Detecting Bots inside a Host using Network Behavior 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...

Proceedings. 2006 31st IEEE Conference on Local Computer Networks, 2006

To date, techniques to counter cyber-attacks have predominantly been reactive; they focus on monitoring network traffic, detecting anomalies and cyber-attack traffic patterns, and, a posteriori, combating the cyber-attacks and mitigating their effects. Contrary to such approaches, we advocate proactively detecting and identifying botnets prior to their being used as part of a cyber-attack [12]. In this paper, we present our work on using machine learning-based classification techniques to identify the command and control (C2) traffic of IRC-based botnets-compromised hosts that are collectively commanded using Internet Relay Chat (IRC). We split this task into two stages: (I) distinguishing between IRC and non-IRC traffic, and (II) distinguishing between botnet and real IRC traffic. For Stage I, we compare the performance of J48, naive Bayes, and Bayesian network classifiers, identify the features that achieve good overall classification accuracy, and determine the classification sensitivity to the training set size. While sensitive to the training data and the attributes used to characterize communication flows, machine learning-based classifiers show promise in identifying IRC traffic. Using classification in Stage II is trickier, since accurately labeling IRC traffic as botnet and non-botnet is challenging. We are currently exploring labeling flows as suspicious and non-suspicious based on telltales of hosts being compromised.

Communications of the IBIMA, 2022

Rightful owners don't realize that their devices are infected and used to conduct malicious actions. The number of different types of botnets that have been implemented is truly impressive (Garcia et al. 2014). They use different infection and communication methods. The most popular are based on P2P, HTTP/HTTPS and IRC protocols. They are used for sending and receiving commands from their owner called a botmaster. Another difference between botnets is their architecture. Most of them are centralized with only one C2,

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.

Botnet are becoming the most significant threat to the internet world. Botnet is the automated process of attackers that interacts with network traffic and its services. Botnet are automatically updated into the compromised system to collect the authenticated information. In this paper, we present a model to extract some features which are helpful to analyze the behaviour of bot members present in the particular network traffic. On the other hand, various superior methods are evaluated to extract weather network traffic contain bot or not. In particularly, our evaluation shows that the particular traffic contain any bot member in their communication.

Procedia Computer Science, 2018

During the past decade, botnet has emerged as a very serious threat to cyber security by proving it's capability of co mpromising billions of computers and making them does the illegal work.There are a number of existing ways by which botnet can be detected.A comprehensive overview of the existing techniques is also stated in this paper. Due to the involvement of huge amount of data, detection of botnet using machine learning algorithms is in huge trend. In this paper, we have used machine learning to train classifiers by a specific network flow dataset. Thereafter, the trained classifiers were applied on the collected data in order to evaluate the results. Analysis of network flow data is usedas a method of detection because it doesn't depend upon the packet content hence giving immunity towards the latest form of encryption and obfuscation used by attackers in order to hide their bots. Results are clearly showing that the proposed method is capable of differentiating the normal traffic and the bot traffic with a high accuracy and low false positive rate. In addition to this, almost every type of botnet can be detected using the proposed model.

Botnet comprises of collection of bot-infected computers that allows an attacker to take control and carry out large scale cyber attacks. Botnets have been used to perform various malicious activities such as Distributed Denial of Service (DDoS), information stealing, and cyber physical attacks. Botnets act in a stealthy manner by keeping themselves hidden from the users of compromised systems. In this paper, we present a survey of botnet detection techniques and classify them into four classes: (i) signature-based, (ii) anomaly-based, (iii) data miningbased, and (iv) honeypot-based. We then compare different detection techniques based on their response to unknown bots, encrypted bots, protocol and structure independence, real time detection and accuracy of detection.

The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 2023

A botnet is one of the most dangerous forms of security issues. It infects unsecured computers and transmit malicious commands. By using botnet, the attacker can launch a variety of attacks, such as distributed denial of service (DDoS), data theft, and phishing. The botnet may contain a lot of infected hosts and its size is usually large. In this paper, we addressed the problem of botnet detection based on network’s flows records and activities in the host. We proposed a host-based approach that detects a host, that has been compromised by observing the flow of in-out bound traffic. To prove the existence of command and control communication, we examine host network flow. Once the bot process has been identified in the host being monitored, this knowledge allows blocking any in/out traffic with the bot’s server. In addition to providing information about the compromised machine’s IP address and how it communicates with servers, the log file is generated, which can provide data about the command and control (C&C) servers. Most existing work on detecting botnet is based on flow-based traffic analysis by mining their communication patterns. Our work distinguishes itself from other methods of bot detection from its ability to use real-time host-related data for detection.

