Biblio

This paper proposes an architecture of Secure Shell (SSH) honeypot using port knocking and Intrusion Detection System (IDS) to learn the information about attacks on SSH service and determine proper security mechanisms to deal with the attacks. Rapid development of information technology is directly proportional to the number of attacks, destruction, and data theft of a system. SSH service has become one of the popular targets from the whole vulnerabilities which is existed. Attacks on SSH service have various characteristics. Therefore, it is required to learn these characteristics by typically utilizing honeypots so that proper mechanisms can be applied in the real servers. Various attempts to learn the attacks and mitigate them have been proposed, however, attacks on SSH service are kept occurring. This research proposes a different and effective strategy to deal with the SSH service attack. This is done by combining port knocking and IDS to make the server keeps the service on a closed port and open it under user demand by sending predefined port sequence as an authentication process to control the access to the server. In doing so, it is evident that port knocking is effective in protecting SSH service. The number of login attempts obtained by using our proposed method is zero.

The objective of the Honeypot security system is a mechanism to identify the unauthorized users and intruders in the network. The enterprise level security can be possible via high scalability. The whole theme behind this research is an Intrusion Detection System and Intrusion Prevention system factors accomplished through honeypot and honey trap methodology. Dynamic Configuration of honey pot is the milestone for this mechanism. Eight different methodologies were deployed to catch the Intruders who utilizing the unsecured network through the unused IP address. The method adapted here to identify and trap through honeypot mechanism activity. The result obtained is, intruders find difficulty in gaining information from the network, which helps a lot of the industries. Honeypot can utilize the real OS and partially through high interaction and low interaction respectively. The research work concludes the network activity and traffic can also be tracked through honeypot. This provides added security to the secured network. Detection, prevention and response are the categories available, and moreover, it detects and confuses the hackers.

The current paper is a continuation of a published article and is about the results of implementing a Honeypot in the Cloud. A five years period of raw data is analyzed and explained in the current Cyber Security state and landscape.

Web attacks have proliferated across the whole Internet in recent years. To protect websites, security vendors and researchers collect attack information using web honeypots. However, web attackers can hide themselves by using stepping stones (e.g., VPN, encrypted proxy) or anonymous networks (e.g., Tor network). Conventional web honeypots lack an effective way to gather information about an attacker's identity, which raises a big obstacle for cybercrime traceability and forensics. Traditional forensics methods are based on traffic analysis; it requires that defenders gain access to the entire network. It is not suitable for honeypots. In this paper, we present the design, implementation, and deployment of the Micro-Honeypot, which aims to use the browser fingerprinting technique to track a web attacker. Traditional honeypot lure attackers and records attacker's activity. Micro-Honeypot is deployed in a honeypot. It will run and gather identity information when an attacker visits the honeypot. Our preliminary results show that Micro-Honeypot could collect more information and track attackers although they might have used proxies or anonymous networks to hide themselves.

A honeypot provides information about the new attack and exploitation methods and allows analyzing the adversary's activities during or after exploitation. One way of an adversary to communicate with a server is via secure shell (SSH). SSH provides secure login, file transfer, X11 forwarding, and TCP/IP connections over untrusted networks. SSH is a preferred target for attacks, as it is frequently used with password-based authentication, and weak passwords are easily exploited using brute-force attacks. In this paper, we introduce a Virtual Machine Introspection based SSH honeypot. We discuss the design of the system and how to extract valuable information such as the credential used by the attacker and the entered commands. Our experiments show that the system is able to detect the adversary's activities during and after exploitation, and it has advantages compared to currently used SSH honeypot approaches.

The Dark Web is known as the part of the Internet operated by decentralized and anonymous-preserving protocols like Tor. To date, the research community has focused on understanding the size and characteristics of the Dark Web and the services and goods that are offered in its underground markets. However, little is still known about the attacks landscape in the Dark Web. For the traditional Web, it is now well understood how websites are exploited, as well as the important role played by Google Dorks and automated attack bots to form some sort of "background attack noise" to which public websites are exposed. This paper tries to understand if these basic concepts and components have a parallel in the Dark Web. In particular, by deploying a high interaction honeypot in the Tor network for a period of seven months, we conducted a measurement study of the type of attacks and of the attackers behavior that affect this still relatively unknown corner of the Web.

