Distributed Denial of Service (DDoS) attacks are performed from multiple agents towards a single victim. Essentially, all attacking agents generate multiple packets towards the victim to overwhelm it with requests, thereby overloading the resources of the victim. Since it is very complex and expensive to conduct a real DDoS attack, most organizations and researchers result in using simulations to mimic an actual attack. The researchers come up with diverse algorithms and mechanisms for attack detection and prevention. Further, simulation is good practice for determining the efficacy of an intrusive detective measure against DDoS attacks. However, some mechanisms are ineffective and thus not applied in real life attacks. Nowadays, DDoS attack has become more complex and modern for most IDS to detect. Adjustable and configurable traffic generator is becoming more and more important. This paper first details the available datasets that scholars use for DDoS attack detection. The paper further depicts the a few tools that exist freely and commercially for use in the simulation programs of DDoS attacks. In addition, a traffic generator for normal and different types of DDoS attack has been developed. The aim of the paper is to simulate a cloud environment by OMNET++ simulation tool, with different DDoS attack types. Generation normal and attack traffic can be useful to evaluate developing IDS for DDoS attacks detection. Moreover, the result traffic can be useful to test an effective algorithm, techniques and procedures of DDoS attacks. eration occurs in two stages, namely the compromise stage and the attack stage.An attacker will compromise available defenseless systems and install attack tools, thereby turning the machines into zombies. The second stage involves sending attack commands into the zombie machines via a secure mechanism so as to target a specific victim [1]. Cyber security experts and other researchers are faced with the challenges of unraveling DDoS attack vectors as well as ways to prevent such attacks. The scholars conduct attack simulation using either real data or simulated data based on previous attack characteristics. Simulation involves tools that have attack agents and defense agents. Attack agents are the daemon which is attack executors and master which is the attack coordinator.Defense agents are the sensors, samplers, detectors, filters and investigators [2]. Journal of Information Security over, the network traces that results from such interaction are also collected to conduct anomaly detection. In particular, this experiment is performed using Amazon web services (AWS) platform. It explores the generation of labeled datasets for quantifying the security threats impact to cloud data centers. Among the researchers, the detection of instruction is an exciting topic. Specifically, the discovery of anomaly is one of the vital factors that help in detecting several novel attacks. Due to the complexity of these systems, however, its application has not been appropriate.
A Distributed Denial of Service Attack (DDoS) is an attack in which multiple systems compromised by a Trojan are maliciously used to target a single system. The attack leads to the denial of a certain service on the target system. In a DDoS attack, both the target system and the systems used to perform the attack are all victims of the attack. The compromised systems are also called Botnets. These attacks occur on networked systems, among them the cloud computing facet. Scholars have tried coming up with separate mechanisms for detecting and preventing such attacks long before they occur. However, as technology progresses in advancement so do the attack mechanisms. In cloud computing, security issues affect various stakeholders who plan on cloud adoption. DDoS attacks are such serious concerns that require mitigation in the cloud. This paper presents a survey of the various mechanisms, both traditional and modern, that are applied in detecting cloud-based DDoS attacks.
Percentage mammographic breast density (MBD) is one of the most notable biomarkers. It is assessed visually with the support of radiologists with the four qualitative Breast Imaging Reporting and Data System (BIRADS) categories. It is demanding for radiologists to differentiate between the two variably allocated BIRADS classes, namely, “BIRADS C and BIRADS D.” Recently, convolution neural networks have been found superior in classification tasks due to their ability to extract local features with shared weight architecture and space invariance characteristics. The proposed study intends to examine an artificial intelligence (AI)-based MBD classifier toward developing a latent computer-assisted tool for radiologists to distinguish the BIRADS class in modern clinical progress. This article proposes a multichannel DenseNet architecture for MBD classification. The proposed architecture consists of four-channel DenseNet transfer learning architecture to extract significant features from a single patient's two a mediolateral oblique (MLO) and two craniocaudal (CC) views of digital mammograms. The performance of the proposed classifier is evaluated using 200 cases consisting of 800 digital mammograms of the different BIRADS density classes with validated density ground truth. The classifier's performance is assessed with quantitative metrics such as precision, responsiveness, specificity, and the area under the curve (AUC). The concluding preliminary outcomes reveal that this intended multichannel model has delivered good performance with an accuracy of 96.67% during training and 90.06% during testing and an average AUC of 0.9625. Obtained results are also validated qualitatively with the help of a radiologist expert in the field of MBD. Proposed architecture achieved state-of-the-art results with a fewer number of images and with less computation power.
Probabilistic graphical models are employed in a variety of areas such as artificial intelligence and machine learning to depict causal relations among sets of random variables. In this research, we employ probabilistic graphical models in the form of Bayesian network to detect coronavirus disease 2019 (denoted as COVID-19) disease. We propose two efficient Bayesian network models that are potent in encoding causal relations among random variable, i.e., COVID-19 symptoms. The first Bayesian network model, denoted as BN1, is built depending on the acquired knowledge from medical experts. We collect data from clinics and hospitals in Saudi Arabia for our research. We name this authentic dataset DS covid . The second Bayesian network model, denoted as BN2, is learned from the real dataset DS covid depending on Chow-Liu tree approach. We also implement our proposed Bayesian network models and present our experimental results. Our results show that the proposed approaches are capable of modeling the issue of making decisions in the context of COVID-19. Moreover, our experimental results show that the two Bayesian network models we propose in this work are effective for not only extracting casual relations but also reducing uncertainty and increasing the effectiveness of causal reasoning and prediction.
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