Threat Detection by Deploying Darknet

SN Computer Science

The cyberspace continues to evolve more complex than ever anticipated, and same is the case with security dynamics there. As our dependence on cyberspace is increasing day-by-day, regular and systematic monitoring of cyberspace security has become very essential. A darknet is one such monitoring framework for deducing malicious activities and the attack patterns in the cyberspace. Darknet traffic is the spurious traffic observed in the empty address space, i.e., a set of globally valid Internet Protocol (IP) addresses which are not assigned to any hosts or devices. In an ideal secure network system, no traffic is expected to arrive on such a darknet IP space. However, in reality, noticeable amount of traffic is observed in this space primarily due to the Internet wide malicious activities, attacks and sometimes due to the network level misconfigurations. Analyzing such traffic and finding distinct attack patterns present in them can be a potential mechanism to infer the attack trends in the real network. In this paper, the existing Basic and Extended AGgregate and Mode (AGM) data formats for darknet traffic analysis is studied and an efficient 29-tuple Numerical AGM data format suitable for analyzing the source IP address validated TCP connections (three-way handshake) is proposed to find attack patterns in this traffic using Mean Shift clustering algorithm. Analyzing the patterns detected from the clusters results in providing the traces of various attacks such as Mirai bot, SQL attack, and brute force. Analyzing the source IP validated TCP, darknet traffic is a potential technique in Cyber security to find the attack trends in the network.

Proceedings of the 5th …, 2005

Threats to the privacy of users and to the availability of Internet infrastructure are evolving at a tremendous rate. To characterize these emerging threats, researchers must effectively balance monitoring the large number of hosts needed to quickly build confidence in new attacks, while still preserving the detail required to differentiate these attacks. One class of techniques that attempts to achieve this balance involves hybrid systems that combine the scalable monitoring of unused address blocks (or darknets) with forensic honeypots (or honeyfarms). In this paper we examine the properties of individual and distributed darknets to determine the effectiveness of building scalable hybrid systems. We show that individual darknets are dominated by a small number of sources repeating the same actions. This enables source-based techniques to be effective at reducing the number of connections to be evaluated by over 90%. We demonstrate that the dominance of locally targeted attack behavior and the limited life of random scanning hosts result in few of these sources being repeated across darknets. To achieve reductions beyond source-based approaches, we look to source-distribution based methods and expand them to include notions of local and global behavior. We show that this approach is effective at reducing the number of events by deploying it in 30 production networks during early 2005. Each of the identified events during this period represented a major globally-scoped attack including the WINS vulnerability scanning, Veritas Backup Agent vulnerability scanning, and the MySQL Worm.

International Journal of Innovative Technology and Exploring Engineering

The malicious activities in the darknet are an emerging threat to the cyber space. Darknet sites operate using TOR(The Onion Router) hidden services which provides the feature of disguising the users of the transaction in the darknet market place. Hence identifying and monitoring such illegal activities in the marketplace has become a tedious task for the cyber and law enforcement officials. This paper presents a prototype for a framework which analyse the traffic flow in the darknet as finding the exact sender and receiver is almost impossible as the TOR is increasing the layers of security to the maximum extent making it impossible to track the users in the transactions. Here we give a methodology using webcrawlers and extract the data from the darknet sites to find the domain of the traffic flow through which the broad area of traffic can be sorted out which would be beneficial for the cyber and law enforcement agencies to find the illicit trade in the darknet market places

The Web is only a segment or portion of the internet. And surface web is the most upfront segment of the web which is easily accessible through conventional search engines like Google, Yahoo or Bing. After the surface web. The Deep web starts its journey and it is unclear how much bigger is the Deep web than the surface web. Almost 96 percent is the Deep Web of WWW (World Wide Web) and a portion of the Deep Web is called the Dark Web which holds around 57 percent of illegal activities. Unlawful discussions, terrorist activities, weapons and drugs dealing, child pornography are some of the criminal activities from the Dark Web. Techniques used for locating criminals and their arranged crimes are somewhere more difficult than real-world tracing as most of them occur anonymously. The dark web was designed mainly to provide users with more privacy. But nowadays, most dark webs are built for crimes, illegal data extraction, hacking, creaking, breaking security and facing trouble to the danger of human life. So we need to monitor the dark websites, threats analysis and detection for cyber security.

