Extending Black Domain Name List by Using Co-occurrence Relation between DNS Queries

In recent days, botnet plays an important role in malware distribution. This has been used as a primary approach for the proliferation of the malicious activities via the internet by attackers. To evade blacklisting, recent botnets make use of domain flux or internet protocol (IP) flux. This work focuses on domain flux. Domain flux uses domain generation algorithms (DGAs) to generate a list of domain names based on a seed and these domain names contacts command and control (C&C) server till it gets access permission to the system. This work presents the fully labeled domain name data set entitled as AmritaDGA which can be used for doing research in the field of detecting domain names which are generated using DGAs. We evaluate the efficacy of deep learning architectures with Keras embedding as domain name representation method on AmritaDGA. AmritaDGA is composed of two data sets. The first data set is collected from the publicly available sources. The second data set is collected from an internal real-time network. The performance of the trained model on public data set is evaluated on unseen samples of a public data set and private corpora. Deep learning architectures performed well in most of the cases of test experiments. The baseline system has been made publicly available and the data set is distributed for Detecting Malicious Domain names (DMD 2018) shared task.

Domain names play an increasingly important role for the botnet activities. Traditionally, DNS traces from several local DNS servers are used passively to measure the DNS query behavior. However, since botnets are a wide-scale threat and usually reside in geographically dispersed networks, the vantage point of several local DNS servers is sometimes too small to help us understand the DNS query behavior (e.g., whether queried or not, average query rate) of botnets. In this paper, we actively measure the DNS query behavior of botnets in geographically dispersed networks via the DNS cache probing technique. We first analytically characterize how multiple domain names are queried by botnets in different networks under certain circumstances. Then, we actively measure real botnet samples in the wild to gain insight into how multiple domain names are queried by botnets in 480 geographically dispersed networks globally, and show that our analytical characterization well describes the DNS query behavior of the botnet samples. The active measurement technique can help to acquire extensive DNS query information in different networks and thus potentially facilitate various DNS-related research and applications.

In today’s security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extracting statistical features by principal component analysis. The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy.

Botnets pose a major threat to the information security of organizations and individuals. The bots (malware infected hosts) receive commands and updates from the Command and Control (C&C) servers, and hence, contacting and communicating with these servers is an essential requirement of bots. However, once a malware is identified in the infected host, it is easy to find its C&C server and block it, if the domain names of the servers are hard-coded in the malware. To counter such detection, many malwares families use probabilistic algorithms known as domain generation algorithms (DGAs) to generate domain names for the C&C servers. This makes it difficult to track down the C&C servers of the Botnet even after the malware is identified. In this paper, we propose a probabilistic approach for the identification of domain names which are likely to be generated by a malware using DGA. The proposed solution is based on the hypothesis that human generated domain names are usually inspired by the words from a particular language (say English), whereas DGA generated domain names should contain random sub-strings in it. Results show that the percentage of false negatives in the detection of DGA generated domain names using the proposed method is less than 29% across 30 DGA families considered by us in our experimentation.

Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modeling

Domain generation algorithms (DGAs) are commonly leveraged by malware to create lists of domain names, which can be used for command and control (C&C) purposes. Approaches based on machine learning have recently been developed to automatically detect generated domain names in real-time. In this paper, we present a novel DGA called CharBot, which is capable of producing large numbers of unregistered domain names that are not detected by state-of-the-art classifiers for real-time detection of the DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and LSTM.MI (a deep learning approach). The CharBot is very simple, effective, and requires no knowledge of the targeted DGA classifiers. We show that retraining the classifiers on CharBot samples is not a viable defense strategy. We believe these findings show that DGA classifiers are inherently vulnerable to adversarial attacks if they rely only on the domain name string to make a decision. Designing a robust DGA classifier may, therefore, necessitate the use of additional information besides the domain name alone. To the best of our knowledge, the CharBot is the simplest and most efficient black-box adversarial attack against DGA classifiers proposed to date.

