Biblio

In this cyber era, the number of cybercrime problems grows significantly, impacting network communication security. Some factors have been identified, such as malware. It is a malicious code attack that is harmful. On the other hand, a botnet can exploit malware to threaten whole computer networks. Therefore, it needs to be handled appropriately. Several botnet activity detection models have been developed using a classification approach in previous studies. However, it has not been analyzed about selecting features to be used in the learning process of the classification algorithm. In fact, the number and selection of features implemented can affect the detection accuracy of the classification algorithm. This paper proposes an analysis technique for determining the number and selection of features developed based on previous research. It aims to obtain the analysis of using features. The experiment has been conducted using several classification algorithms, namely Decision tree, k-NN, Naïve Bayes, Random Forest, and Support Vector Machine (SVM). The results show that taking a certain number of features increases the detection accuracy. Compared with previous studies, the results obtained show that the average detection accuracy of 98.34% using four features has the highest value from the previous study, 97.46% using 11 features. These results indicate that the selection of the correct number and features affects the performance of the botnet detection model.

The botnet is a serious network security threat that can cause servers crash, so how to detect the behavior of Botnet has already become an important part of the research of network security. DNS(Domain Name System) request is the first step for most of the mainframe computers controlled by Botnet to communicate with the C&C(command; control) server. The detection of DNS request domain names is an important way for mainframe computers controlled by Botnet. However, the detection method based on fixed rules is hard to take effect for botnet based on DGA(Domain Generation Algorithm) because malicious domain names keep evolving and derive many different generation methods. Contrasted with the traditional methods, the method based on machine learning is a better way to detect it by learning and modeling the DGA. This paper presents a method based on the Naive Bayes model, the XGBoost model, the SVM(Support Vector Machine) model, and the MLP(Multi-Layer Perceptron) model, and tests it with real data sets collected from DGA, Alexa, and Secrepo. The experimental results show the precision score, the recall score, and the F1 score for each model.

HTTP flood DDoS (Distributed Denial of Service) attacks send illegitimate HTTP requests to the targeted site or server. These kinds of attacks corrupt the networks with the help of massive attacking nodes thus blocking incoming traffic. Computer network connected devices are the major source to distributed denial of service attacks (or) botnet attacks. The computer manufacturers rapidly increase the network devices as per the requirement increases in the different environmental needs. Generally the manufacturers cannot ship computer network products with high level security. Those network products require additional security to prevent the DDoS attacks. The present technology is filled with 4G that will impact DDoS attacks. The million DDoS attacks had experienced in every year by companies or individuals. DDoS attack in a network would lead to loss of assets, data and other resources. Purchasing the new equipment and repair of the DDoS attacked network is financially becomes high in the value. The prevention mechanisms like CAPTCHA are now outdated to the bots and which are solved easily by the advanced bots. In the proposed work a secured botnet prevention mechanism provides network security by prevent and mitigate the http flooding based DDoS attack and allow genuine incoming traffic to the application or server in a network environment with the help of integrating invisible challenge and Resource Request Rate algorithms to the application. It offers double security layer to handle malicious bots to prevent and mitigate.

The ubiquitous nature of the Internet of Things (IoT) devices and their wide-scale deployment have remarkably attracted hackers to exploit weakly-configured and vulnerable devices, allowing them to form large IoT botnets and launch unprecedented attacks. Modeling the behavior of IoT botnets leads to a better understanding of their spreading mechanisms and the state of the network at different levels of the attack. In this paper, we propose a generic model to capture the behavior of IoT botnets. The proposed model uses Markov Chains to study the botnet behavior. Discrete Event System Specifications environment is used to simulate the proposed model.

The botnet-based network assault are one of the most serious security threats overlay the Internet this day. Although significant progress has been made in this region of research in recent years, it is still an ongoing and challenging topic to virtually direction the threat of botnets due to their continuous evolution, increasing complexity and stealth, and the difficulties in detection and defense caused by the limitations of network and system architectures. In this paper, we propose a novel and efficient botnet detection method, and the results of the detection method are validated with the CTU-13 dataset.

