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IEEE 2014 JAVA NETWORK SECURITY PROJECTS Behavioral malware detection in delay tolerant networks

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IEEE 2014 JAVA NETWORK SECURITY PROJECTS Behavioral malware detection in delay tolerant networks

  1. 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com BEHAVIORAL MALWARE DETECTION IN DELAY TOLERANT NETWORKS ABSTRACT The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern attaching in detecting malware, especially when dealing with polymorphic or obfuscated malware. In this paper, we first propose a general behavioral characterization of proximity malware which based on Naive Bayesian model, which has been successfully applied in non- DTN settings such as filtering email spams and detecting botnets. We identify two unique challenges for extending Bayesian malware detection to DTNs and propose a simple yet effective method, look-ahead, to address the challenges. Furthermore, we propose two extensions to look-ahead, dogmatic filtering and adaptive look-ahead, to address the challenge of “malicious nodes sharing false evidence”. Real mobile network traces are used to verify the effectiveness of the proposed methods. EXISTING SYSTEM Previous researches quantify the threat of proximity malware attack and demonstrate the possibility of launching such an attack, which is confirmed by recent reports on hijacking hotel Wi-Fi hotspots for drive-by malware attack. With the adoption of new short-range communication technologies such as NFC and Wi-Fi Direct that facilitate spontaneous bulk data
  2. 2. transfer between spatially proximate mobile devices, the threat of proximity malware is becoming more realistic and relevant than ever. Proximity malware based on the DTN model brings unique security challenges that are not present in the model. EXISTING TECHNIQUE Proximity malware DISADVANTAGE  Central monitoring and resource limits are absent in the DTN model.  Very risk to collecting evidence and also having insufficient evidence.  It is filter the false evidence in sequentially and distributedly. PROPOSED SYSTEM Behavioral characterization, in terms of system call and program flow, has been previously proposed as an effective alternative to pattern matching for malware detection. In our model, malware-infected nodes’ behaviors are observed by others during their multiple opportunistic encounters: Individual observations may be imperfect, but abnormal behaviors of infected nodes are identifiable in the long-run. We identify challenges for extending Bayesian malware detection to DTNs, and propose a simple yet effective method, look-ahead, to address the challenges. Furthermore, we propose two extensions to look-ahead, dogmatic filtering and adaptive look-ahead, to address the challenge of “malicious nodes sharing false evidence”. PROPOSED TECHNIQUE  General behavioral characterization ADVANTAGE  Real mobile network traces are used to verify the effectiveness of the proposed methods.  The proposed evidence consolidation strategies in minimizing the negative impact of liars on the shared evidence’s quality.
  3. 3.  It is used to identify the abnormal behaviors of infected nodes in the long-run.  We can design easily. SOFTWARE REQUIREMENTS  Operating system : Windows7  Front End : Microsoft Visual Studio .Net 2010  Coding Language : C#  Backend : SQL Server 2008 HARDWARE REQUIREMENTS  Processor : Pentium Dual Core 2.00GHZ  Hard disk : 40 GB  Mouse : Logitech.  RAM : 2GB(minimum)  Keyboard : 110 keys enhanced. USED TECHNOLOGIES WPF, Stored Procedure, 3-Tier Architecture, C# FUTURE WORK In prospect, extension of the behavioral characterization of proximity malware to account for strategic malware detection evasion with game theory is a challenging yet interesting future work.

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