Attaining Fairness in Communication for Omniscience

This paper studies how to attain fairness in communication for omniscience that models the multi-terminal compress sensing problem and the coded cooperative data exchange problem where a set of users exchange their observations of a discrete multiple random source to attain omniscience—the state that all users recover the entire source. The optimal rate region containing all source coding rate vectors that achieve omniscience with the minimum sum rate is shown to coincide with the core (the solution set) of a coalitional game. Two game-theoretic fairness solutions are studied: the Shapley value and the egalitarian solution. It is shown that the Shapley value assigns each user the source coding rate measured by their remaining information of the multiple source given the common randomness that is shared by all users, while the egalitarian solution simply distributes the rates as evenly as possible in the core. To avoid the exponentially growing complexity of obtaining the Shapley value, a polynomial-time approximation method is proposed which utilizes the fact that the Shapley value is the mean value over all extreme points in the core. In addition, a steepest descent algorithm is proposed that converges in polynomial time on the fractional egalitarian solution in the core, which can be implemented by network coding schemes. Finally, it is shown that the game can be decomposed into subgames so that both the Shapley value and the egalitarian solution can be obtained within each subgame in a distributed manner with reduced complexity.

Digital Immunity Module: Preventing Unwanted Encryption using Source Coding

Unwanted data encryption, such as ransomware attacks, continues to be a significant cybersecurity threat. Ransomware is a preferred weapon of cybercriminals who target small to large organizations' computer systems and data centres. It is malicious software that infects a victim's computer system and encrypts all its valuable data files. The victim needs to pay a ransom, often in cryptocurrency, in return for a decryption key. Many solutions use methods, including the inspection of file signatures, runtime process behaviors, API calls, and network traffic, to detect ransomware code. However, unwanted data encryption is still a top threat. This paper presents the first immunity solution, called the digital immunity module (DIM). DIM focuses on protecting valuable business-related data files from unwanted encryption rather than detecting malicious codes or processes. We show that methods such as file entropy and fuzzy hashing can be effectively used to sense unwanted encryption on a protected file, triggering our novel source coding method to paralyze the malicious manipulation of data such as ransomware encryption. Specifically, maliciously encrypted data blocks consume exponentially larger space and longer writing time on the DIM-protected file system. As a result, DIM creates enough time for system/human intervention and forensics analysis. Unlike the existing solutions, DIM protects the data regardless of ransomware families and variants. Additionally, DIM can defend against simultaneously active multiple ransomware, including the most recent hard to detect and stop fileless ones. We tested our solution on 39 ransomware families, including the most recent ransomware attacks. DIM successfully defended our sample file dataset (1335 pdf, jpg, and tiff files) against those ransomware attacks with zero file loss.

Digital Immunity Module: Preventing Unwanted Encryption using Source Coding

Unwanted data encryption, such as ransomware attacks, continues to be a significant cybersecurity threat. Ransomware is a preferred weapon of cybercriminals who target small to large organizations' computer systems and data centres. It is malicious software that infects a victim's computer system and encrypts all its valuable data files. The victim needs to pay a ransom, often in cryptocurrency, in return for a decryption key. Many solutions use methods, including the inspection of file signatures, runtime process behaviors, API calls, and network traffic, to detect ransomware code. However, unwanted data encryption is still a top threat. This paper presents the first immunity solution, called the digital immunity module (DIM). DIM focuses on protecting valuable business-related data files from unwanted encryption rather than detecting malicious codes or processes. We show that methods such as file entropy and fuzzy hashing can be effectively used to sense unwanted encryption on a protected file, triggering our novel source coding method to paralyze the malicious manipulation of data such as ransomware encryption. Specifically, maliciously encrypted data blocks consume exponentially larger space and longer writing time on the DIM-protected file system. As a result, DIM creates enough time for system/human intervention and forensics analysis. Unlike the existing solutions, DIM protects the data regardless of ransomware families and variants. Additionally, DIM can defend against simultaneously active multiple ransomware, including the most recent hard to detect and stop fileless ones. We tested our solution on 39 ransomware families, including the most recent ransomware attacks. DIM successfully defended our sample file dataset (1335 pdf, jpg, and tiff files) against those ransomware attacks with zero file loss.

Grid Lifespan Enlargement for Assessment in Multihop Wireless Detector Facilities

In energy limited wireless sensor networks, both local quantization andmultihop transmission are essential to save transmission energy and thus prolong the network lifetime. The goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional.The network lifetime optimization problem includes three components: Optimizing source coding at each sensor node, optimizing source throughput at each sensor node.Optimizing multihop routing path. Source coding optimization can be decoupled from source throughput and multihop routing path optimization and is solved by introducing a concept of equivalent 1-bit Mean Square Error (MSE) function. Based on optimal source coding, multihop routing path optimization is formulated as a linear programming problem, which suggests a new notion of character based routing. It is also seen that optimal multihop routing improves the network lifetime bound significantly compared with single-hop routing for heterogeneous networks. Furthermore, the gain is more significant when the network is denser since there are more opportunities for multihop routing. Also the gain is more significant when the observation noise variances are more diverse.