2015

Cyberspace has become a massive battlefield between computer criminals and computer security experts. In addition, large-scale cyber attacks have enormously matured and became capable to generate, in a prompt manner, significant interruptions and damage to Internet resources and infrastructure. Denial of Service (DoS) attacks are perhaps the most prominent and severe types of such large-scale cyber attacks. Furthermore, the existence of widely available encryption and anonymity techniques greatly increases the difficulty of the surveillance and investigation of cyber attacks. In this context, the availability of relevant cyber monitoring is of paramount importance. An effective approach to gather DoS cyber intelligence is to collect and analyze traffic destined to allocated, routable, yet unused Internet address space known as darknet. In this thesis, we leverage big darknet data to generate insights on various DoS events, namely, Distributed DoS (DDoS) and Distributed Reflection Do...

IJIRIS:: AM Publications,India, 2019

Localization attacks, in which IP addresses located as sensors comprising Darknet systems are detected, are well-known. Attackers can detect sensors in secret by sending probing traffic with concealed signals to the target network. In response to this, we have developed countermeasures using a dynamic monitoring method, in which there is a dynamic switchover of sensors reflected in the published monitoring results. In this study, we will consider a case wherein the attacker is attempting to embed concealed signals between multiple ports within one sensor. Therefore, we propose a countermeasure method in which there is dynamic monitoring of each destination port. In this paper, we have verified the impact on publishable monitoring results when applying the proposed method to the nicter Darknet in Japan.

2021

Cyberspace has to turn out to be a large battlefield among laptop criminals and laptop protection experts. In addition, large-scale cyber attacks have fairly matured and turned out to be successful to generate, in an active manner, sizable interruptions and harm to Internet assets and infrastructure. Denial of Service (DoS) assaults are possibly the maximum distinguished and extreme styles of large-scale cyber assaults.Furthermore, the lifestyles of broadly to be had encryption and anonymity techniques significantly will increase the problem of the surveillance and research of cyber assaults. In this context, the supply of applicable cyber tracking is of paramount importance. A powerful method to acquire DOS cyber intelligence is to accumulate and examine site visitors destined to allocated, routable, but unused Internet cope with space called dark net. In this thesis, we leverage large dark net facts to generate insights on diverse DOS events, namely, Distributed DOS (DDoS) and Dis...

2020

Darknets are ranges of IP addresses advertised without answering any traffic. Darknets help to uncover interesting network events, such as misconfigurations and network scans. Interpreting darknet traffic helps against cyber-attacks-e.g., malware often reaches darknets when scanning the Internet for vulnerable devices. The traffic reaching darknets is however voluminous and noisy, which calls for efficient ways to represent the data and highlight possibly important events. This paper evaluates a methodology to summarize packets reaching darknets. We represent the darknet activity as a graph, which captures remote hosts contacting the darknet nodes ports, as well as the frequency at which each port is reached. From these representations, we apply community detection algorithms in the search for patterns that could represent coordinated activity. By highlighting such activities we are able to group together, for example, groups of IP addresses that predominantly engage in contacting specific targets, or, vice versa, to identify targets which are frequently contacted together, for exploiting the vulnerabilities of a given service. The network analyst can recognize from the community detection results, for example, that a group of hosts has been infected by a botnet and it is currently scanning the network in search of vulnerable services (e.g., SSH and Telnet among the most commonly targeted). Such piece of information is impossible to obtain when analyzing the behavior of single sources, or packets one by one. All in all, our work is a first step towards a comprehensive aggregation methodology to automate the analysis of darknet traffic, a fundamental aspect for the recognition of coordinated and anomalous events.

2019

In the darknet security topic, it is important to analyze the threats that characterize the network. This paper deeply investigates the literature of attacks against the Tor network, presenting the most relevant threats in this context. In order to provide an important tool for the research community, we propose an exhaustive taxonomy based on the target of the attack. Such taxonomy represents a characterization scheme to identify cyber-attacks related to darknet environments and better understand their functioning. The proposed work should therefore be considered an important step forward in the darknet security field.

