Rigorously characterizing the statistical properties of cyber attacks is an important problem. In this paper, we propose the first statistical framework for rigorously analyzing honeypot-captured cyber attack data. The framework is built on the novel concept of stochastic cyber attack process , a new kind of mathematical objects for describing cyber attacks. To demonstrate use of the framework, we apply it to analyze a low-interaction honeypot dataset, while noting that the framework can be equally applied to analyze high-interaction honeypot data that contains richer information about the attacks. The case study finds, for the first time, that Long-Range Dependence (LRD) is exhibited by honeypot-captured cyber attacks. The case study confirms that by exploiting the statistical properties (LRD in this case), it is feasible to predict cyber attacks (at least in terms of attack rate) with good accuracy. This kind of prediction capability would provide sufficient early-warning time for defenders to adjust their defense configurations or resource allocations. The idea of "gray-box" (rather than "black-box") prediction is central to the utility of the statistical framework, and represents a significant step towards ultimately understanding (the degree of) the predictability of cyber attacks.
In terms of using honeypots to improve defense, we note that honeypots have been used to help detect various attacks including DoS (denial-of-service) @cite_43 , worms @cite_41 @cite_52 , botnets @cite_33 @cite_15 @cite_24 , Internet-Messaging threats @cite_30 , generating attack signatures @cite_16 @cite_20 , and detecting targeted attacks @cite_53 . These studies are important, but are orthogonal to the focus of the present paper.
@cite_30 @cite_33 @cite_41 @cite_53 @cite_52 @cite_24 @cite_43 @cite_15 @cite_16 @cite_20
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A honeypot is a closely monitored network decoy serving several purposes: it can distract adversaries from more valuable machines on a network, provide early warning about new attack and exploitation trends, or allow in-depth examination of adversaries during and after exploitation of a honeypot. Deploying a physical honeypot is often time intensive and expensive as different operating systems require specialized hardware and every honeypot requires its own physical system. This paper presents Honeyd, a framework for virtual honeypots that simulates virtual computer systems at the network level. The simulated computer systems appear to run on unallocated network addresses. To deceive network fingerprinting tools, Honeyd simulates the networking stack of different operating systems and can provide arbitrary routing topologies and services for an arbitrary number of virtual systems. This paper discusses Honeyd's design and shows how the Honeyd framework helps in many areas of system security, e.g. detecting and disabling worms, distracting adversaries, or preventing the spread of spam email. The rapid evolution of large-scale worms, viruses and bot-nets have made Internet malware a pressing concern. Such infections are at the root of modern scourges including DDoS extortion, on-line identity theft, SPAM, phishing, and piracy. However, the most widely used tools for gathering intelligence on new malware -- network honeypots -- have forced investigators to choose between monitoring activity at a large scale or capturing behavior with high fidelity. In this paper, we describe an approach to minimize this tension and improve honeypot scalability by up to six orders of magnitude while still closely emulating the execution behavior of individual Internet hosts. We have built a prototype honeyfarm system, called Potemkin, that exploits virtual machines, aggressive memory sharing, and late binding of resources to achieve this goal. While still an immature implementation, Potemkin has emulated over 64,000 Internet honeypots in live test runs, using only a handful of physical servers. In modern days, breakthroughs in information and communications technologies lead to more and more devices of every imaginable type being connected to the Internet. This also strengthens the need for protection against cyber-attacks, as virtually any devices with a wireless connection could be vulnerable to malicious hacking attempts. Meanwhile, honeypot-based deception mechanism has been considered as one of the methods to ensure security for modern networks in the Internet of Things (IoT). In this paper, we address the problem of defending against attacks in honeypot-enabled networks by looking at a game-theoretic model of deception involving an attacker and a defender. The attacker may try to deceive the defender by employing different types of attacks ranging from a suspicious to a seemingly normal activity, while the defender in turn can make use of honeypots as a tool of deception to trap attackers. The problem is modeled as a Bayesian game of incomplete information, where equilibria are identified for both the one-shot game and the repeated game versions. Our results show that there is a threshold for the frequency of active attackers, above which both players will take deceptive actions and below which the defender can mix up his her strategy while keeping the attacker’s success rate low. Most of computer security systems use the signatures of well-known attacks to detect hackers' attacks. For these systems, it is very important to get the accurate signatures of new attacks as soon as possible. For this reason, there have been several researches on honeypots. However, honeypots can not collect information about hackers attacking active computers except themselves. In this paper, we propose the DecoyPort system to redirect hackers toward honeypots. The DecoyPort system creates the DecoyPorts on active computers. All interactions with the DecoyPorts are considered as suspect because the ports are not those for real services. Accordingly, every request sent to the DecoyPorts is redirected to honeypots by the DecoyPort system. Consequently, our system enables honeypots to collect information about hackers attacking active computers except themselves.