Andriy V. Goncharenko, "Specific Case of Two Dynamical Options in Application to the Security Issues: Theoretical Development", International Journal of Computer Network and Information Security(IJCNIS), Vol.14, No.1, pp.1-12, 2022. DOI:10.5815/ijcnis.2022.01.01

Photoplethysmogram (PPG) sensing is a field of signal measurement that involves accurate sensor design and efficient signal processing. Sensing interfaces have matured due to use of sophisticated nano-meter technologies, that allow for high speed, and low error sampling. Thus, in order to improve the efficiency of PPG sensing, the signal processing unit must be tweaked. A wide variety of algorithms have been proposed by researchers that use different classification models for signal conditioning and error reduction. When applied to blood pressure (BP) monitoring, the efficiency of these models is limited by their ability to differentiate between BP levels. In order to improve this efficiency, the underlying text proposes a novel multimodal ensemble classifier. The proposed classifier accumulates correct classification instances from a series of highly efficient classifiers in order to enhance the efficiency of PPG sensing. This efficiency is compared with standard classification models like k-nearest neighbors (kNN), random forest (RF), linear support vector machine (LSVM), multilayer perceptron (MLP), and logistic regression (LR). It is observed that the proposed model is 10% efficient than these models in terms of classification accuracy; and thus, can be used for real time BP monitoring PPG signal acquisition scenarios. This accuracy is estimated by comparing actual BP values with measured BP values, and then evaluating error difference w.r.t. other algorithms.

According to Cybersecurity Ventures, the damage related to cybercrime is projected to reach $6 trillion annually by 2021. The majority of the cyberattacks are directed at financial institutions as this reduces the number of intermediaries that the attacker needs to attack to reach the target - monetary proceeds. Research has shown that malware is the preferred attack vector in cybercrimes targeted at banks and other financial institutions. In light of the above, this paper presents a Bayesian Attack Network modeling technique of cyberattacks in the financial sector that are perpetuated by crimeware. We use the GameOver Zeus malware for our use cases as it’s the most common type of malware in this domain. The primary targets of this malware are any users of financial services. Today, financial services are accessed using personal laptops, institutional computers, mobile phones and tablets, etc. All these are potential victims that can be enlisted to the malware’s botnet. In our approach, phishing emails as well as Common Vulnerabilities and Exposures (CVEs) which are exhibited in various systems are employed to derive conditional probabilities that serve as inputs to the modeling technique. Compared to the state-of-the-art approaches, our method generates probability density curves of various attack structures whose semantics are applied in the mitigation process. This is based on the level exploitability that is deduced from the vertex degrees of the compromised nodes that characterizes the probability density curves.

The complexity of interconnected devices requires constant real-time monitoring, as failure of one part can have catastrophic consequences for the entire system. Computer-information monitoring tools enable us to always be one step ahead of potential problems that may occur in a monitored network environment, whether it is a human-caused configuration or simply an element has failed or stopped working. Not only can they report potential problems, but they can also solve the problem itself. For example, if an element needs increased resources at a given time, the tool itself can recognize it and automatically increase the resource needs of that element. By setting up a monitoring system in a virtual environment, the results can be seen and through their analysis will bring an optimal solution when it comes to what agent to use. This paper presents analysis of how network monitoring agent is responding in cases when there is increased use of shared resources. Knowing this can help in choosing what agent should be used in any given environment, and with that more resources will be saved. This leads to better utilization of resources which is an important in mid-size and big setup of computer monitoring systems.

In recent years the domain of Internet of Things (IoT) has acquired great interest from the ICT community. Environmental observation and collecting information is one of the key reasons that IoT infrastructure facilitates the creation of many varieties of the latest business methods and applications. There are however still issues about security measures to be resolved to ensure adequate operation of devices. Distributed Denial of Service (DDoS) attacks are currently the most severe virtual threats that are causing serious damage to many IoT devices. With this in mind, numerous research projects were carried out to discover new methods and develop Novel techniques and solutions for DDOS attacks prevention. The use of new technology, such as software-defined networking (SDN) along with IoT devices has proven to be an innovative solution to mitigate DDoS attacks. In this article, we are using a novel data sharing system in IoT units that link IoT units with the SDN controller and encrypt information from IoT unit. We use conventional Redstone cryptographic algorithms to encrypt information from IoT devices in this framework. The Proposed Belief Based Secure Correlation methodology supports the prevention of DDOS attacks and other forms of data attacks. The system proposes new routes for transmission through the controller and communicates with approved switches for the safe transmission of data. To simulate our entire scenario, we proposed the algorithm Belief Based Secure Correlation (BBSC) implemented in SDN–IoT Testbed and verified IoT data is secure during transmission in the network.

The optoelectronic Biswapped-Hyper Hexa-Cell is a recently reported recursive and a symmetrical architecture of Biswapped Family. This symmetrical network has claimed and proved to be advantageous in terms of network diameter, bisection width, minimum node-degree and network cost compared to its counterpart architecture of OTIS family named ‘OTIS Hyper Hexa-Cell’ and traditional grid-based architecture of Biswapped family named ‘Biswapped-Mesh’. In this paper, we present a novel and efficient parallel algorithm for counting sort for sorting distinct numeric values on dh-dimensional Biswapped-Hyper Hexa-Cell optoelectronic network. The parallel algorithm demands 10d_h+12+ log_2⁡〖S_A 〗 electronic and 10 optical moves, where SA is the size of count array: Acip[SA], and SA equals to maximal minus minimal numeric value plus one. On the basis of analysis, it is concluded that proposed algorithm delivers better performance since speedup and efficiency improved for worst case scenario (difference between maximal and minimal data values becomes larger) with the increase of only few communication moves required for sorting.

With the advancement of technology, cybercrimes are surging at an alarming rate as miscreants pour into the world's modern reliance on the virtual platform. Due to the accumulation of an enormous quantity of cybercrime data, there is huge potential to analyze and segregate the data with the help of Machine Learning. The focus of this research is to construct a model, Ensem_SLDR which can predict the relevant sections of IT Act 2000 from the compliant text/subjects with the aid of Natural Language Processing, Machine Learning, and Ensemble Learning methods. The objective of this paper is to implement a robust technique to categorize cybercrime into two sections, 66 and 67 of IT Act 2000 with high precision using ensemble learning technique. In the proposed methodology, Bag of Words approach is applied for performing feature engineering where these features are given as input to the hybrid model Ensem_SLDR. The proposed model is implemented with the help of model stacking, comprising Support Vector Machine (SVM), Logistic Regression, Decision Tree, and Random Forest and gave better performance by having 96.55 % accuracy, which is higher and reliable than the past models implemented using a single learning algorithm and some of the existing hybrid models. Ensemble learning techniques enhance model performance and robustness. This research is beneficial for cyber-crime cells in India, which have a repository of detailed information on cybercrime including complaints and investigations. Hence, there is a need for model and automation systems empowered by artificial intelligence technologies for the analysis of cybercrime and their classification of its sections.