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As the Internet is growing – so is the vulnerability of the network. Companies now days are spending huge amount of money to protect their sensitive data from different attacks that they face. DoS or Denial Of Service attacks are one of such kind of attacks. In this paper, we at first recognize different kinds of DoS attacks and then propose a new...
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... receiver operating characteristics (ROC) was found to be as expected consisting mostly of true positives and fewer numbers of false positives. Figure 8, figure 9 and figure 10 show the details of confusion matrix and receiver operating characteristics respectively. Table I includes the success and error rates of level 2 and level 3 testing as well. ...
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This paper presents the main results of an investigation of road disruptions generated within the Metropolis of La Paz by two events: a natural one and a social-related one. The first one was produced by a strong hailstorm on the 19th of February 2002, and the second one, called «The Gas War», resulted from riots and road blockades in October 2003....
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... The study adopted the RNN-LSTM design, following five stages: data collection, preprocessing of data, building a network, training a network, and testing a network, in (Mukhopadhayay et al., 2014). The selected study area represented upper, middle, and lower-class residents, and hence, the results of the study can be fairly generalized. ...
Real estate refers to a class of real property such as land and its associated infrastructure. The prediction of real estate prices in cities, which is affected by a number of parameters, is an open research problem. The lack of reliable and effective tools for price forecasting in real estate, especially in residential housing, can adversely affect investment flows and the growth of the real estate sector. Taking Tanzania as an example, the price prediction practices rely on human suggestions that are prone to personal bias and subjective to price hysteria for personal gain and impact consumer expectations. To address the challenge, this paper designed a real estate price trend prediction model for the cities using Recurrent Neural Networks (RNN) with a Long Short-Term Memory (LSTM). The study identified the factors influencing real estate property prices, including size, location, time, property quality, accompanied services, market nature, price of land, cost of building materials, and value for money. However, the study spotted the size, price, location, and time as key factors in predicting price trends when using RNN-LSTM. The results show that the proposed RNN-LSTM model performed better with 50% MSE less compared to the Convolutional Neural Network (CNN). In computing the price trend per location, the model prediction accuracy was 97.45%, 79.23%, and 53.8% for the high class, middle class, and low class, respectively, resulting in an average prediction accuracy of 76.8%
... The most efficient and adaptable method of threat detection now is the NetFlow analyzer [6]. Some apps and devices may collect IP traffic data on their overlays and send it to NetFlow for assessment thanks to the NetFlow technology, an existing IP traffic evaluation, review, and analysis benchmark on the internet. ...
... • Software installation without security consideration. • There are no checks or data analyses carried out [4]. ...
As the internet grows and diversity, attackers use various attacks to crash the servers and to stop specific sites. Multiple computers and multiple Internet connections are targeted by using distributed denial of service (DDoS) attacks. The aim of this paper is to identify the best algorithm among the selected algorithms (i.e., gradient descent with momentum algorithm, scaled conjugate gradient, and variable learning rate gradient descent algorithm. In this study, the recurrent neural network was trained to check the accuracy and detection of DDoS attacks. The intention of this training was to allow the system to learn and classify the input traffic into the category. The proposed system's training was composed of three separate algorithms utilizing recurrent neural networks. The MATLAB 2018a simulator was used for training purpose. Moreover, clean the Knowledge Discovery Dataset (KDD) during design and include the values of protocols, attacks, and flags. The neural network model was subsequently developed, and the KDD was trained using Artificial Neural Network (ANN). The results of DDoS attacks’ detection were analyzed using MATLAB's ANN toolbox. The success rate of the variable learning rate gradient descent algorithm was 99.9% accuracy and the short timing was 2 minutes and 29 seconds. The variable learning rate gradient descent algorithm gives better results than gradient descent with momentum and scaled conjugate gradient algorithms. In the state of the art, different algorithms have been trained in different neural networks and different KDD datasets by using selective DDoS attacks but in this research recurrent neural network was used for three different algorithms. In this research, we have used total of 22 attacks for detection of DDoS attacks’ accuracy.
... In this attack a single attacker bombards the target with a large amount of data packets (for any protocol e.g., HTTP etc.) to consume its resources with this illegitimate traffic making it unavailable for legitimate ones. A lot of research has been carried out to detect such attacks using simple machine learning and deep learning approaches [35]. ...
Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic areas for researchers to protect network infrastructure from adversary activities. Several network systems suffer from drawbacks that allow intruders to use malicious traffic to obtain unauthorized access. Attacks such as Distributed Denial of Service attacks (DDoS), Denial of Service attacks (DoS), and Service Scans demand a unique automatic system capable of identifying traffic abnormality at the earliest stage to avoid system damage. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not only the issue with current Intrusion Detection Systems (IDS), but the efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. Thus, this study concentrates on constructing an ensemble classifier using the proposed Integrated Evaluation Metrics (IEM) to determine the best performance of IDS models. The automated Ranking and Best Selection Method (RBSM) is performed using the proposed IEM to select the best model for the ensemble classifier to detect highly accurate attacks using machine learning and deep learning techniques. Three datasets of real IoT traffic were merged to extend the proposed approach’s ability to detect attack traffic from heterogeneous IoT networks. The results show that the performance of the proposed model achieved the highest accuracy of 99.45% and 97.81% for binary and multi-classification, respectively.
... Implementation of the confusion matrix that measures detection performance is the best option as this matrix has a wide range of measurement of detection. e matrix has also been utilised by many researchers [38][39][40][41][42][43][44][45]. ...
With increment in dependency on web technology, a commensurate increase has been noted in destructive attempts to disrupt the essential web technologies, hence leading to service failures. Web servers that run on Hypertext Transfer Protocol (HTTP) are exposed to denial-of-service (DoS) attacks. A sophisticated version of this attack known as distributed denial of service (DDOS) is among the most dangerous Internet attacks, with the ability to overwhelm a web server, thereby slowing it down and potentially taking it down completely. This paper reviewed 12 recent detection of DDoS attack at the application layer published between January 2014 and December 2018. A summary of each detection method is summarised in table view, along with in-depth critical analysis, for future studies to conduct research pertaining to detection of HTTP DDoS attack.
This project deals with enterprises and individuals to have security awareness and utilizes the assessment tool by following the MITRE ATT&CK framework techniques. A penetration attack is done on two common virtual machines that are used globally for everyday use; testing the windows and Linux operating systems for comparison and identify the effectiveness of security and determine the security weakness. Emphasizing all companies are not secure as they think they might be. In our investigation, the objective is to pentest with the following tools: LEGION, Rapid 7 Metasploitable and Mandiant Advantage also using SNORT and Wireshark as the network intrusion detection to capture events and logs. The experiment involves testing a sample size of the MITRE ATT&CK matrix; that is reconnaissance, lateral movement, credential access, discovery, and Execution tactics. The vulnerabilities and weaknesses found will be showcased in a security report manner and will establish business impact with risk rating along with how to overcome and mitigate those vulnerabilities.
