In this context, an analogy can be made between the spread of a virus or a worm in a computer network and the spread of diseases in human beings as part of a pandemic. Some examples of malware campaigns are the WannaCry attack, carried out in May 2017 and estimated to have infected more than 200,000 computers in 150 countries, and the ILOVEYOU attack, which in May 2000 infected about 10% of the global internet-connected computers, causing global economy damages of up to $8 billion. This situation makes a worm more infectious than a virus, but not necessarily more dangerous. For example, the primary difference between a virus and a worm is that a virus is triggered and spread through the intervention of the victim, whereas a worm is a stand-alone malicious program that can self-replicate and propagate through a network without requiring any human intervention. When conducting a study on how malware impacts a computer network, it is crucial to identify the type of malware that is being dealt with and its behavioral characteristics.ĭifferent types of malware exist on the Internet, such as Trojan, spyware, ransomware, virus, worm, rootkit, among others, each having special characteristics that make them unique in the way they operate. A set of activities carried out by threat agents using specific techniques for some particular purpose is called a malware campaign. Such malicious activity is performed by a threat agent, who may be an individual actor or even an organized crime group. #Let it die pc malware softwareThe term malware refers to any software intentionally designed to cause damage to a computer, server, or computer network. Malware attacks are currently a serious concern on computer networks and IoT devices, although they are not a new phenomenon. In addition, we describe both offensive and defensive techniques, based on the proposed MalSEIRS model, through extensive experimentation, as well as disclosing real-life malware campaigns that can be better understood by using the suggested model. As a result, the time-dependent rates of infection, recovery, and loss of immunity enable us to capture the complex dynamism of malware spreading behavior, which is influenced by a variety of external circumstances. In this context, the paper at hand proposes MalSEIRS, a novel dynamic model, to predict malware distribution in a network based on the SEIRS epidemiological model. However, malware attacks have also widely proliferated, mainly aiming at legitimate network users, resulting in millions of dollars in damages if proper protection and response measures are not settled and enforced. Over the last few decades, the Internet has brought about a myriad of benefits to almost every aspect of our daily lives.
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