Among the diverse forms of malware, Botnet is the most widespread and serious threat which occurs commonly in today's cyber-attacks. A botnet is a group of compromised computers which are remotely controlled by hackers to launch various network attacks, such as DDoS attack, spam, click fraud, identity theft and information phishing. Botnet has become a popular and productive tool behind many cyber-attacks. The defining characteristic of botnets is the use of command and control channels through which they can be updated and directed. Recently malicious botnets evolve into HTTP botnets out of typical IRC botnets. Data mining algorithms allow us to automate detecting characteristics from large amount of data, which the conventional heuristics and signature based methods could not apply.

Journal of Information Engineering and Applications, 2013

With the pervasiveness of internet, huge threats have been seen in last few decades. These threats involve the activities for violation of security in terms of integrity, confidentiality, denial of service, authentication. Due to the existence of such threats, there is requirement to defend our immense corporate secret, online banking account details and social networking account accessible via web interface. Over last few decades there is the emergence of botnet within internet. Botnet can be considered as the mass of compromise machine that are under the authority and control of single botmaster. Because of existence of such botnet there arouse intrusion. And hence intrusion detection has turn out to be sphere of influence of information assurance. At the network-level, the research work to detect bots has proceeded along two important area of vertical and horizontal correlation engine. Vertical and local correlation engine have the downside that these systems require prior knowledge about communication channel and it is indispensable to have at least two hosts in the monitored network(s) should be the members of the same botnet. Hence the new autonomous model is proposed by combining the concept of observation of command and responses received. This model will be built in controlled environment with recording of network activity by using subspace and evidence accumulation clustering. Proposed models are helpful for detection of bots in the midst of few false positives.

2020 14th International Conference on Innovations in Information Technology (IIT), 2020

With the advancement of computers and technology, security threats are also evolving at a fast pace. Botnets are one such security threat which requires a high level of research and focus in order to be eliminated. In this paper, we use machine learning to detect Botnet attacks. Using the Bot-IoT and University of New South Wales (UNSW) datasets, four machine learning models based on four classifiers are built: Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, and Decision Trees. Using 82,000 records from UNSW-NB15 dataset, the decision trees model has yielded the best overall results with 99.89% testing accuracy, 100% precision, 100% recall, and 100% F-score in detecting botnet attacks.

IRJET, 2022

In past decades, use of internet is growing in every part of technology and which is hindered by various security issues. Relatively, it questions on data confidentiality, data integrity and data availability. One of the source of data breach is botnet and it is a threat to internet security. Therefore, to make the system robust, reliable and secure there are some mechanisms for botnet detection and removal process. Botnets are generally categorized according to the protocol used by the command-and-control server in IRC, HTTP, DNS or Peer to Peer (P2P) botnets. Botnets can be detected using various algorithms such as decision tree, random forest, KNN, K-nearest neighbor, naïve bayes, support vector machine, etc. In this paper, The analysis of various papers on botnet attacks and detection techniques.

2008

Botnets are now the key platform for many Internet attacks, such as spam, distributed denial-of-service (DDoS), identity theft, and phishing. Most of the current botnet detection approaches work only on specific botnet command and control (C&C) protocols (e.g., IRC) and structures (e.g., centralized), and can become ineffective as botnets change their C&C techniques. In this paper, we present a general detection framework that is independent of botnet C&C protocol and structure, and requires no a priori knowledge of botnets (such as captured bot binaries and hence the botnet signatures, and C&C server names/addresses). We start from the definition and essential properties of botnets. We define a botnet as a coordinated group of malware instances that are controlled via C&C communication channels. The essential properties of a botnet are that the bots communicate with some C&C servers/peers, perform malicious activities, and do so in a similar or correlated way. Accordingly, our detection framework clusters similar communication traffic and similar malicious traffic, and performs cross cluster correlation to identify the hosts that share both similar communication patterns and similar malicious activity patterns. These hosts are thus bots in the monitored network. We have implemented our BotMiner prototype system and evaluated it using many real network traces. The results show that it can detect real-world botnets (IRC-based, HTTP-based, and P2P botnets including Nugache and Storm worm), and has a very low false positive rate.

International Journal of Electrical and Computer Engineering (IJECE), 2023

Cybersecurity is seriously threatened by Botnets, which are controlled networks of compromised computers. The evolving techniques used by botnet operators make it difficult for traditional methods of botnet identification to stay up. Machine learning has become increasingly effective in recent years as a means of identifying and reducing these hazards. The CTU-13 dataset, a frequently used dataset in the field of cybersecurity, is used in this study to offer a machine learning-based method for botnet detection. The suggested methodology makes use of the CTU-13, which is made up of actual network traffic data that was recorded in a network environment that had been attacked by a botnet. The dataset is used to train a variety of machine learning algorithms to categorize network traffic as botnet-related/benign, including decision tree, regression model, naïve Bayes, and neural network model. We employ a number of criteria, such as accuracy, precision, and sensitivity, to measure how well each model performs in categorizing both known and unidentified botnet traffic patterns. Results from experiments show how well the machine learning based approach detects botnet with accuracy. It is potential for use in actual world is demonstrated by the suggested system's high detection rates and low false positive rates.

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.