Domain Name System (DNS) has become a target of the Distributed Denial of Service (DDoS) attacks. When a DNS is under a DDoS flood attack, all the domain information under that DNS becomes unreachable, eventually causing unavailability of those particular domain names. In this paper, we propose a method which includes periodic stale content update and maintains a list of most frequently queried domain names of different DNS servers. Our simulation results show that the our method can serve more than \(70\%\) of the total cache responses during a massive DNS Flood attack.

Cyberattacks significantly impact the services based on the internet that is used in our daily lives. Any disruption will make it extremely difficult for us to carry out our daily activities. Cyberattacks will disrupt online services, exploit vulnerabilities to breach databases and servers, and so on. Various systems and services contribute to the Internet’s seamless functionality. The Domain Name System (DNS) is one of the most important services. DNS is used to resolve domain names into machine-readable IP addresses. DNS, like many other Internet services, is vulnerable to cyber-attacks. While DNS faces a slew of threats, one in particular appears to stand out. DNS is vulnerable to a variety of distributed denial-of-service attacks. The distributed reflection denial of service (DRDoS) attack, a flooding attack against DNS servers that renders them unavailable, disrupting domain name resolution activities, is one of the most common variants. DRDoS attacks have been on the rise in recent years. DNS lookup outages would significantly impact our online activities in the world of ultra-connectivity because they are typically the first step in establishing a connection with a server. The purpose of this paper is to present a state-of-the-art review of DRDoS attack detection and mitigation algorithms as well as the datasets on which these algorithms operate. Finally, we discussed each of these algorithms' relative merits and demerits.

We statistically investigated the total ANY resource record (RR) based DNS query request packet traffic from the Internet to the top domain DNS server in a university campus network through January 1st, 2011 to December 31st, 2012. The obtained results are: (1) We found a significant increase in the inbound ANY RR based DNS query request traffic at November 28th, 2011. (2) In the DNS query request packet traffic, we observed only a query keyword of the campus domain name. (3) We found a correlation between the total inbound DNS query request packet traffic and the DNS query request packet traffic including the query keyword. (4) Also, we carried out the loading test sending ANY, A, and PTR RR unique DNS queries to a test DNS server, we observed no difference among the vmstat parameters, and the load value was 0.10-0.20. These results indicate that the ANY RR based DNS request packet traffic is quite strange. However, it should be meaningless activity.

The ability to detect malicious domain names is critical for protection against internet security, data theft, and other dangers. Current methods for recognising malicious domain names have demonstrated poor detection accuracy in dealing with massive data. This paper proposes a novel malicious domain name detection method based on an improved Hidden Markov Model (HMM). Firstly, by analysing various characteristics of good and evil domain names in DNS communication, we can use Spark fast extraction to distinguish their attributes; then, we can quickly classify unknown domain names accurately by using Baum-Welch algorithm and Viterbi algorithm in Hidden Markov Model (BVHMM) to achieve the effective detection of malicious domain names; finally, to test our approach, we conducted a series of experiments, and the experimental results demonstrate that our model achieves good accuracy and recall rate as compared with other detection models.

Threats related to computer security constantly evolving and attacking the networks and internet all the time. New security threats and the sophisticated methods that hackers use can bypass the detection and prevention mechanisms. A new approach which can handle and analyze massive amount of logs from diverse sources such as network packets, Domain name system (DNS) logs, proxy logs, system/service logs etc. required. This approach can be typically termed as big data. This approach can protect and provide solution to various security issues such as fraud detection, malicious activities and other advanced persistent threats. Apache spark is a distributed big data based cluster computing platform which can store and process the security data to give real time protection. In this paper, we collect only DNS logs from client machines in local area network (LAN) and store it in a server. To find the domain name as either benign or malicious, we propose deep learning based approach. For comparison, we have evaluated the effectiveness of various deep learning approaches such as recurrent neural network (RNN), long short-term memory (LSTM) and other traditional machine learning classifiers. Deep learning based approaches have performed well in comparison to the other classical machine learning classifiers. The primary reason is that deep learning algorithms have the capability to obtain the right features implicitly. Moreover, LSTM has obtained highest malicious detection rate in all experiments in comparison to the other deep learning approaches.

