In this chapter, you will see how the simplex method simplifies when it is applied to a class of optimization problems that are known as “network flow models.” You will also see that if a network flow model has “integer-valued data,” the simplex method finds an optimal solution that is integer-valued.

Flows in Networks

Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms

Blockchain technology is getting a growing attention from various organizations and researchers as it provides magical solutions to the problems associated with the classical centralized architecture. Blockchain, whether public or private, is a distributed ledger with the capability of maintaining the integrity of transactions by decentralizing the ledger among participating users. On the other hand, the Internet of Things (IoT) represents a revolution of the Internet which can connect nearly all environment devices over the Internet to share their data to create novel services and applications for improving our quality of life. Although the centralized IoT system provides countless benefits, it raises several challenges. Resolving these challenges can be done by integrating IoT with blockchain technology. To be prepared for the integration process, this chapter provides an overview of technical aspects of the blockchain and IoT. It started by reviewing blockchain technology and its main structure. Applications and challenges of the blockchain are also presented. This is followed by reviewing the IoT system by highlighting common architecture and essential characteristics. Various applications and challenges of the IoT system are also discussed.

Technical aspects of blockchain and IoT

The rise of malware

The Internet of Things (IoT) has become one of the unprecedented research trends for both academic and commercial organizations. Every day, the publicity of the IoT is increased. This is because the unlimited benefits that the IoT can bring to our environment. The IoT has the ability to connect almost all objects of real-world to communicate and cooperate with each other over the Internet to facilitate generating new applications and services that can improve our quality of life. This paper provides an overview of the IoT system with highlighting its applications, challenges, and open issues. It starts with discussing the state-of-the-art of the IoT system and its layered architecture. This is followed by discussing different characteristics of the IoT with presenting a comparison between common IoT communication technologies. Different IoT applications and challenges are also discussed. At the end, open research directions related to the IoT are also presented.

/pdf/internet-of-things-state-of-the-art-challenges-applications-16bmok5gcy.pdf

Internet of Things: state-of-the-art, challenges, applications, and open issues

In recent years, there has been a huge growth in the world of intelligent devices for home automation. Such gadgets are designed in order to ease the interaction between people and daily home duties. Although individually they are simple to work with, each appliance has its own configuration interface which adds overhead to the general user experience. This paper presents a solution for connecting more devices into a signal entity which can be easily accessed at any time. The implementation integrates the functionalities of different home automation devices into a single application.

Smart home IoT system

Before and during COVID-19: A Cohesion Network Analysis of Students’ Online Participation in Moodle Courses

Nowadays, because of its increased popularity, Android is target to a growing number of attacks and malicious applications, with the purpose of stealing private information and consuming credit by subscribing to premium services. Most of the current commercial antivirus solutions use static signatures for malware detection, which may fail to detect different variants of the same malware and zero-day attacks. In this paper, we present a behavior-based, dynamic analysis security solution, called Android Malware Detection System, for detecting both well-known and zero-day malware. The proposed solution uses a machine learning classifier in order to differentiate between the behaviors of legitimate and malicious applications. In addition, it uses the application statistics for determining its reputation. The final decision is based on a combination of the classifier's result and the application reputation. The solution includes a unique and extensive set of data collectors, which gather application-specific data that describe the behavior of the monitored application. We evaluated our solution on a set of legitimate and malicious applications and obtained a high accuracy of 0.985. Our system is able to detect zero-day malware samples that are not detected by current commercial solutions. Our solution outperforms other similar solutions running on mobile devices. Copyright © 2015 John Wiley & Sons, Ltd.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sec.1340

Smart malware detection on Android

Modern applications produce large quantities of data in the form of logs and events in order to facilitate quick failure diagnosis and mitigation. Special Big Data database systems like Elasticsearch are needed to store and manage these logs. Through such a system, the data is used to query different events and trace down issues that appear in the application. To further improve the efficiency of failure discovery and prevention, the query mechanisms can be extended with Machine Learning techniques to observe anomalies in the infrastructure and automatically alert the managers or administrators. This research project is tracking the development of a scalable Elasticsearch monitoring system. In later stages, the focus will fall on enhancing the system's detection functionality by applying Machine Learning jobs on the stored data.

Systems Monitoring and Big Data Analysis Using the Elasticsearch System

Cybersecurity is an arms race, with both the security and the adversaries attempting to outsmart one another, coming up with new attacks, new ways to defend against those attacks, and again with new ways to circumvent those defences. This situation creates a constant need for novel, realistic cybersecurity datasets. This paper introduces the effects of using machine-learning-based intrusion detection methods in network traffic coming from a real-life architecture. The main contribution of this work is a dataset coming from a real-world, academic network. Real-life traffic was collected and, after performing a series of attacks, a dataset was assembled. The dataset contains 44 network features and an unbalanced distribution of classes. In this work, the capability of the dataset for formulating machine-learning-based models was experimentally evaluated. To investigate the stability of the obtained models, cross-validation was performed, and an array of detection metrics were reported. The gathered dataset is part of an effort to bring security against novel cyberthreats and was completed in the SIMARGL project.

https://www.mdpi.com/1424-8220/21/13/4319/pdf

Mihai Carabas

Papers

Smart home IoT system

Before and during COVID-19: A Cohesion Network Analysis of Students’ Online Participation in Moodle Courses

Smart malware detection on Android

Systems Monitoring and Big Data Analysis Using the Elasticsearch System

The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset.