Network Forensic Analysis by Correlation of Attacks with Network Attributes

Kaushik, Atul Kant; Pilli, Emmanuel S.; Joshi, R. C.

doi:10.1007/978-3-642-15766-0_18

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…Further, maintaining network logs is not trivial, and analysis often requires the inspection of large files . To expedite the attribution mechanism, forensics can also be integrated with visualization tools .…”

Section: Survey Of Attribution Techniquesmentioning

confidence: 99%

Attribution in cyberspace: techniques and legal implications

Shamsi

Zeadally

Sheikh

et al. 2016

Security Comm Networks

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Attribution of cybercrimes is significant in limiting the rate of crime as well as in preparing the required level of response. Motivated by this significance, we introduce a level-based approach for achieving attribution. In our proposed approach, attribution consists of three steps: (1) identification of the cyberweapon used; (2) determination of the origin of the attack; and (3) identification of the actual attacker. We conduct an in-depth analysis of recently proposed attribution techniques. Our analysis reveals that indirect methods of attribution are particularly effective when attributing cybercrimes; many of them remain unattributed. We also discuss some of the legal issues pertaining to attribution, and we argue that well-defined international laws for cyberspace along with strong cooperation among governments are needed to track down and punish cybercriminals.

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Section: Survey Of Attribution Techniquesmentioning

confidence: 99%

Attribution in cyberspace: techniques and legal implications

Shamsi

Zeadally

Sheikh

et al. 2016

Security Comm Networks

View full text Add to dashboard Cite

show abstract

“…Analysis of large data streams is essential in many domains, including natural language processing [2], load balancing [3], and forensic analysis [4]. Typically, the data volume renders exact analysis algorithms too expensive.…”

Section: Introductionmentioning

confidence: 99%

SALSA: Self-Adjusting Lean Streaming Analytics

Basat¹,

Einziger²,

Mitzenmacher³

et al. 2021

Preprint

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Counters are the fundamental building block of many data sketching schemes, which hash items to a small number of counters and account for collisions to provide good approximations for frequencies and other measures. Most existing methods rely on fixed-size counters, which may be wasteful in terms of space, as counters must be large enough to eliminate any risk of overflow. Instead, some solutions use small, fixed-size counters that may overflow into secondary structures.This paper takes a different approach. We propose a simple and general method called SALSA for dynamic re-sizing of counters, and show its effectiveness. SALSA starts with small counters, and overflowing counters simply merge with their neighbors. SALSA can thereby allow more counters for a given space, expanding them as necessary to represent large numbers. Our evaluation demonstrates that, at the cost of a small overhead for its merging logic, SALSA significantly improves the accuracy of popular schemes (such as Count-Min Sketch and Count Sketch) over a variety of tasks. Our code is released as open source [1].

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Combating the Distributed Network Attacks using A Proposed Progressive Analyzer based on an Ensemble Learning Framework

Thomas¹,

Meshram²

2022

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Network Forensic Analysis by Correlation of Attacks with Network Attributes

Cited by 7 publications

References 4 publications

Attribution in cyberspace: techniques and legal implications

Attribution in cyberspace: techniques and legal implications

SALSA: Self-Adjusting Lean Streaming Analytics

Combating the Distributed Network Attacks using A Proposed Progressive Analyzer based on an Ensemble Learning Framework

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