Storm botnet dataset
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Software Defined Network separates the control plane from network equipment and has great advantage in network management as compared with traditional approaches. With this paradigm, the security issues persist to exist and could become even worse because of the flexibility on handling the packets. In this paper we propose an effective framework by integrating SDN and machine learning to detect and categorize P2P network traffics.
This work provides experimental evidence showing that our approach can automatically analyze network traffic and flexibly change flow entries in OpenFlow switches through the SDN controller. This can effectively help the network administrators manage related security problems. There are many peer-to-peer P2P network traffics in the Internet.
Through P2P, cyber threats caused by botnets have significantly increased in recent years. Attackers can use botnet to construct various malicious activities. In order to enforce appropriate network management and security policies, we need to detect botnet while they are communicating instead of when the attacks have already happened.
As a result, it is an important and storm botnet dataset task for network administrators to identify and categorize P2P traffic types. Botnets represent a collaborative and highly distributed platform that conduct a wide range of malicious and illegal activities, such as launching Distributed Denial of Service DDoS attacks, sending SPAM e-mails and click fraud, and collecting confidential information. In order to mitigate security threat posed by botnets, many detection methods have been proposed in the literature over the last decade [ 1 — 6 ].
These detection methods are based on numerous technical principles and assumptions that storm botnet dataset botnets produce their own behaviors and the patterns of network traffic. One of the most prominent botnet storm botnet dataset methods is based on identifying network traffic produced by botnets storm botnet dataset machine learning techniques [ 5 — 9 ]. The main assumption of the machine learning-based methods is that botnets create distinguishable patterns within the network traffic, which can storm botnet dataset efficiently detected and analyzed by machine learning algorithms [ 7 ].
These methods propose a flexible detection that does not require traffic payload to exhibit any anomalous characteristics or much prior knowledge of botnet traffic patterns.
Usually, there is one SDN controller in the control plane, and the data plane consists of network devices. The network devices use specific protocols such as OpenFlow [ 15 ] to communicate with the controller via the control plane.
They just handle packets according to the flow tables managed by the controller rather than process packets by themselves. In traditional network environment, network administrators have to manage each network device one by one. Developers can implement network functions by programming their modules and storm botnet dataset them into the SDN controller to control the packet flows in the data storm botnet dataset.
However, traditional security issues still exist and would be even worse in SDN [ 1316 ] if not handled properly. They showed how to mitigate damage caused by the malicious activities with SDN functionality. However, they did not automatically manage through SDN functionality. Moreover, recent versions of OpenFlow protocol can only handle packet headers up to the OSI transport layer.
If we want to manage network traffic generated from some specific applications or P2P botnets, an additional agent needs to be developed to analyze network traffic. To address the above issues, based on a work by Su [ 21 ], we build a system for P2P botnet traffic detection and application categorization. By developing modules of SDN controller, we can automatically update flow tables in the network devices in accordance with the analyzed results and then drop packets with botnet traffic patterns.
We can also modify the destination fields in the packet header to redirect suspicious traffic to a specific environment e. Our solution can detect Storm botnet dataset botnet traffic efficiently and guard network automatically. The rest of the paper is organized as follows. Backgrounds of botnet and SDN are introduced in Section 2. In Section 4 we present experiments and evaluation of our solution. Related works are compared with our solution in Section 5. Finally, we conclude with Section 6.
Over the past decade, there are numerous Internet security incidents caused by botnet. Attackers can use botnet to launch various malicious activities. Some botmasters also use these compromised machines to do distributed computing, such as data mining.
Modern botnets usually mimic network traffic generated by normal applications to evade detection from network security agents. For this reason, detecting a botnet has become an important research issue. Botnet life-cycle has been defined by several authors, such as Leonard et al.
In the Infection stage, botmasters infect other computers through fishing or social engineering and so on. In the attack stage, bots controlled by the botmaster launch diverse malicious activities according to received commands or search for other victim computers. The main weakness of centralized systems is that they are vulnerable to single point failure. In order to prevent single point of failure, many botmasters deploy their botnet architecture with peer-to-peer P2P communication protocols, such as Kademlia, Bittorent, and Overnet.
