A Novel IP Trace-back Mechanism for Identifying IP Spoofers

It is for quite a while known aggressors may use made source IP address to hide their genuine ranges. To get the spoofers,a number of IPtraceback frameworks have been proposed. In any case, because of the difficulties of arrangement, there has been not a broadly adopted IPtraceback arrangement, at any rate at the Internet level. Thusly, the fog on the territories of spoofers has never been scattered till now. This proposes idle IPtraceback that avoids the association inconveniences of IPtraceback techniques. PIT looks at Internet Control Message Protocol bungle messages actuated by parodying development, and tracks the spoofers considering open accessible data

Efficient IP Trace back Mechanism for Identifying IP Spoofers

- It is well known that aggressors or spoofers may utilize fake source IP address to hide their genuine areas from victims. So, to catch these spoofers a number of  techniques for tracing IP address have been proposed .But, because of the challenges of deployment of those techniques ,they have not been widely adopted, at least at the Internet level. So,that is why we can’t end the attacks made by spoofers. This proposes inactive IP trace back that side steps the organization troubles of IP trace back methods. PIT looks at Internet Control Message Protocol bungle messages enacted by parodying development, and tracks the spoofers considering open accessible data

IP TRACEBACK Scenarios

Internet Protocol (IP) trace back is the enabling technology to control Internet crime. In this paper, we present novel and practical IP traceback systems which provide a defense system with the ability to find out the real sources of attacking packets that traverse through the network. IP traceback is to find the origin of an IP packet on the Internet without relying on the source IP address field. Due to the trusting nature of the IP protocol, the source IP address of a packet is not authenticated. As a result, the source address in an IP packet can be falsified (IP address spoofing). Spoof IP packets can be used for different attacks. The problem of finding the source of a packet is called the IP traceback problem. IP Traceback is a critical ability for identifying sources of attacks and instituting protection measures for the Internet. Most existing approaches to this problem have been tailored toward DDoS attack detection

DISCLOSING THE LOCATIONS OF IP SPOOFERS FROM PATHWAY BACKSCATTER IN PASSIVE IP TRACEBACK

It is very long known attackers may use forged source IP address to obscure their real locations. To capture the spoofers, a number of IP traceback mechanisms have been proposed. However, due to the challenges of deployment, there has been not a widely adopted IP traceback solution, at least at the Internet level. As a result, the mist on the locations of spoofers has never been dissipated till now. This paper proposes passive IP traceback (PIT) that bypasses the deployment difficulties of IP traceback techniques. PIT investigates Internet Control Message Protocol error messages (named path backscatter) triggered by spoofing traffic, and tracks the spoofers based on public available information (e.g., topology). In this way, PIT can find the spoofers without any deployment requirement. This paper illustrates the causes, collection, and the statistical results on path backscatter, demonstrates the processes and effectiveness of PIT, and shows the captured locations of spoofers through applying PIT on the path backscatter data set. These results can help further reveal IP spoofing, which has been studied for long but never well understood. Though PIT cannot work in all the spoofing attacks, it may be the most useful mechanism to trace spoofers before an Internet-level traceback system has been deployed in real

On packet marking and Markov modeling for IP Traceback: A deep probabilistic and stochastic analysis

From many years, the methods to defend against Denial of Service attacks have been very attractive from different point of views, although network security is a large and very complex topic. Different techniques have been proposed and so-called packet marking and IP tracing procedures have especially demonstrated a good capacity to face different malicious attacks. While host-based DoS attacks are more easily traced and managed, network-based DoS attacks are a more challenging threat. In this paper, we discuss a powerful aspect of the IP traceback method, which allows a router to mark and add information to attack packets on the basis of a fixed probability value. We propose a potential method for modeling the classic probabilistic packet marking algorithm as Markov chains, allowing a closed form to be obtained for evaluating the correct number of received marked packets in order to build a meaningful attack graph and analyze how marking routers must behave to minimize the overall overhead

On the nonexistence of k reptile simplices in ℝ^3 and ℝ^4

A d-dimensional simplex S is called a k-reptile (or a k-reptile simplex) if it can be tiled by k simplices with disjoint interiors that are all mutually congruent and similar to S. For d = 2, triangular k-reptiles exist for all k of the form a^2, 3a^2 or a^2+b^2 and they have been completely characterized by Snover, Waiveris, and Williams. On the other hand, the only k-reptile simplices that are known for d ≥ 3, have k = m^d, where m is a positive integer. We substantially simplify the proof by Matoušek and the second author that for d = 3, k-reptile tetrahedra can exist only for k = m^3. We then prove a weaker analogue of this result for d = 4 by showing that four-dimensional k-reptile simplices can exist only for k = m^2

A multi-disciplinary framework for cyber attribution

Effective Cyber security is critical to the prosperity of any nation in the modern world. We have become dependant upon this interconnected network of systems for a number of critical functions within society. As our reliance upon this technology has increased, as has the prospective gains for malicious actors who would abuse these systems for their own personal benefit, at the cost of legitimate users. The result has been an explosion of cyber attacks, or cyber enabled crimes. The threat from hackers, organised criminals and even nations states is ever increasing. One of the critical enablers to our cyber security is that of cyber attribution, the ability to tell who is acting against our systems. A purely technical approach to cyber attribution has been found to be ineffective in the majority of cases, taking too narrow approach to the attribution problem. A purely technical approach will provide Indicators Of Compromise (IOC) which is suitable for the immediate recovery and clean up of a cyber event. It fails however to ask the deeper questions of the origin of the attack. This can be derived from a wider set of analysis and additional sources of data. Unfortunately due to the wide range of data types and highly specialist skills required to perform the deep level analysis there is currently no common framework for analysts to work together towards resolving the attribution problem. This is further exasperated by a communication barrier between the highly specialised fields and no obviously compatible data types. The aim of the project is to develop a common framework upon which experts from a number of disciplines can add to the overall attribution picture. These experts will add their input in the form of a library. Firstly a process was developed to enable the creation of compatible libraries in different specialist fields. A series of libraries can be used by an analyst to create an overarching attribution picture. The framework will highlight any intelligence gaps and additionally an analyst can use the list of libraries to suggest a tool or method to fill that intelligence gap. By the end of the project a working framework had been developed with a number of libraries from a wide range of technical attribution disciplines. These libraries were used to feed in real time intelligence to both technical and nontechnical analysts who were then able to use this information to perform in depth attribution analysis. The pictorial format of the framework was found to assist in the breaking down of the communication barrier between disciplines and was suitable as an intelligence product in its own right, providing a useful visual aid to briefings. The simplicity of the library based system meant that the process was easy to learn with only a short introduction to the framework required