BotMiner: clustering analysis of network traffic for protocol-and structure-independent botnet detection (original) (raw)
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Botnet Detection by Monitoring Similar Communication Patterns
Corr, 2010
Botnet is most widespread and occurs commonly in today's cyber attacks, resulting in serious threats to our network assets and organization's properties. Botnets are collections of compromised computers (Bots) which are remotely controlled by its originator (BotMaster) under a common Command-and-Control (C&C) infrastructure. They are used to distribute commands to the Bots for malicious activities such as distributed denial-of-service (DDoS) attacks, spam and phishing. Most of the existing Botnet detection approaches concentrate only on particular Botnet command and control (C&C) protocols (e.g., IRC,HTTP) and structures (e.g., centralized), and can become ineffective as Botnets change their structure and C&C techniques. In this paper at first we provide taxonomy of Botnets C&C channels and evaluate well-known protocols which are being used in each of them. Then we proposed a new general detection framework which currently focuses on P2P based and IRC based Botnets. This proposed framework is based on definition of Botnets. Botnet has been defined as a group of bots that perform similar communication and malicious activity patterns within the same Botnet. The point that distinguishes our proposed detection framework from many other similar works is that there is no need for prior knowledge of Botnets such as Botnet signature.
CluSiBotHealer: Botnet Detection through Similarity Analysis of Clusters
Journal of Advances in Computer Networks, 2015
Botnets are responsible for most of the security threats in the Internet. Botnet attacks often leverage on their coordinated structures among bots spread over a vast geographical area. In this paper, we propose CluSiBotHealer, a novel framework for detection of Peer-to-Peer (P2P) botnets through data mining technique. P2P botnets are more resilient structure of botnets (re)designed to overcome single point of failure of centralized botnets. Our proposed system is based on clustering of C&C flows within a monitored network for suspected bots. Leveraging on similarity of packet structures and flow structures of frequently exchanged C&C flows within a P2P botnet, our proposed system initially uses clustering of flows and then Jaccard similarity coefficient on sample sets derived from clusters for accurate detection of bots. Ours is a very effective and novel framework which can be used for proactive detection of P2P bots within a monitored network. We empirically validated our model on traces collected from three different P2P botnets namely Nugache, Waledac and P2P Zeus.
BotSniffer: Detecting Botnet Command and Control Channels in Network Traffic
Botnets are now recognized as one of the most serious security threats. In contrast to previous malware, botnets have the characteristic of a command and control (C&C) channel. Botnets also often use existing common protocols, e.g., IRC, HTTP, and in protocol-conforming manners. This makes the detection of botnet C&C a challenging problem. In this paper, we propose an approach that uses network-based anomaly detection to identify botnet C&C channels in a local area network without any prior knowledge of signatures or C&C server addresses. This detection approach can identify both the C&C servers and infected hosts in the network. Our approach is based on the observation that, because of the pre-programmed activities related to C&C, bots within the same botnet will likely demonstrate spatial-temporal correlation and similarity. For example, they engage in coordinated communication, propagation, and attack and fraudulent activities. Our prototype system, BotSniffer, can capture this spatial-temporal correlation in network traffic and utilize statistical algorithms to detect botnets with theoretical bounds on the false positive and false negative rates. We evaluated BotSniffer using many real-world network traces. The results show that BotSniffer can detect real-world botnets with high accuracy and has a very low false positive rate.
Detecting Botnets with Tight Command and Control
Proceedings. 2006 31st IEEE Conference on Local Computer Networks, 2006
Systems are attempting to detect botnets by examining traffic content for IRC commands or by setting up honeynets. Our approach for detecting botnets is to examine flow characteristics such as bandwidth, duration, and packet timing looking for evidence of botnet command and control activity. We have constructed an architecture that first eliminates traffic that is unlikely to be a part of a botnet, classifies the remaining traffic into a group that is likely to be part of a botnet, then correlates the likely traffic to find common communications patterns that would suggest the activity of a botnet. Our results show that botnet evidence can be extracted from a traffic trace containing almost 9 million flows.
Botnet Detection Based on Network Behavior
Advances in Information Security, 2008
Current techniques for detecting botnets examine traffic content for IRC commands, monitor DNS for strange usage, or set up honeynets to capture live bots. Our botnet detection approach is to examine flow characteristics such as bandwidth, packet timing, and burst duration for evidence of botnet command and control activity. We have constructed an architecture that first eliminates traffic that is unlikely to be a part of a botnet, classifies the remaining traffic into a group that is likely to be part of a botnet, then correlates the likely traffic to find common communications patterns that would suggest the activity of a botnet. Our results show that botnet evidence can be extracted from a traffic trace containing over 1.3 million flows.
Hybrid Botnet Detection Based on Host and Network Analysis
Journal of Computer Networks and Communications
Botnet is one of the most dangerous cyber-security issues. The botnet infects unprotected machines and keeps track of the communication with the command and control server to send and receive malicious commands. The attacker uses botnet to initiate dangerous attacks such as DDoS, fishing, data stealing, and spamming. The size of the botnet is usually very large, and millions of infected hosts may belong to it. In this paper, we addressed the problem of botnet detection based on network’s flows records and activities in the host. Thus, we propose a general technique capable of detecting new botnets in early phase. Our technique is implemented in both sides: host side and network side. The botnet communication traffic we are interested in includes HTTP, P2P, IRC, and DNS using IP fluxing. HANABot algorithm is proposed to preprocess and extract features to distinguish the botnet behavior from the legitimate behavior. We evaluate our solution using a collection of real datasets (malicio...
Wide-scale Botnet Detection and Characterization
2000
Malicious botnets are networks of compromised computers that are controlled remotely to perform large-scale distributed denial-of-service (DDoS) attacks, send spam, trojan and phishing emails, distribute pirated media or conduct other usually illegitimate activities.
research.ijcaonline.org
Among the diverse forms of malware, Botnet is the serious threat which occurs commonly in today"s cyber attacks and cyber crimes. Botnet are designed to perform predefined functions in an automated fashion, where these malicious activities ranges from online searching of data, accessing lists, moving files sharing channel information to DDoS attacks against critical targets, phishing, click fraud etc. Existence of command and control(C&C) infrastructure makes the functioning of Botnet unique; in turn throws challenges in the mitigation of Botnet attacks.
A Survey on Botnet Command and Control Traffic Detection
2015
Internet users have been attacked by widespread email viruses earlier, but now scenario has been changed. Now attackers are no more interested to just attract media attention by infecting a large number of computers on the network; in fact, their interest has been shifted to compromising and controlling the infected computers for their personal profits. This new attack trend brings the concept of botnets over the global network of computers. With the high reported infection rates, the vast range of illegal activities and powerful comebacks, botnets are one of the main threats against the cyber security. This paper provides the readers with a background on botnet life-cycle, architecture and malicious activities. It also classifies botnet detection techniques, reviews the recent research works on botnet traffic detection and finally indicates some challenges posed to future work on botnet detection.
CoCoSpot: Clustering and recognizing botnet command and control channels using traffic analysis
Computer Networks, 2013
We present CoCoSpot, a novel approach to recognize botnet command and control channels solely based on traffic analysis features, namely carrier protocol distinction, message length sequences and encoding differences. Thus, CoCoSpot can deal with obfuscated and encrypted C&C protocols and complements current methods to fingerprint and recognize botnet C&C channels. Using average-linkage hierarchical clustering of labeled C&C flows, we show that for more than 20 recent botnets and over 87,000 C&C flows, CoCoSpot can recognize more than 88% of the C&C flows at a false positive rate below 0.1%.