This paper dives into the growing world of IoT botnets that have taken the world by storm in the past five years. Though alone an IP camera cannot produce enough traffic to be considered a DDoS. But a botnet that has over 150,000 connected IP cameras can generate as much as 1 Tbps in traffic. Botnets catch many by surprise because their attacks and infections may not be as apparent as a DDoS, some other cases include using these cameras and printers for extracting information or quietly mine cryptocurrency at the IoT device owner's expense. Here we analyze damages on IoT hacking and define botnet architecture. An overview of Mirai botnet and cryptojacking provided to better understand the IoT botnets.

The spread of Internet of Things (IoT) devices in our homes, healthcare, industries etc. are more easily infiltrated than desktop computers have resulted in a surge in botnet attacks based on IoT devices, which may jeopardize the IoT security. Hence, there is a need to detect these attacks and mitigate the damage. Existing systems rely on supervised learning-based intrusion detection methods, which require a large labelled data set to achieve high accuracy. Botnets are onerous to detect because of stealthy command & control protocols and large amount of network traffic and hence obtaining a large labelled data set is also difficult. Due to unlabeled Network traffic, the supervised classification techniques may not be used directly to sort out the botnet that is responsible for the attack. To overcome this limitation, a semi-supervised Deep Learning (DL) approach is proposed which uses Semi-supervised GAN (SGAN) for IoT botnet detection on N-BaIoT dataset which contains "Bashlite" and "Mirai" attacks along with their sub attacks. The results have been compared with the state-of-the-art supervised solutions and found efficient in terms of better accuracy which is 99.89% in binary classification and 59% in multi classification on larger dataset, faster and reliable model for IoT Botnet detection.

A botnet is a new type of attack method developed and integrated on the basis of traditional malicious code such as network worms and backdoor tools, and it is extremely threatening. This course combines deep learning and neural network methods in machine learning methods to detect and classify the existence of botnets. This sample does not rely on any prior features, the final multi-class classification accuracy rate is higher than 98.7%, the effect is significant.

The internet has developed and transformed the world dramatically in recent years, which has resulted in several cyberattacks. Cybersecurity is one of society’s most serious challenge, costing millions of dollars every year. The research presented here will look into this area, focusing on malware that can establish botnets, and in particular, detecting connections made by infected workstations connecting with the attacker’s machine. In recent years, the frequency of network security incidents has risen dramatically. Botnets have previously been widely used by attackers to carry out a variety of malicious activities, such as compromising machines to monitor their activities by installing a keylogger or sniffing traffic, launching Distributed Denial of Service (DDOS) attacks, stealing the identity of the machine or credentials, and even exfiltrating data from the user’s computer. Botnet detection is still a work in progress because no one approach exists that can detect a botnet’s whole ecosystem. A detailed analysis of a botnet, discuss numerous parameter’s result of detection methods related to botnet attacks, as well as existing work of botnet identification in field of machine learning are discuss here. This paper focuses on the comparative analysis of various classifier based on design of botnet detection technique which are able to detect P2P botnet using machine learning classifier.

This paper proposes a new strategy, named resident strategy, for defending IoT networks from repeated infection of malicious botnets in the Botnet Defense System (BDS). The resident strategy aims to make a small-scale white-hat botnet resident in the network respond immediately to invading malicious botnets. The BDS controls the resident white-hat botnet with two parameters: upper and lower number of its bots. The lower limit prevents the white-hat botnet from disappearing, while the upper limit prevents it from filling up the network. The BDS with the strategy was modeled with agent-oriented Petri nets and was evaluated through the simulation. The result showed that the proposed strategy was able to deal with repeatedly invading malicious botnets with about half the scale of the conventional white-hat botnet.