For the real-time environment, protecting against threats is a sufficient task and minimizing the false alarm rate is another inevitable part of the cyber defense mechanism. Providing security to a network or organization is becoming arduous with time due to the increasing traffic complexity. According to the Cisco reported by cyber defense magazine, the trend in the growth of ransomware attack is 350% annually and the expected expenditures on cyber-security are to reach $1 trillion by 2024. The cost of ransomware is spiked to US$ 20 Billion against US$ 11.5 Billion in 2019. The phishing attack variants have the highest occurrence followed by malware/ ransomware attacks. Reports from show the rise in different types of cyber threats during the COVID-19 pandemic. As per the report published in, during the COVID-19 crisis, attackers targeted consumers and enterprises through a themed attack. Attackers are looking for every possible way to execute their malicious intent. Today, the world is going through a COVID-19 pandemic. Detecting malicious activities and offering a secure environment against the Internet’s sophisticated traffic of a diverse set of users are the top priorities of security firms. A person sitting at one end can easily access others’ information at different ends within a fraction of a second due to the Internet’s globalization. It makes a more significant proportion of the population dependent on the Internet for their daily activities (e.g., gaming, shopping, chatting, financial activities, study, etc.), making them prone to several threats and attacks. The digitization of service and other activities turned the Internet into an inevitable part in various tasks. Thus, the proposed approach shows an encouraging result to detect zero-day attacks. The performance against benchmark data set CICIDS18 shows a promising result of 91.62% for binary-class classification on this model. The result analysis of the proposed zero-day attack detection shows higher performance for accuracy of 91.33% for the binary classification and accuracy of 90.35% for multi-class classification on real-time attack data. This model evaluates the performance using generated signatures at the training phase. The proposed work consists of two phases ( a) Signature generation and ( b) Evaluation phase. This paper proposes a novel robust and intelligent cyber-attack detection model to cover the issues mentioned above using the concept of heavy-hitter and graph technique to detect zero-day attacks. Covering attacks that produce lower traffic is difficult through neural network models because it requires higher traffic for correct prediction. Detecting zero-day attacks through these techniques miss several parameters like frequency of particular byte streams in network traffic and their correlation. Existing approaches either uses ML/DNN or anomaly-based approach to protect against these attacks.
Dos locate zero byte file software#
Zero-day attack(s) targeting unknown vulnerabilities of a software or system opens up further research direction in the field of cyber-attacks. Example import java.io.With the introduction of the Internet to the mainstream like e-commerce, online banking, health system and other day-to-day essentials, risk of being exposed to various are increasing exponentially. This method returns an integer representing the total number of bytes or, -1 if the end of the file is reached.Īssume we have the following image in the directory D:/imagesįollowing program reads contents of the above image using the FileInputStream.int read(byte b, int off, int len) − This method accepts a byte array, its offset (int) and, its length (int) as parameters and reads the contents of the current InputStream, to the given array.This method returns an integer representing the total number of bytes or, -1 if the end of the file is reached. Int read(byte b) − This method accepts a byte array as parameter and reads the contents of the current InputStream, to the given array. This method returns -1 if the end of the file is reached. Int read() − This simply reads data from the current InputStream and returns the read data byte by byte (in integer format). Then read the contents of the specified file using either of the variants of read() method −.First of all, you need to instantiate this class by passing a String variable or a File object, representing the path of the file to be read.įileInputStream inputStream = new FileInputStream("file_path") įileInputStream inputStream = new FileInputStream(file).To read the contents of a file using this class − It is usually used to read the contents of a file with raw bytes, such as images. The FileInputStream class reads the data from a specific file (byte by byte).
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