The utilize of the web has made humans and companies powerless to exterior assaults. Indeed, cyber problems essentially influence information frameworks with distinctive types of malicious attacks such as spyware, virus, social engineering, etc. The Internet e-mail service, in particular, has become one of the most dependable methods of communication among people, institutions, and companies. The development of digital signatures to e-mail services has raised the e-mail security, which led to replacing the standard mailing of registered letters. Unfortunately, the process of sending and receiving e-mails has created a negative impact means on security and privacy from cybercriminals by diffusing spam and malware. As a result, e-mail hosts are constantly under attack by malicious programs that are often attached to e-mails. In this paper, the simulation model and prototype of an email traffic monitor developed and tested in order to prove the ability of our proposed method for detecting new viruses. This paper states the success possibility of this new method based on the simulation results. The results of the analysis suggest that the Cyber Security Modeling Language (CySeMoL) model has a good performance of operating system vulnerability prediction. At last, some useful suggestions in the context of the CySeMoL model are presented.

Through a Bluetooth connection between the Muse 2 device and the meditation app, leveraging IoT capabilities. The methodology encompasses data collection, preprocessing, feature extraction, and model training, all while utilizing Internet of Things (IoT) functionalities. The Muse 2 device records EEG data from multiple electrodes, which is then processed and analyzed within a mobile meditation platform. Preprocessing steps involve eliminating redundant columns, handling missing data, normalizing, and filtering, making use of IoT-enabled techniques. Feature extraction is carried out on EEG signals, utilizing statistical measures such as mean, standard deviation, and entropy. Three different models, including Support Vector Machine (SVM), Random Forest, and Multi-Layer Perceptron (MLP), are trained using the preprocessed data, incorporating Internet of Things (IoT) based methodologies. Model performance is assessed using metrics like accuracy, precision, recall, and F1-score, highlighting the effectiveness of IoT-driven techniques. Notably, the MLP and Random Forest models demonstrate remarkable accuracy and precision, underlining the potential of this IoT-integrated approach. Specifically, the three models achieved high accuracies, with Random Forest leading at 0.999, followed by SVM at 0.959 and MLP at 0.99. This study not only contributes to the field of brain-computer interfaces and assistive technologies but also showcases a viable method to seamlessly integrate the Muse 2 device into meditation practices, promoting self-awareness and mindfulness with the added power of IoT technology.

To improve and facilitate transactions between customers and financial institutions, the utilization of Internet banking has been leveraged to deliver a heightened caliber of service characterized by heightened security and efficiency in contrast to traditional banking modalities. It is suggested in this research introduces an innovative security framework, designed to furnish a protected mechanism ensuring secure communication, authentication, confidentiality, and safeguarding of financial transactions between banking institutions and end-users, all without necessitating reliance on a physical card. The fundamental underpinning of this proposed system involves the amalgamation of a Quick Response (QR) code with a hybridized Advanced Encryption Standard-Elliptic Curve Cryptography (AES-ECC) model. Following the successful installation of the security application on the mobile device, and subsequent to an accomplished registration and encryption of data inputs, encoding and decoding processes are facilitated through the intrinsic encoding and decryption keys embedded within this hybrid algorithm. The clientele receives a QR code containing encrypted transaction details, and upon scanning this code via the designated Android application, the pertinent information is promptly displayed. Empirical assessments validate the effectiveness of the suggested approach, demonstrating superior outcomes when juxtaposed with prevailing methodologies.

Ensuring the correction of heterogeneous and complex systems is an essential stage in the process of engineering systems. In this paper, we propose an approach to verify and validate complex systems specified by SysML language. We translate SysML specifications into Promela models in order to validate the designed systems by model checking SPIN. The requirements properties are translated to Linear Temporal Logic (LTL) formulae and verified by Spin. A case study is presented to illustrate the effectiveness of our approach.

The SCITEPRESS Digital Library offers online access to all papers presented at INSTICC conferences, as well as a few conferences organized by others.

Huge efforts have been put in the developing of diagnostic methods to skin cancer disease. In this paper, two different approaches have been addressed for detection the skin cancer in dermoscopy images. The first approach uses a global method that uses global features for classifying skin lesions, whereas the second approach uses a local method that uses local features for classifying skin lesions. The aim of this paper is selecting the best approach for skin lesion classification. The dataset has been used in this paper consist of 200 dermoscopy images from Pedro Hispano Hospital (PH2). The achieved results are; sensitivity about 96%, specificity about 100%, precision about 100%, and accuracy about 97% for globalization approach while, sensitivity about 100%, specificity about 100%, precision about 100%, and accuracy about 100% for Localization Approach, these results showed that the localization approach achieved acceptable accuracy and better than globalization approach for skin cancer lesions classification.