Parameter estimation of a susceptible–infected–recovered–dead computer worm model

Computer worms are serious threats to Internet security and have caused billions of dollars of economic losses during the past decades. In this study, we implemented a susceptible–infected–recovered–dead (SIRD) model of computer worms and analyzed the characteristics and mechanisms of worm transmission. We applied the ordinary differential equation model to simulate the transmission process of computer worms and estimated the unknown parameters of the SIRD model through the methods of least squares, Markov chain Monte Carlo, and ensemble Kalman filtering (ENKF). The results reveal that the proposed SIRD model is more accurate than the susceptible–exposed–infected–recovered–susceptible model with respect to parameter estimation.

Intrusion detection system based on bagging with support vector machine

<p>Due to their rapid spread, computer worms perform harmful tasks in networks, posing a security risk; however, existing worm detection algorithms continue to struggle to achieve good performance and the reasons for that are: First, a large amount of irrelevant data affects classification accuracy. Second, individual classifiers do not detect all types of worms effectively. Third, many systems are based on outdated data, making them unsuitable for new worm species. The goal of the study is to use data mining algorithms to detect worms in the network because they have a high ability to detect new types accurately. The proposal is based on the UNSW NB15 dataset and uses a support vector machine to train and test the ensemble bagging algorithm. To detect various types of worms efficiently, the contribution suggests combining correlation and Chi2 feature selection method called Chi2-Corr to select relevant features and using support vector machine (SVM) in the bagging algorithm. The system achieved accuracy reaching 0.998 with Chi2-Corr, and 0.989, 0.992 with correlation and chi-square separately.</p>

Toward Robust Monitoring of Malicious Outbreaks

Recently, diffusion processes in social networks have attracted increasing attention within computer science, marketing science, social sciences, and political science. Although the majority of existing works focus on maximizing the reach of desirable diffusion processes, we are interested in deploying a group of monitors to detect malicious diffusion processes such as the spread of computer worms. In this work, we introduce and study the [Formula: see text]-Monitoring Game} on networks. Our game is composed of two parties an attacker and a defender. The attacker can launch an attack by distributing a limited number of seeds (i.e., virus) to the network. Under our [Formula: see text]-Monitoring Game, we say an attack is successful if and only if the following two conditions are satisfied: (1) the outbreak/propagation reaches at least α individuals without intervention, and (2) it has not been detected before reaching β individuals. Typically, we require that β is no larger than α in order to compensate the reaction delays after the outbreak has been detected. On the other end, the defender’s ultimate goal is to deploy a set of monitors in the network that can minimize attacker’s success ratio in the worst-case. (We also extend the basic model by considering a noisy diffusion model, where the propagation probabilities on each edge could vary within an interval.) Our work is built upon recent work in security games, our adversarial setting provides robust solutions in practice. Summary of Contribution: Although the diffusion processes in social networks have been extensively studied, most existing works aim at maximizing the reach of desirable diffusion processes. We are interested in deploying a group of monitors to detect malicious diffusion processes, such as the spread of computer worms. To capture the impact of model uncertainty, we consider a noisy diffusion model in which the propagation probabilities on each edge could vary within an interval. Our work is built upon recent work in security games; our adversarial setting leads to robust solutions in practice.

A Fractional SAIDR Model in the Frame of Atangana–Baleanu Derivative

It is possible to produce mobile phone worms, which are computer viruses with the ability to command the running of cell phones by taking advantage of their flaws, to be transmitted from one device to the other with increasing numbers. In our day, one of the services to gain currency for circulating these malignant worms is SMS. The distinctions of computers from mobile devices render the existing propagation models of computer worms unable to start operating instantaneously in the mobile network, and this is particularly valid for the SMS framework. The susceptible–affected–infectious–suspended–recovered model with a classical derivative (abbreviated as SAIDR) was coined by Xiao et al., (2017) in order to correctly estimate the spread of worms by means of SMS. This study is the first to implement an Atangana–Baleanu (AB) derivative in association with the fractional SAIDR model, depending upon the SAIDR model. The existence and uniqueness of the drinking model solutions together with the stability analysis are shown through the Banach fixed point theorem. The special solution of the model is investigated using the Laplace transformation and then we present a set of numeric graphics by varying the fractional-order θ with the intention of showing the effectiveness of the fractional derivative.

Detection of Worms Over Cloud Environment

In recent years, computer worms are the remarkable difficulties found in the distributed computing. The location of worms turns out to be more unpredictable since they are changing quickly and much more refined. The difficulties in gathering worm's payload were recognized for identifying and gathering worm's payloads and the honey pot which is high-intelligent to gather the payload of zero-day polymorphic heterogeneous and homogeneous stages in distributed computing. The Signature-based discovery of worms strategies work with a low false-positive rate. We propose an irregularity based interruption location instrument for the cloud which specifically benefits from the virtualization advancements all in all. Our proposed abnormality location framework is detached from spreading computer worm contamination and it can recognize new computer worms. Utilizing our methodology, a spreading computer worm can be distinguished on the spreading conduct itself without getting to or straightforwardly affecting running virtual machines of the cloud.

Blakespotting

The chapter argues that the unpredictable viral behavior of William Blake’s proverbs in contemporary culture is critically and politically instructive. The widespread practice of citing Blake proverbs across various media platforms reveals the radical potential that Blake’s multi-media poetry possessed within the “original” historical contexts in which he wrote. Understanding the proverb form as a viral medium that spreads through a population’s contradictory desires for self-regulation illuminates proverbs’ centrality to Blake’s art and its challenge to the regulatory power of laws. The intellectual groundwork for this challenge lay in eighteenth-century practices of collecting national proverbs and in historical research into the Book of Proverbs. The chapter closes by analyzing how Blake’s proverbs relate to computer worms and also how they inform the ways that Jim Jarmusch’s film Dead Man laments America’s history of missed political opportunities.

FUZZY RECURRENT MAPPINGS IN MULTIAGENT SIMULATION OF POPULATION DYNAMICS SYSTEMS

The article deals with the problems of analyzing multi-agent models of population dynamics. The problems studied are caused by a number of uncertainties associated with variables, boundary conditions, initial states, parameter values, etc. Given problems could be found in tasks associated with cyber security of critical infrastructures (e.g. DDoS attacks, computer worms, etc.). To solve this problem, a linguistic fuzzy model has been developed, which allows describing systems of population dynamics in a more realistic way. Population dynamics is described by a set of rules, each of which involves entry and exit in the form of fuzzy sets or fuzzy functions, which are applied iteratively. The complexity of describing the processes of population dynamics systems, the presence of fuzzification and defuzzification algorithms, and the use of fuzzy sets and linguistic variables make it necessary to develop new methods for analyzing such systems. The approaches proposed in the article to the study of systems of population dynamics make it possible to apply a unified description of processes of different nature in the form of a production set of rules.