In cyberspace, unknown zero-day attacks can bring safety hazards. Traditional defense methods based on signatures are ineffective. Based on the Cyberspace Mimic Defense (CMD) architecture, the paper proposes a framework to detect the attacks and respond to them. Inputs are assigned to all online redundant heterogeneous functionally equivalent modules. Their independent outputs are compared and the outputs in the majority will be the final response. The abnormal outputs can be detected and so can the attack. The damaged executive modules with abnormal outputs will be replaced with new ones from the diverse executive module pool. By analyzing the abnormal outputs, the correspondence between inputs and abnormal outputs can be built and inputs leading to recurrent abnormal outputs will be written into the zero-day attack related database and their reuses cannot work any longer, as the suspicious malicious inputs can be detected and processed. Further responses include IP blacklisting and patching, etc. The framework also uses honeypot like executive module to confuse the attacker. The proposed method can prevent the recurrent attack based on the same exploit.

Many attacks target vulnerabilities of home IoT devices, such as bugs in outdated software and weak passwords. The home network is at a vantage point for deploying security appliances to deal with such IoT attacks. We propose a comprehensive home network defense, Pot2DPI, and use it to raise an attacker's uncertainty about devices and enable the home network to monitor traffic, detect anomalies, and filter malicious packets. The security offered by Pot2DPI comes from a synthesis of practical techniques: honeypot, deep packet inspection (DPI), and a realization of moving target defense (MTD) in port forwarding. In particular, Pot2DPI has a chain of honeypot and DPI that collects suspicious packet traces, acquires attack signatures, and installs filtering rules at a home router timely. Meanwhile, Pot2DPI shuffles the mapping of ports between the router and the devices connected to it, making a targeted attack difficult and defense more effective. Pot2DPI is our first step towards securing a smart home.

Cloud computing emerges as an endowment technological data for the longer term and increasing on one of the standards of utility computing is most likely claimed to symbolize a wholly new paradigm for viewing and getting access to computational assets. As a result of protection problem many purchasers hesitate in relocating their touchy data on the clouds, regardless of gigantic curiosity in cloud-based computing. Security is a tremendous hassle, considering the fact that so much of firms present a alluring goal for intruders and the particular considerations will pursue to lower the advancement of distributed computing if not located. Hence, this recent scan and perception is suitable to honeypot. Distributed Denial of Service (DDoS) is an assault that threats the availability of the cloud services. It's fundamental investigate the most important features of DDoS Defence procedures. This paper provides exact techniques that been carried out to the DDoS attack. These approaches are outlined in these paper and use of applied sciences for special kind of malfunctioning within the cloud.

Honeynet is deployed to trap attackers and learn their behavior patterns and motivations. Conventional honeynet is implemented by dedicated hardware and software. It suffers from inflexibility, high CAPEX and OPEX. There have been several virtualized honeynet architectures to solve those problems. But they lack a standard operating environment and common architecture for dynamic scheduling and adaptive resource allocation. Software Defined Security (SDS) framework has a centralized control mechanism and intelligent decision making ability for different security functions. In this paper, we present a new intelligent honeynet architecture based on SDS framework. It implements security functions over Network Function Virtualization Infrastructure (NFVI). Under uniform and intelligent control, security functional modules can be dynamically deployed and collaborated to complete different tasks. It migrates resources according to the workloads of each honeypot and power off unused modules. Simulation results show that intelligent honeynet has a better performance in conserving resources and reducing energy consumption. The new architecture can fit the needs of future honeynet development and deployment.

Remote Access Trojans (RATs) give remote attackers interactive control over a compromised machine. Unlike large-scale malware such as botnets, a RAT is controlled individually by a human operator interacting with the compromised machine remotely. The versatility of RATs makes them attractive to actors of all levels of sophistication: they've been used for espionage, information theft, voyeurism and extortion. Despite their increasing use, there are still major gaps in our understanding of RATs and their operators, including motives, intentions, procedures, and weak points where defenses might be most effective. In this work we study the use of DarkComet, a popular commercial RAT. We collected 19,109 samples of DarkComet malware found in the wild, and in the course of two, several-week-long experiments, ran as many samples as possible in our honeypot environment. By monitoring a sample's behavior in our system, we are able to reconstruct the sequence of operator actions, giving us a unique view into operator behavior. We report on the results of 2,747 interactive sessions captured in the course of the experiment. During these sessions operators frequently attempted to interact with victims via remote desktop, to capture video, audio, and keystrokes, and to exfiltrate files and credentials. To our knowledge, we are the first large-scale systematic study of RAT use.