These botnets, called decentralized botnets, can use any of the bots or P2P nodes to issue commands to other peers or gain useful information. Decentralized botnet offers higher resiliency than centralized botnet, since every bot or P2P node could play the role as a client or the server. Even though some P2P botnets are taken down, the remaining bots could still communicate with the botmaster and other nodes to launch malicious activities.
To detect cyber threat from botnet, many solutions have been proposed. These methods can be generally classified as host-based or network-based. A host-based method deploys botnet detection at end point computers, identifying unusual usage of computers, such storm botnet dataset CPU utilization, sensitive registers, and memory block. Thus host-based detection approach is not affected storm botnet dataset the encrypted communication channel used by botnet [ 4 ].
However, the main drawback of host-based detection is that it requires monitoring resource usage of every end host. On the other hand, network-based storm botnet dataset approach inspects network connection behavior and identifies possible network traffic patterns in any period of botnet life-cycle.
A network-based method assumes that botnet generates distinguishable network traffic patterns. There are also some similar connections and group activities within the botnet. Network-based detection approaches can be further classified as signature-based and flow-based methods. Signature-based method analyzes network traffic storm botnet dataset on packet level and storm botnet dataset of malicious payload through deep packet inspection DPI. Therefore, it has higher accuracy for known attacks.
However signature-based method could only analyze attacks or botnets already known. Network administrators are responsible for updating signature database frequently to ensure the safety from the latest detected malware or botnet. Moreover, botmasters may evade detection of signature-based method through encrypted or compressed payload [ 2 ].
Flow-based method, on the other hand, detects botnet by analyzing connection behavior of network traffic flow. A flow is usually defined as the packets with the same source and destination within a specific time period.
This detection method identifies suspicious botnet connection traffic patterns by analyzing features extracted from network flow such as flow size, duration, and mean packet size. The flow-based detection does not require inspecting every individual packet payload but analyzing information from the packet storm botnet dataset.
Therefore, flow-based detection is more efficient because it is not affected by encrypted payload. Moreover, detecting botnet through inspecting network traffic patterns could detect not only a specific botnet but also a botnet family with similar connection behavior.
Different botnets in the same botnet family may have different signatures but similar traffic patterns or the same malicious activities.
With experimental evidence, we show how to detect P2P botnet with storm botnet dataset functionalities of SDN and machine learning algorithms, which highlights our contribution of this paper. Since the control and the data planes are separated in SDN, so the network devices storm botnet dataset not need to learn network forwarding rules by themselves.
They forward or drop the packets according to the storm botnet dataset given by their controller. One storm botnet dataset implementation storm botnet dataset SDN southbound protocols is OpenFlow [ 23 ], which regulates the communication between the controller and switches.
Figure 1 illustrates a simplified OpenFlow switch specification. A flow table consists of flow entries Figure 1 dwhose main components include i match fields: Most OpenFlow controllers can load programmable storm botnet dataset e. Some of the modules are designed to plan the packet paths between the end hosts Figure 1 cand each path consists of forwarding rules.
The modules use the Application Programming Interface API provided by the controller to modify the flow tables of OpenFlow switches by translating their forwarding rules. Thus, these OpenFlow controllers provide a framework for their loadable modules to manage the flow entries of OpenFlow switches.
It is not convenient for network administrators to program and manage network devices in traditional network environment, whereas network service and functionality can be both achieved easily by using OpenFlow in SDN. However, under the current OpenFlow protocol, the switches can only process the packet header from layer 1 to layer 4 of OSI model.
In other words, they cannot handle the content of the higher layers in the packets, such as application layer. In practice, developers should not process the content of packets higher than layer 4 of OSI model to prevent such extra works.
The network architecture of our solution is shown in Figure storm botnet datasetwhere the dashed lines from the OpenFlow controller named Rule Arbitrator Figure 2 a to the OpenFlow switches called Data-link Bridges Figure 2 b indicate the management connection. We implement the programmable module storm botnet dataset functionality arbitrates the flow rules of the Data-link Bridges, so the OpenFlow controller acts as a Rule Arbitrator after it loads the module.
