University Cyber Attack Courseend Project 1 DESCRIPTION Yo.pdf

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University Cyber Attack. Course-end Project 1 DESCRIPTION You are a cyber security officer and member of the Incident Response Team. During the summer vacation, one of the teaching staff members, Samantha, reports to the Dean about abusive and threatening messages received over an email. Dean collects the following details from her: Complete Name: Antha R. Coll. Personal Email ID, Official Email ID. Antha also reported that during the term examination, she obstructed one of the students, Tony Lee, due to unfair means during examination. As an investigator, your task is to identify the following: Task 1: Obtain a scanning report of the entire network and identify how many terminals are connected with the Windows operating system and the Linux-based systems. Note: Learners can use any platform like Kali Linux. Task 2: Identify CVE score of the victims vulnerability. Note: Learners can use any open-source data sets for vulnerability like NVD (National Vulnerability Database). Task 3: Identify whether the victims terminal is affected with MiMT attack or not and submit the incident report for the same. Note: Learners can orchestrate any attacks like Denial-of-service attack and create reports based on it. Task 4: Use email forensics analysis and identify the senders IP address Note: Learners can create a dummy email ID, perform this task, or send an email to anyone. They can identify the sender's IP address. Task5: Submit the complete incidence report.

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For effective security incidence response on the network, a reputable approach must be in place at both protected and unprotected region of the network. This is because compromise in the demilitarized zone could be precursor to threat inside the network. The improved complexity of attacks in present times and vulnerability of system are motivations for this work. Past and present approaches to intrusion detection and prevention have neglected victim and attacker properties despite the fact that for intrusion to occur, an overt act by an attacker and a manifestation, observable by the intended victim, which results from that act are required. Therefore, this paper presents a threat characterization model for attacks from the victim and the attacker perspective of intrusion using data mining technique. The data mining technique combines Frequent Temporal Sequence Association Mining and Fuzzy Logic. Apriori Association Mining algorithm was used to mine temporal rule patterns from alert sequences while Fuzzy Control System was used to rate exploits. The results of the experiment show that accurate threat characterization in multiple intrusion perspectives could be actualized using Fuzzy Association Mining. Also, the results proved that sequence of exploits could be used to rate threat and are motivated by victim properties and attacker objectives.

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By now you've probably heard about new ransomware threats like CryptoWall, which encrypts your data and demands payment to unlock it. These threats are delivered via malicious email attachments or websites, and once CryptoWall executes and connects to an external command and control server, it starts to encrypt files throughout your network. Therefore, spotting infections quickly can limit the damage. Don’t fall victim to ransomware! AlienVault USM uses several built-in security controls working in unison to detect ransomware like CryptoWall, usually as soon as it attempts to connect to the hackers’ command and control server. How does it all work? Join us for a live demo that will show how AlienVault USM detects these threats quickly, saving you valuable clean up time by limiting the damage from the attack. You'll learn: How AlienVault USM detects communications with the command and control server How the behavior is correlated with other signs of trouble to alert you of the threat Immediate steps you need to take to stop the threat and limit the damage

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As malware continues to grow more sophisticated and more plentiful - traditional signature and heuristics-based defenses no longer cut it. Instead, the industry has recently turned to using machine learning for malicious file detection. The challenge with this approach is that machine learning itself comes with vulnerabilities - and if left unattended presents a new attack surface for attackers to exploit. In this paper we present a survey of research in the area of machine learning-based malware classifiers, the attacks they encounter, and the defensive measures available. We start by reviewing recent advances in malware classification, including the most important works using deep learning. We then discuss in detail the field of adversarial machine learning and conduct an exhaustive review of adversarial attacks and defenses in the field of malware classification.

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Understanding the term hacking as any unconventional way of interacting with some system it is easy to conclude that there are enormous number of people who hacked or tried to hack someone or something. The article, as result of author research, analyses hacking from different points of view, including hacker's point of view as well as the defender's point of view. Here are discussed questions like: Who are the hackers? Why do people hack? Law aspects of hacking, as well as some economic issues connected with hacking. At the end, some questions about victim protection are discussed together with the weakness that hackers can use for their own protection. The aim of the article is to make readers familiar with the possible risks of hacker's attacks on the mobile phones and on possible attacks in the announced food of the internet of things (next IoT) devices

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Which of the following is consistent with Leviathan theory .pdf

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Utilice el caso "Encontrar el camino hacia la excelencia operativa en Zeni" para responder a esta pregunta. El Gerente General Patricio Zeni le ha pedido que analice los datos de rendimiento operativo disponibles y le proporcione la evidencia que necesita para equilibrar el sistema de produccin y aumentar el cumplimiento del nivel de servicio. Suponiendo que usted es el responsable del rea de produccin de ZENI, qu indica su anlisis de los datos presentados al respecto? Caso a continuacin A principios de enero de 2012, Patricio Zeni, Gerente General de UFI (Unidad Industria Forestal), divisin de ZENI, terminaba en su oficina luego de un da sofocante en la planta de la empresa en Esquina, localidad de Corrientes, provincia del noreste argentino. Entre otras cosas, se haba reunido durante casi tres horas con Pablo Costa, el Gerente de Planta. Los mrgenes de la UFI no eran tan buenos como solan ser. La economa argentina, con una inflacin anual promedio entre 15% y 20% y un tipo de cambio rezagado (Anexo 1), haba elevado los costos y los precios de los productos se fijaban en los mercados externos que no compensaban los efectos de la poltica monetaria del pas. Con todo esto en mente, Patricio estaba muy preocupado porque otro ao con resultados negativos dejara a la empresa casi fuera de un negocio que histricamente haba sido rentable. Despus de revisar un informe sobre el nivel de calidad del servicio de ZENI (Anexo 2), Patricio encontr una nueva fuente de preocupacin: la entrega del producto no fue tan oportuna como debera haber sido. Pero nada de esto podra abordarse sin tener en cuenta el aumento de la rentabilidad. Patricio reflexion Aunque creo que estamos haciendo lo que tenemos que hacer, deberamos estar obteniendo ms ganancias de este negocio. Podemos encontrar oportunidades para mejorar nuestras prcticas de produccin? Seremos capaces de introducir algunos cambios para mejorar nuestra competitividad? ANTECEDENTES DE ZENI Fundada por Enrique R. Zeni, ZENI comenz a operar en 1940 como una pequea comercializadora de granos.1 Creci de manera constante a lo largo de los aos, convirtindose en una de las comercializadoras de granos y semillas oleaginosas ms grandes de Argentina con un profesionalismo cada vez mayor para brindar servicios de valor agregado. Para impulsar el crecimiento de la empresa, Carlos Zeni, hijo del fundador, se incorpor a su equipo directivo y, aos ms tarde, sus tres hijos Patricio, Marina e Ignacio tambin entraron a trabajar all. Ignacio administraba el negocio de corretaje de ZENI, mientras que Marina estaba a cargo del negocio de produccin de ctricos y ganado, y Patricio encabezaba la UFI de ZENI. Carlos particip en las decisiones de gestin de estas unidades, especialmente en la ganadera y la UFI. De hecho, viajaba con frecuencia a Corrientes, como l deca, para estar al tanto de lo que pasa en nuestro negocio Adems, Patricio realizaba frecuentes viajes a la sede de ZENI en Buenos Aires para administrar la empresa con su .

Utilice el caso Encontrar el camino hacia la excelencia ope.pdf

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