Recently, the increase of interconnectivity has led to a rising amount of IoT enabled devices in botnets. Such botnets are currently used for large scale DDoS attacks. To keep track with these malicious activities, Honeypots have proven to be a vital tool. We developed and set up a distributed and highly-scalable WAN Honeypot with an attached backend infrastructure for sophisticated processing of the gathered data. For the processed data to be understandable we designed a graphical frontend that displays all relevant information that has been obtained from the data. We group attacks originating in a short period of time in one source as sessions. This enriches the data and enables a more in-depth analysis. We produced common statistics like usernames, passwords, username/password combinations, password lengths, originating country and more. From the information gathered, we were able to identify common dictionaries used for brute-force login attacks and other more sophisticated statistics like login attempts per session and attack efficiency.

Attacks of Ransomware are increasing, this form of malware bypasses many technical solutions by leveraging social engineering methods. This means established methods of perimeter defence need to be supplemented with additional systems. Honeypots are bogus computer resources deployed by network administrators to act as decoy computers and detect any illicit access. This study investigated whether a honeypot folder could be created and monitored for changes. The investigations determined a suitable method to detect changes to this area. This research investigated methods to implement a honeypot to detect ransomware activity, and selected two options, the File Screening service of the Microsoft File Server Resource Manager feature and EventSentry to manipulate the Windows Security logs. The research developed a staged response to attacks to the system along with thresholds when there were triggered. The research ascertained that witness tripwire files offer limited value as there is no way to influence the malware to access the area containing the monitored files.

Honeypots are a common tool to set intrusion alarms and to study attacks against computer systems. In order to be convincing, honeypots attempt to resemble actual systems that are in active use. Recently, researchers have begun to develop honeypots for programmable logic controllers (PLCs). The tools of which we are aware have limited functionality compared to genuine devices. Particularly, they do not support running actual PLC programs. In order to improve upon the interactive capabilities of PLC honeypots we set out to develop a simulator for Siemens S7-300 series PLCs. Our current prototype XPOT supports PLC program compilation and interpretation, the proprietary S7comm protocol and SNMP. While the supported feature set is not yet comprehensive, it is possible to program it using standard IDEs such as Siemens' TIA portal. Additionally, we emulate the characteristics of the network stack of our reference PLC in order to resist OS fingerprinting attempts using tools such as Nmap. Initial experiments with students whom we trained in PLC programming indicate that XPOT may resist cursory inspection but still fails against knowledgeable and suspicious adversaries. We conclude that high-interactive PLC honeypots need to support a fairly complete feature set of the genuine, simulated PLC.

Twitter is one of the most popular microblogging social systems, which provides a set of distinctive posting services operating in real time. The flexibility of these services has attracted unethical individuals, so-called "spammers", aiming at spreading malicious, phishing, and misleading information. Unfortunately, the existence of spam results non-ignorable problems related to search and user's privacy. In the battle of fighting spam, various detection methods have been designed, which work by automating the detection process using the "features" concept combined with machine learning methods. However, the existing features are not effective enough to adapt spammers' tactics due to the ease of manipulation in the features. Also, the graph features are not suitable for Twitter based applications, though the high performance obtainable when applying such features. In this paper, beyond the simple statistical features such as number of hashtags and number of URLs, we examine the time property through advancing the design of some features used in the literature, and proposing new time based features. The new design of features is divided between robust advanced statistical features incorporating explicitly the time attribute, and behavioral features identifying any posting behavior pattern. The experimental results show that the new form of features is able to classify correctly the majority of spammers with an accuracy higher than 93% when using Random Forest learning algorithm, applied on a collected and annotated data-set. The results obtained outperform the accuracy of the state of the art features by about 6%, proving the significance of leveraging time in detecting spam accounts.

A honeypot is a deception tool for enticing attackers to make efforts to compromise the electronic information systems of an organization. A honeypot can serve as an advanced security surveillance tool for use in minimizing the risks of attacks on information technology systems and networks. Honeypots are useful for providing valuable insights into potential system security loopholes. The current research investigated the effectiveness of the use of centralized system management technologies called Puppet and Virtual Machines in the implementation automated honeypots for intrusion detection, correction and prevention. A centralized logging system was used to collect information of the source address, country and timestamp of intrusions by attackers. The unique contributions of this research include: a demonstration how open source technologies is used to dynamically add or modify hacking incidences in a high-interaction honeynet system; a presentation of strategies for making honeypots more attractive for hackers to spend more time to provide hacking evidences; and an exhibition of algorithms for system and network intrusion prevention.

Active defense is a popular defense technique based on systems that hinder an attacker's progress by design, rather than reactively responding to an attack only after its detection. Well-known active defense systems are honeypots. Honeypots are fake systems, designed to look like real production systems, aimed at trapping an attacker, and analyzing his attack strategy and goals. These types of systems suffer from a major weakness: it is extremely hard to design them in such a way that an attacker cannot distinguish them from a real production system. In this paper, we advocate that, instead of adding additional fake systems in the corporate network, the production systems themselves should be instrumented to provide active defense capabilities. This perspective to active defense allows containing costs and complexity, while at the same time provides the attacker with a more realistic-looking target, and gives the Incident Response Team more time to identify the attacker. The proposed proof-of-concept prototype system can be used to implement active defense in any corporate production network, with little upfront work, and little maintenance.

