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We follow the same procedure as discussed in P2P data transmission. We keep track of the number of small packet size and the ratio of the equal small packet size.
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The research design is divided in two phases. The first phase consists of the collection of malware signatures by using honeypot. These malware binaries will be used to infect the VMware host in order to monitor their activities.
After the bot process is identified in the monitored host, the file associated with this process will be scanned by the administrator. This file can provide valuable information about the C&C server IP address and how the communication is established between the bot (the compromised machine) and the C&C server. Moreover, the administrator can know how the botnet is propagated and infect other machines. This knowledge permits to the administrator to, in one hand, block all the incoming and outgoing traffic with the C&C server, and, in another hand, improve the overall detection mechanism by exploiting these information to build new rules.
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This subsection presents the laboratory components used for the experiment of this research. These components are shown in Figure 1.
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In our experiment, when host process is infected with Rbot, the Rbot process injects the svchost process (windows service). The svchost performs DNS query to obtain the C&C server IP address. Then, the Rbot process establishes TCP connection to this address. Rbot injects the svchost process with malicious code. The host analysis does not cover any process injection methods. Therefore, the Rbot process will evade our host analysis analyzer. However, if we use both levels host analyzer and network analyzer, this bot will be detected by the network analyzer. In fact, the detection accuracy rate is increased, if we combine the network and the host analyzer. In this case, all the flows are classified correctly in the right class (botnet or legitimate).
Zhao et al. propose an IDns system to detect malicious domain names in the C&C server for APT (advanced persistent threat) attack. The proposed system is deployed at the network side which can reduce the network traffic volume that has to be registered then analyzed.
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The bot in the botnet is a bad (malicious) software that is run on the victim’s machine without his knowledge. The attacker who owns the bot takes control of the machine and formed, with other bots, a network of infected machines. The attacker is known as botnet master. The botnet master uses special mechanism to communicate with these bots and exchange commands through the botnet. This mechanism called Command and Control server (C&C).
The 14 features that are studied for malicious DNS detector in this approach are divided into 5 categories: domain name features, DNS answer features, time value features, TTL value-based features, and active probing features. This botnet detection can detect only malwares that depend on DNS. Hence, P2P botnet cannot be detected.
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We can detect IP fluxing in DNS by keeping track of two artifacts: IP address assigned to domain and domain cache TTL. Every time the host sends a query for specific domain, we keep track of the last IP assigned (if there), the new one and the domain cache TTL. In fact, if newest IP address is not in the same class subnet of the previous IP and cache TTL period is less than 1000, then this domain is suspicious. Indeed, normal domain assigns multiple IP addresses in the same class subnet and long domain cache TTL period.
To mitigate against such sophisticated bots whose one process malicious activity is not considered as a bot, we take into account process correlation during the monitoring. We keep track of the interprocess relations and aggregate the behavior vector from the processes correlation. The obtained behavior vectors are equal for all the correlated processes like a parent process and its children processes. We can easily exclude some normal process with correlation such as windows services by maintaining a white list. Therefore, it is not hard to differentiate a set of processes behaving malicious altogether. This means that these processes have exactly the same behavior vectors.
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Using antivirus software and a firewall to protect the host are not enough to prevent its infection by botnet malware. Moreover, even if we can stop the C&C server, the infected host (with bot) can be launched again into future attack. In this case, we need a host-based detection to eliminate the bot program from the host. There are various researches in host-based detection techniques [9–11]. Huang proposes an effective bot host detection solution based on network failure tracking in a host during short period. The solution consists of two phases: (1) training phase and (2) detection phase. The first phase is used to extract features from failure flows. The second phase analyses the data based on knowledge obtained during training phase using C4/5 algorithm. In , Etemad and Vahdani deal with the detection of C&C centralized botnet by using host analysis. Their solution is based on incoming and outgoing host real traffic analyses.
The four first artifacts are used to determine packets belonging to the same flow. The artifact numbers five and six are used to compute the failed connection ratio feature presented in Section 4/1.1. The last five artifacts are used as follows.
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Therefore, we evaluate the second classifier by eliminating these two features from the behavior vector. The second classier decreases the false positive rate and increases the accuracy rate.
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The target of monitoring the infected host, using sniffer tool (wireshark) and malware analysis tools (process monitor and process explorer), is to be able to extract the communication pattern characteristics between the C&C server and this host. After that, we use these tools to analyze the collected malware behavior and gather behavior information artifacts that we need.
The characteristics needed for our features vector analysis can be extracted directly from the network flow record. For each flow record, we first determine the traffic type then compute the features needed based on the traffic type.
It consists of a physical computer (the host) with Linux as an operating system. On this host, we install a VM with Windows 7 as an operating system. The VM will be the target of the attack in this research, and the Linux operating system is the controller on which honeypot is installed. For a dynamic analysis, different tools will be used to carry out the experiment:(i)Honeypot: this tool is used to collect malware samples from the Internet. In fact, the collection of the malwares using this tool is the safest way. The physical computer is connected to the Internet (https://zhilservis-rzn.ru/free/?key=9484) directly to expose the computer vulnerabilities and hence attracts attackers.