Domain fluxing is a general method for botnet operators to control the victims and escape detection. Botnets based on domain fluxing, such as Conficker, Torpig, Kraken, generate a unique list of domain names based on a predefined domain generation algorithm (DGA). If the algorithm is known in advance, it is easy to identify and block botnet traffic. Unfortunately, exploiting details about the algorithm requires reverse-engineering technology and that is not always feasible. In this paper, we propose a methodology to detect auto-generated domains by measuring the disparity between auto-generated domains and normal domains. The idea is based on the observation that the normal domain names differ from auto-generated domain names in readability, randomness etc., because botnet don’t use well-formed words which is highly likely registered. Clustering algorithm is used to group auto-generated domains into several separated clusters and normal domains into other clusters. As shown in the validation and experiment phase, we prove this method can detect DGA domains with high performance.

The theoretical and practical aspects of an anomaly detection system in DNS queries, which serves as a crucial tool for ensuring the security of internet infrastructure, are examined. Anomalous DNS queries pose a serious threat to network security and stability as they can be an indicator of cyber attacks or malicious activity. One of the most common threats is the use of DNS to carry out DDoS attacks, in particular through the DNS amplification mechanism. An analysis of existing anomaly detection methods, including statistical, signature-based approaches, and machine learning methods, is conducted. The key stages of the proposed system's development are described, including data collection, preprocessing, and analysis, as well as the creation of a normal activity profile for identifying deviations. Innovative methods based on deep learning and time series analysis open new horizons in automated detection of anomalies in DNS traffic. Deep neural networks, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are used to detect complex patterns in large data sets, including the textual and temporal aspects of DNS queries. The primary goal of the study is to demonstrate the effectiveness of a combined approach to DNS traffic analysis, utilizing modern machine learning algorithms such as Isolation Forest, One-Class SVM, and K-means. The proposed system achieves a high level of accuracy (92%) and completeness (90%) in anomaly detection, as confirmed by testing results on the CAIDA Passive DNS Dataset. A description of the modular architecture of the system is presented, which allows for scalability in large networks with high traffic levels. The proposed approach is flexible and adaptive, enabling integration with existing network security and incident response tools.

The analysis of scientific paper and Internet sources devoted to the problem of information counteraction to threats of phishing sites of organizations is carried out. The need for the use of protective equipment on the side of organizations is shown. The relevance of the research topic is confirmed by the presence in the RU zone registry of domain names similar in spelling to well-known educational resources. A method for detecting fake websites based on checking the visual similarity of domain names is proposed. The developed method is based on the use of the Levenshtein metric used in the problem of fuzzy string comparison. The proposed method was tested on the dataset of domain names from the RU zone registry for a certain period of time. When implementing the fake site detection method, the Java programming language and the JGraphT library were used. The test results show an improvement in the quality of the search for domain names similar in spelling to the specified domain name of a web resource. The proposed method can be used in the implementation of one of the modules of a software tool for countering the threats of fake educational sites.

Recent convergence of telephony with the Internet offers malicious actors the ability to craft cross-channel attacks that leverage both telephony and Internet resources. Bulk messaging services can be used to send unsolicited SMS messages to phone numbers. While the long-term properties of email spam tactics have been extensively studied, such behavior for SMS spam is not well understood. In this paper, we discuss a novel SMS abuse attribution system called CHURN. The proposed system is able to collect data about large SMS abuse campaigns and analyze their passive DNS records and supporting website properties. We used CHURN to systematically conduct attribution around the domain names and IP addresses used in such SMS spam operations over a five year time period. Using CHURN, we were able to make the following observations about SMS spam campaigns: (1) only 1 % of SMS abuse domains ever appeared in public domain blacklists and more than 94 % of the blacklisted domain names did not appear in such public blacklists for several weeks or even months after they were first reported in abuse complaints, (2) more than 40 % of the SMS spam domains were active for over 100 days, and (3) the infrastructure that supports the abuse is surprisingly stable. That is, the same SMS spam domain names were used for several weeks and the IP infrastructure that supports these campaigns can be identified in a few networks and a small number of IPs, for several months of abusive activities. Through this study, we aim to increase the situational awareness around SMS spam abuse, by studying this phenomenon over a period of five years.