A honeypot is a deception tool for enticing attackers to make efforts to compromise the electronic information systems of an organization. A honeypot can serve as an advanced security surveillance tool for use in minimizing the risks of attacks on information technology systems and networks. Honeypots are useful for providing valuable insights into potential system security loopholes. The current research investigated the effectiveness of the use of centralized system management technologies called Puppet and Virtual Machines in the implementation automated honeypots for intrusion detection, correction and prevention. A centralized logging system was used to collect information of the source address, country and timestamp of intrusions by attackers. The unique contributions of this research include: a demonstration how open source technologies is used to dynamically add or modify hacking incidences in a high-interaction honeynet system; a presentation of strategies for making honeypots more attractive for hackers to spend more time to provide hacking evidences; and an exhibition of algorithms for system and network intrusion prevention.

Honeynet is a collection of honeypots that are set up to attract as many attackers as possible to learn about their patterns, tactics, and behaviors. However, existing honeypots suffer from a variety of fingerprinting techniques, and the current honeynet architecture does not fully utilize features of residing honeypots due to its coarse-grained data control mechanisms. To address these challenges, we propose an SDN-based intelligent honeynet called HoneyMix. HoneyMix leverages the rich programmability of SDN to circumvent attackers' detection mechanisms and enables fine-grained data control for honeynet. To do this, HoneyMix simultaneously establishes multiple connections with a set of honeypots and selects the most desirable connection to inspire attackers to remain connected. In this paper, we present the HoneyMix architecture and a description of its core components.

A trap set to detect attempts at unauthorized use of information systems. But setting up these honeypots and keep these guzzling electricity 24X7 is rather expensive. Plus there is always a risk of a skillful hacker or a deadly malware may break through this and compromise the whole system. Honeypot name suggest, a pot that contents full of honey to allure beers, but in networks Scenario honeypot is valuable tool that helps to allure attackers. It helps to detect and analyze malicious activity over your network. However honeypots used for commercial organization do not share data and large honeypot gives read only data. We propose an Arm based device having all capability of honeypots to allure attackers. Current honeypots are based on large Network but we are trying to make s device which have the capabilities to establish in small network and cost effective. This research helps us to make a device based on arm board and CCFIS Software to allure attackers which is easy to install and cost effective. CCFIS Sensor helps us to Capture malware and Analysis the attack. In this we did reverse Engineering of honeypots to know about how it captures malware. During reverse engineering we know about pros and cons of honeypots that are mitigated in CCFIS Sensor. After Completion of device we compared honeypots and CCFIS Sensor to check the effectiveness of device.

Honey pots and honey nets are popular tools in the area of network security and network forensics. The deployment and usage of these tools are influenced by a number of technical and legal issues, which need to be carefully considered together. In this paper, we outline privacy issues of honey pots and honey nets with respect to technical aspects. The paper discusses the legal framework of privacy, legal ground to data processing, and data collection. The analysis of legal issues is based on EU law and is supported by discussions on privacy and related issues. This paper is one of the first papers which discuss in detail privacy issues of honey pots and honey nets in accordance with EU law.

Honeypot is a common method of attack capture, can maximize the reduction of cyber-attacks. However, its limited application layer simulation makes it impossible to use effectively in power system. Through research on sandboxing technology, this article implements the simulated power manager applications by packaging real power manager applications, in order to expand the honeypot applied range.

The data processing capabilities of MapReduce systems pioneered with the on-demand scalability of cloud computing have enabled the Big Data revolution. However, the data controllers/owners worried about the privacy and accountability impact of storing their data in the cloud infrastructures as the existing cloud computing solutions provide very limited control on the underlying systems. The intuitive approach - encrypting data before uploading to the cloud - is not applicable to MapReduce computation as the data analytics tasks are ad-hoc defined in the MapReduce environment using general programming languages (e.g, Java) and homomorphic encryption methods that can scale to big data do not exist. In this paper, we address the challenges of determining and detecting unauthorized access to data stored in MapReduce based cloud environments. To this end, we introduce alarm raising honeypots distributed over the data that are not accessed by the authorized MapReduce jobs, but only by the attackers and/or unauthorized users. Our analysis shows that unauthorized data accesses can be detected with reasonable performance in MapReduce based cloud environments.