176 research outputs found

    Study of Peer-to-Peer Network Based Cybercrime Investigation: Application on Botnet Technologies

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    The scalable, low overhead attributes of Peer-to-Peer (P2P) Internet protocols and networks lend themselves well to being exploited by criminals to execute a large range of cybercrimes. The types of crimes aided by P2P technology include copyright infringement, sharing of illicit images of children, fraud, hacking/cracking, denial of service attacks and virus/malware propagation through the use of a variety of worms, botnets, malware, viruses and P2P file sharing. This project is focused on study of active P2P nodes along with the analysis of the undocumented communication methods employed in many of these large unstructured networks. This is achieved through the design and implementation of an efficient P2P monitoring and crawling toolset. The requirement for investigating P2P based systems is not limited to the more obvious cybercrimes listed above, as many legitimate P2P based applications may also be pertinent to a digital forensic investigation, e.g, voice over IP, instant messaging, etc. Investigating these networks has become increasingly difficult due to the broad range of network topologies and the ever increasing and evolving range of P2P based applications. In this work we introduce the Universal P2P Network Investigation Framework (UP2PNIF), a framework which enables significantly faster and less labour intensive investigation of newly discovered P2P networks through the exploitation of the commonalities in P2P network functionality. In combination with a reference database of known network characteristics, it is envisioned that any known P2P network can be instantly investigated using the framework, which can intelligently determine the best investigation methodology and greatly expedite the evidence gathering process. A proof of concept tool was developed for conducting investigations on the BitTorrent network.Comment: This is a thesis submitted in fulfilment of a PhD in Digital Forensics and Cybercrime Investigation in the School of Computer Science, University College Dublin in October 201

    Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

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    In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide a comprehensive review of the techniques used by attackers to set up such a channel and to hide its presence from the attacked parties and the security tools they use. We then switch to the defensive side of the problem, and review approaches that have been proposed for the detection and disruption of C2 channels. We also map such techniques to widely-adopted security controls, emphasizing gaps or limitations (and success stories) in current best practices.Comment: Work commissioned by CPNI, available at c2report.org. 38 pages. Listing abstract compressed from version appearing in repor

    Distributed Denial of Service Attacks on Cloud Computing Environment‎

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    This paper aimed to identify the various kinds of distributed denial of service attacks (DDoS) attacks, their destructive capabilities, and most of all, how best these issues could be counter attacked and resolved for the benefit of all stakeholders along the cloud continuum, preferably as permanent solutions. A compilation of the various types of DDoS is done, their strike capabilities and most of all, how best cloud computing environment issues could be addressed and resolved for the benefit of all stakeholders along the cloud continuum. The key challenges against effective DDoS defense mechanism are also explored

    A Historical evaluation of C&C complexity

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    The actions of Malware are often controlled through uniform communications mechanisms, which are regularly changing to evade detection techniques and remain prolific. Though geographically dispersed, malware-infected nodes being controlled for a common purpose can be viewed as a logically joint network, now loosely referred to as a botnet. The evolution of the mechanisms or processes for controlling the networks of malware-infected nodes may be indicative of their sophistication relative to a point of inception or discovery (if inception time is unknown). A sampling of botnet related malware at different points of inception or discovery can provide accurate representations of the sophistication variance of command and control processes. To accurately measure a sampling, a matrix of sophistication, deemed the Complexity Matrix (CM), was created to categorize the signifying characteristics of Command and Control (C&C) processes amongst a historically-diverse selection of bot binaries. In this paper, a survey of botnets is conducted to identify C&C characteristics that accurately represent the level of sophistication being implemented within a specified time frame. The results of the survey are collected in a CM and used to generate a subsequent roadmap of C&C milestones

    The End of the Canonical IoT Botnet: A Measurement Study of Mirai's Descendants

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    Since the burgeoning days of IoT, Mirai has been established as the canonical IoT botnet. Not long after the public release of its code, researchers found many Mirai variants compete with one another for many of the same vulnerable hosts. Over time, the myriad Mirai variants evolved to incorporate unique vulnerabilities, defenses, and regional concentrations. In this paper, we ask: have Mirai variants evolved to the point that they are fundamentally distinct? We answer this question by measuring two of the most popular Mirai descendants: Hajime and Mozi. To actively scan both botnets simultaneously, we developed a robust measurement infrastructure, BMS, and ran it for more than eight months. The resulting datasets show that these two popular botnets have diverged in their evolutions from their common ancestor in multiple ways: they have virtually no overlapping IP addresses, they exhibit different behavior to network events such as diurnal rate limiting in China, and more. Collectively, our results show that there is no longer one canonical IoT botnet. We discuss the implications of this finding for researchers and practitioners

    An Analysis and Enumeration of the Blockchain and Future Implications

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    The blockchain is a relatively new technology that has grown in interest and potential research since its inception. Blockchain technology is dominated by cryptocurrency in terms of usage. Research conducted in the past few years, however, reveals blockchain has the potential to revolutionize several different industries. The blockchain consists of three major technologies: a peer-to-peer network, a distributed database, and asymmetrically encrypted transactions. The peer-to-peer network enables a decentralized, consensus-based network structure where various nodes contribute to the overall network performance. A distributed database adds additional security and immutability to the network. The process of cryptographically securing individual transactions forms a core service of the blockchain and enables semi-anonymous user network presence

    The Cooperative Defense Overlay Network: A Collaborative Automated Threat Information Sharing Framework for a Safer Internet