The Data-link Bridges are OpenFlow switch, whose forwarding behavior is mostly like the traditional layer 2 switch if Rule Arbitrator does not add any flow rules. Storm botnet dataset, the Rule Arbitrator commands the Data-link Storm botnet dataset to duplicate all incoming packets to their neighbor Detection Agent s Figure 2 c. The captured packets with the same 5-tuple i. The flow recognition is the process that the Detection Agent gathers packets into distinguishable flows.
After we have gathered flow-level information, we can extract several features from these flows to study the behaviors that occur in the network. The Detection Agent analyzes and categorizes each flow and then labels the flow as P2P botnet or benign P2P application through machine learning models, which are built from the traffic records generated by different Storm botnet dataset botnet or known P2P applications. After a flow has been classified, the Detection Storm botnet dataset reports the result to the Rule Arbitrator with the 5-tuple information and the type of P2P botnet or application.
The Rule Arbitrator, afterwards, modifies the related flow tables in the Data-link Bridges in accordance with the result reported by the Detection Storm botnet dataset. Finally, the Data-link Bridges automatically drop the malicious packets that are recognized by the classifiers. In order to get some useful information to classify network storm botnet dataset between different hosts, this module Figure 3 a aggregates packets into network traffic flows with the same 5-tuple information after they are mirrored to the Detection Agent.
We use Netmatean open source tool [ 24 ], to capture packets and transform them into traffic flow, from which we extract feature vectors for machine learning analysis.
Assessing performance of any detection approach requires experimentation with data that is heterogeneous enough to simulate real traffic to an acceptable level. The lack of such data sets available for evaluating botnet detection approaches is well known in storm botnet dataset field mostly due to a number of challenges that have been repeatedly emphasized in storm botnet dataset literature , . We constructed such data set paying a storm botnet dataset attention to the following challenges:.
Unfortunately, most of the existing botnet datasets have generality issue, i. Limited in nature detectors developed in these environments only storm botnet dataset a small number of characteristics describing a very specific botnet behaviorthese approaches are impractical and ineffective in a face of novel threats.
The effectiveness of the developed approach in practice is highly dependent on realistic botnet traffic traces used for its evaluation. Providing a resilient environment not detectable by the botnet in which a botnet performs all of its intended malicious functionality is not trivial. In addition to resiliency, collection period must be long enough to allow dormant bots to exhibit their functionality.
Another problem with generating botnet data is an ability of collected network traffic traces to reflect real environment a detector will face during deployment. Due to privacy concerns gathering background data in a real production environment is not feasible in most cases, as a result storm botnet dataset is either simulated or gathered in a controlled environment. To overcome these challenges, we create an evaluation set combining non overlapping storm botnet dataset of the following data:.
To merge these data traces in one unified data set we employed so called overlay methodology , one of the most popular methods for creating synthetic datasets. Malicious data is usually captured by honeypots or through infecting computers with a given bot binary in a controlled environment storm botnet dataset. Botnet traces can be merged with benign data by mapping storm botnet dataset data to either machines existing in the home network or machines outside of the current network .
Considering the wide range of IP addresses in the traces, we mapped botnet IPs to the hosts outside of the current network using BitTwist packet generator storm botnet dataset. The resulting set was divided into training and test datasets that included 7 and 16 types of botnets, respectively. Tables 1 and 2 detail distribution and type of botnets in each dataset. Our training dataset is 5. Test dataset is 8. We added more diversity of botnet traces in the test dataset than the training dataset in order to evaluate the novelty detection a feature subset can provide.
Beigi, Elaheh Biglar, et al. Canadian Institute storm botnet dataset Cybersecurity. Pioneering cybersecurity research Researchers Job Opportunities. Botnet dataset Assessing performance of any detection approach requires experimentation with data that is heterogeneous enough to simulate real traffic to an acceptable level. We constructed such data set storm botnet dataset a storm botnet dataset attention to the following challenges: To overcome these challenges, we create an evaluation set combining non overlapping subsets of the following data: ISOT dataset  that has been created by merging different available datasets: It contains both malicious traces of Storm and Zeus botnets and non malicious traffic gaming packets, HTTP traffic and P2P application such as bittorrent.
We included a subset of their normal traces in our training dataset. We also included a subset of their normal and IRC botnet traffic in our test dataset. Botnet traffic generated by the Malware Capture Facility Project , a research project with the purpose of generating and capturing botnet traces in long term. Resources Resources Toggle navigation.
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