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The second phase of this research consists of the development of an algorithm (using python) to extract needed artifacts. Then, we perform preprocessing steps to collect features which conduct to the final decision. This final decision will be produced using machine learning algorithm using Knime.
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In our analysis, we extract the common botnet behavior. We focus on software process run-time behavior vector including registry modification, file system creation, and network traffic. The host process network traffic analyzing and detection is performed and followed the procedure explained in Section 4. In this section, we explain the features that are used by our technique to analyze the monitored registry, file system, and active time of processes in the host.
To avoid data bias, multiple metrics are measured. Accuracy, precision, recall, F-measure, and false positive rate are the five metrics used to evaluate the performance of our solution. Accuracy depends on the sample size. However, precision and recall measures are independent of the sample size.
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On the other hand, this research is dealing with a malicious activity that can threat the security of the researcher’s network. Therefore, we need first to consider the security of the researcher’s network. The solution is to use a demilitarized zone (DMZ) on the physical machine that collects malwares. This DMZ provides a honeypot in an isolated environment to capture all the malicious traffic in experimental machine.
In fact, eMule generates very small failure connection rate. This is due to the use of a “bad peer source list,” which will be added to the application. This list is updated with the peers IP addresses when the eMule application fails to connect with these peers IP addresses. Consider this peer source list of IP addresses as dead and block for predefined time (15 to 45 minutes). Even if eMule application behaves similar to bot in terms of failed connection rate, eMule is not detected by our technique since we check also equal small packet size feature which is not satisfied in this case.
This paper is organized as follows. Section 2 gives an overview of some research works proposed for botnet detection. Section 3 describes our methodology to reach our objectives and the laboratory experimental setting. Section 4 introduces the proposed technique, HANABot, and its components. Section 5 presents HANABot performance and a comparison with some previous solutions. Finally, we conclude our paper in Section 6 and recommend some future works.
Using this procedure, the process final score will not decrease if the process does one malicious activity in single time window. Every window time, the process conducts malicious activity and its score will increase. The same concept is applied to a set of processes when each one shows malicious activity in various time windows.
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In this stage, we filter out all irrelevant network traffic flows. Notice that this filtering step is just an optional procedure and not critical to our network analyzer. The purpose of this step is to reduce the computational cost by reducing the total number of flows. Then, the network traffic is split into different categories (HTTP traffic, IRC traffic, etc). In each category, all packets that belong to the same flows are extracted. Next, our HANABot algorithm extracts all the artifacts presented in Table 3 from these flows. After that, the HANABot algorithm preprocesses these extracted artifacts to obtain the relevant features of each flow. The preprocessing is applied on the network traffic to collect valuable information that based on the decision is made at the end. In the following, we present HANABot preprocessing steps.
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One of the hot research topics is the botnet detection. Many researches in the literature focus on botnet detection techniques. Many of these techniques are focused only on specific botnet type. Botnet behavior-based detection approaches can be classified into three classes: host-based detection, network-based detection, and hybrid detection. Here, we briefly describe some of the previous researches that propose different solutions to detect botnet.
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Table 10 shows a comparison of the two classifier performances at host level. The first classifier uses all selected features. The performance of this classifier generates high false positive rates and low accuracy rate. This is due to two features that are common between normal P2P application and botnet. These features are as follows: create and set the value of AutoRun key in the registry and Active time of the bot process.
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Naive Bayesian classifier (NB) represents a supervised learning method and a statistical technique for classification. It assumes an implicit probabilistic model. It allows to capture and analyze the relationship between each feature and the corresponding class to derive a conditional probability that links feature values and its class.
Figure 4 shows the network analyzer components. In the following, we present each component in more details.
Delivering e-mail spam procedure is same as delivering legitimate e-mail, utilizing Simple Mail Transfer Protocol (SMTP) on port 25 . The botnet master uses botnet for diffusing spam e-mails at a large scale. The senders of e-mails may belong to different botnet networks and also they may serve different botnet masters for different purposes and this spam emails can have different contents. We keep track of the number of SMTP packets issued from the sender IP address. Every time a SMTP packet is tracked, a variable flag is_SMTP is set to 1. Then after 120 seconds passes, we sum up the is_SMTP value for each sender in the monitored network.
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As malicious bot software spreads, the demand for an automated alternative is increased because manually generated rules become impractical. Machine learning algorithms allows the detection systems to fit fast changing malware. Moreover, it allows an automated method for learning specific patterns and properties that may be presented by botnet.
Alauthaman et al. , propose a P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks. This framework passively monitors the network traffic to detect the botnet communication with the C&C server and between bots. First, network traffic reduction is done to manage the enormous amount of network traffic. Then, 29 features are proposed for botnet detection. Features with small influence on classification model are eliminated by using the feature reduction approach: the classification and regression tree (CART). The target of this feature reduction is to keep only worthy features to obtain better rates of neural network learning and classification accuracy. The evaluation of this approach shows that it gives very good accuracy with 99/2% and outperforms some existing solutions.
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Botnet detection may be implemented at the network level or at the host level. Botnet detection at the network level plays a critical role in security by monitoring the network traffic and providing warning to the network administrator when any unusual event is detected. On the other hand, the detection at the host level plays a crucial role in the detection of malware infection by monitoring files system modification, registry modification, and network traffic on the host.
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