New measures imposed by governments, Internet service providers and other third parties which threaten the state of privacy are also opening new avenues to protecting it. The unwarranted scrutiny of legitimate services such as file hosters and the BitTorrent protocol, once relatively unknown to the casual Internet user, is becoming more obvious. The darknet is a rising contender against these new measures and will preserve the default right to privacy of Internet users. A darknet is defined in the context of file sharing as a network which operates on top of another network such as the Internet for the purpose of secure and private distribution of digital material. While there are other darknet applications in existence, such as Freenet, WASTE again, and Relakks, they harbour some caveats. Whether they be proprietary solutions, depend on other services, are prone to feature creep or have security shortcomings, there is room for improvement. The aim of this paper is to address and im...

IEEE Transactions on Education, 2016

This paper presents a network security laboratory project for teaching network traffic anomaly detection methods to electrical engineering students. The project design follows a research-oriented teaching principle, enabling students to make their own discoveries in real network traffic, using data captured from a large IP darkspace monitor operated at the University of California, San Diego (UCSD). Although darkspace traffic does not include bidirectional conversations (only attempts to initiate them), it contains traffic related to or actually perpetrating a variety of network attacks originating from millions of Internet addresses around the world. This breadth of coverage makes this darkspace data an excellent choice for a hands-on study of Internet attack detection techniques. In addition, darkspace data is less privacy-critical than other network traces, because it contains only unwanted network traffic and no legitimate communication. In the lab exercises presented, students learn about network security challenges, search for suspicious anomalies in network traffic, and gain experience in presenting and interpreting their own findings. They acquire not only security-specific technical skills but also general knowledge in statistical data analysis and data mining techniques. They are also encouraged to discover new phenomena in the data, which helps to ignite their general interest in science and engineering research. The Vienna University of Technology, Austria, first implemented this laboratory during the summer semester 2014, with a class of 41 students. With the help of the Center for Applied Internet Data Analysis (CAIDA) at UCSD, all exercises and IP darkspace data are publicly available.

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers' relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of mal-ware traffic, and encrypted traffic identification in real time. Based on a weight agnostic neural networks (WANNs) methodology, we propose an automated searching neural net architecture strategy that can perform various tasks such as identifying zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

ArXiv, 2020

The availability of sophisticated technologies and methods of perpetrating criminogenic activities in the cyberspace is a pertinent societal problem. Darknet is an encrypted network technology that uses the internet infrastructure and can only be accessed using special network configuration and software tools to access its contents which are not indexed by search engines. Over the years darknets traditionally are used for criminogenic activities and famously acclaimed to promote cybercrime, procurements of illegal drugs, arms deals, and cryptocurrency markets. In countries with oppressive regimes, censorship of digital communications, and strict policies prompted journalists and freedom fighters to seek freedom using darknet technologies anonymously while others simply exploit it for illegal activities. Recently, MIT's Lincoln Laboratory of Artificial Intelligence augmented a tool that can be used to expose illegal activities behind the darknet. We studied relevant literature re...

Lecture Notes in Computer Science, 2014

Proactive cyber-security tools provide basic protection as today's cyber-criminals utilize legitimate traffic to perform attacks and remain concealed quite often until it is too late. As critical resources, hidden behind layers of cyber-defenses, can still become compromised with potentially catastrophic consequences, it is of paramount significance to be able to identify cyber-attacks and prepare a proper defense as early as possible. In this paper we will go over the architecture, deployment and usefulness of a distributed network of honeypots that relies on darknets to obtain its data. As we have envisioned that such a system has the potential to detect large scale events as early as possible we have adopted the name Early Warning Intrusion System (EWIS).

Electronics

The massive modern technical revolution in electronics, cognitive computing, and sensing has provided critical infrastructure for the development of today’s Internet of Things (IoT) for a wide range of applications. However, because endpoint devices’ computing, storage, and communication capabilities are limited, IoT infrastructures are exposed to a wide range of cyber-attacks. As such, Darknet or blackholes (sinkholes) attacks are significant, and recent attack vectors that are launched against several IoT communication services. Since Darknet address space evolved as a reserved internet address space that is not contemplated to be used by legitimate hosts globally, any communication traffic is speculated to be unsolicited and distinctively deemed a probe, backscatter, or misconfiguration. Thus, in this paper, we develop, investigate, and evaluate the performance of machine-learning-based Darknet traffic detection systems (DTDS) in IoT networks. Mainly, we make use of six supervise...