RFID system is an alternative solution to using a central server. In this model, both the reader and the tag perform mutual authentication without the need for the central server. However, many security challenges arise from implementing lightweight authentication protocols in serverless RFID systems. We propose a new secure serverless RFID authentication protocol based on the famous elliptic curve cryptography (ECC). The protocol also maintains the confidentiality and privacy of the messages, tag information, and location. Although most of the current serverless protocols assume secure channels in the setup phase, we assume an insecure environment during the setup phase between the servers, readers, and tags. We ensure that the credentials can be renewed by any checkpoint server in the mobile RFID network. Thus, we implement ECC in the setup phase (renewal phase), to transmit and store the communication credentials of the server to multiple readers so that the tags can perform the mutual authentication successfully while far from the server. The proposed protocol is compared with other serverless frameworks proposed in the literature in terms of computation cost and attacks resistance.

Finite Strain Homogenization Using a Reduced Basis and Efficient Sampling

The computational homogenization of hyperelastic solids in the geometrically nonlinear context has yet to be treated with sufficient efficiency in order to allow for real-world applications in true multiscale settings. This problem is addressed by a problem-specific surrogate model founded on a reduced basis approximation of the deformation gradient on the microscale. The setup phase is based upon a snapshot POD on deformation gradient fluctuations, in contrast to the widespread displacement-based approach. In order to reduce the computational offline costs, the space of relevant macroscopic stretch tensors is sampled efficiently by employing the Hencky strain. Numerical results show speed-up factors in the order of 5–100 and significantly improved robustness while retaining good accuracy. An open-source demonstrator tool with 50 lines of code emphasizes the simplicity and efficiency of the method.

Learning About Situational Crime Prevention From Offenders: Using a Script Framework to Compare the Commission of Completed and Disrupted Sexual Offenses

The collective knowledge of offenders is one of the richest ways to advance understandings of crime commission and effective crime prevention. Drawing on self-report data from 53 incarcerated offenders in three Australian states and territories, the current article presents an innovative method which, through a crime script framework, allows for a first-time comparison of completed versus disrupted sexual offenses involving adult female and child victims at each stage of the crime commission process. Findings (a) highlight the critical need to boost the efficacy of situational prevention in the crime setup phase of the sexual offense script and (b) showcase how incorporating a script framework in offender-based research can identify new directions for crime prevention

ORGANIZATION AND CONDUCTING OF SEMINARS ON THE PLATFORM MOODLE

This article is dedicated to the problem of finding new teaching methods and activities for a student. Nowadays this issue is topical because traditional lectures, practices, and seminars need refreshing by implementing the innovative technology. The seminar is defined as a kind of practical class at university which implies independent research work. Students fulfill given tasks visually representing the material in the form of a summary, report, message, etc. The author’s idea comes from an item that teacher should take in consideration two vectors of skill development in the process of seminar modeling. They are specific skills in the special-topic sphere (math skills, etc.) and general independent researchers’ skills. The aim of our research is to create a procedure of conducting a modern seminar based on the platform Moodle. Moodle has been chosen as an open-source learning platform for MOOCs (Massive Open Online Courses) with a significantly enormous potential. The element named ‘Workshop’ can be created on this platform by setting some parameters by teachers. The most important thing is that an author takes into consideration a teacher’s goals and gives an efficient algorithm to adjust necessary parameters to particular goals. This procedure includes 4 phases, such as a setup phase, submission phase, assessment phase and grading evaluation. The main part of this article contains a lot of tips how to provide every phase with instructions, forms, criteria. The author doesn’t deal with using of other active Moodle-elements which ones are planned to be researched later.