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    With the ever-growing proliferation of hardware and software-based computer security exploits and the increasing power and prominence of distributed attacks, network and system administrators are often forced to make a difficult decision: expend tremendous resources on defense from sophisticated and continually evolving attacks from an increasingly dangerous Internet with varying levels of success; or expend fewer resources on defending against common attacks on "low hanging fruit," hoping to avoid the less common but incredibly devastating zero-day worm or botnet attack. Home networks and small organizations are usually forced to choose the latter option and in so doing are left vulnerable to all but the simplest of attacks. While automated tools exist for sharing information about network-based attacks, this sharing is typically limited to administrators of large networks and dedicated security-conscious users, to the exclusion of smaller organizations and novice home users. In this thesis we propose a framework for a cooperative defense overlay network (CODON) in which participants with varying technical abilities and resources can contribute to the security and health of the internet via automated crowdsourcing, rapid information sharing, and the principle of collateral defense

    Framework for botnet emulation and analysis

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    Criminals use the anonymity and pervasiveness of the Internet to commit fraud, extortion, and theft. Botnets are used as the primary tool for this criminal activity. Botnets allow criminals to accumulate and covertly control multiple Internet-connected computers. They use this network of controlled computers to flood networks with traffic from multiple sources, send spam, spread infection, spy on users, commit click fraud, run adware, and host phishing sites. This presents serious privacy risks and financial burdens to businesses and individuals. Furthermore, all indicators show that the problem is worsening because the research and development cycle of the criminal industry is faster than that of security research. To enable researchers to measure botnet connection models and counter-measures, a flexible, rapidly augmentable framework for creating test botnets is provided. This botnet framework, written in the Ruby language, enables researchers to run a botnet on a closed network and to rapidly implement new communication, spreading, control, and attack mechanisms for study. This is a significant improvement over augmenting C++ code-bases for the most popular botnets, Agobot and SDBot. Rubot allows researchers to implement new threats and their corresponding defenses before the criminal industry can. The Rubot experiment framework includes models for some of the latest trends in botnet operation such as peer-to-peer based control, fast-flux DNS, and periodic updates. Our approach implements the key network features from existing botnets and provides the required infrastructure to run the botnet in a closed environment.Ph.D.Committee Chair: Copeland, John; Committee Member: Durgin, Gregory; Committee Member: Goodman, Seymour; Committee Member: Owen, Henry; Committee Member: Riley, Georg

    Advanced monitoring in P2P botnets

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    Botnets are increasingly being held responsible for most of the cybercrimes that occur nowadays. They are used to carry out malicious activities like banking credential theft and Distributed Denial of Service (DDoS) attacks to generate profit for their owner, the botmaster. Traditional botnets utilized centralized and decentralized Command-and-Control Servers (C2s). However, recent botnets have been observed to prefer P2P-based architectures to overcome some of the drawbacks of the earlier architectures. A P2P architecture allows botnets to become more resilient and robust against random node failures and targeted attacks. However, the distributed nature of such botnets requires the defenders, i.e., researchers and law enforcement agencies, to use specialized tools such as crawlers and sensor nodes to monitor them. In return to such monitoring, botmasters have introduced various countermeasures to impede botnet monitoring, e.g., automated blacklisting mechanisms. The presence of anti-monitoring mechanisms not only render any gathered monitoring data to be inaccurate or incomplete, it may also adversely affect the success rate of botnet takedown attempts that rely upon such data. Most of the existing monitoring mechanisms identified from the related works only attempt to tolerate anti-monitoring mechanisms as much as possible, e.g., crawling bots with lower frequency. However, this might also introduce noise into the gathered data, e.g., due to the longer delay for crawling the botnet. This in turn may also reduce the quality of the data. This dissertation addresses most of the major issues associated with monitoring in P2P botnets as described above. Specifically, it analyzes the anti-monitoring mechanisms of three existing P2P botnets: 1) GameOver Zeus, 2)Sality, and 3) ZeroAccess, and proposes countermeasures to circumvent some of them. In addition, this dissertation also proposes several advanced anti-monitoring mechanisms from the perspective of a botmaster to anticipate future advancement of the botnets. This includes a set of lightweight crawler detection mechanisms as well as several novel mechanisms to detect sensor nodes deployed in P2P botnets. To ensure that the defenders do not loose this arms race, this dissertation also includes countermeasures to circumvent the proposed anti-monitoring mechanisms. Finally, this dissertation also investigates if the presence of third party monitoring mechanisms, e.g., sensors, in botnets influences the overall churn measurements. In addition, churn models for Sality and ZeroAccess are also derived using fine-granularity churn measurements. The works proposed in this dissertation have been evaluated using either real-world botnet datasets, i.e., that were gathered using crawlers and sensor nodes, or simulated datasets. Evaluation results indicate that most of the anti-monitoring mechanisms implemented by existing botnets can either be circumvented or tolerated to obtain monitoring data with a better quality. However, many crawlers and sensor nodes in existing botnets are found vulnerable to the antimonitoring mechanisms that are proposed from the perspective of a botmaster in this dissertation. Analysis of the fine-grained churn measurements for Sality and ZeroAccess indicate that churn in these botnets are similar to that of regular P2P file-sharing networks like Gnutella and Bittorent. In addition, the presence of highly responsive sensor nodes in the botnets are found not influencing the overall churn measurements. This is mainly due to low number of sensor nodes currently deployed in the botnets. Existing and future botnet monitoring mechanisms should apply the findings of this dissertation to ensure high quality monitoring data, and to remain undetected from the bots or the botmasters
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