Malware Propagations in Wireless Ad Hoc Networks

Accurate malware propagation modeling in wireless ad hoc networks (WANETs) represents a fundamental and open research issue which shows distinguished challenges due to complicated access competition, severe channel interference, and dynamic connectivity. As an effort towards the issue, in this paper, we investigate the malware propagation under two spread schemes including Unicast and Broadcast, in Spread Mode and Communication Mode, respectively. We highlight our contributions in three-fold in the light of previous literature works. First, a bound of malware infection rate for each scheme is provided by applying the wireless network capacity theories. Second, the impact of mobility on malware propagations has been studied. Third, discussion of the relationship between different schemes and practical applications is provided. Numerical simulations and detailed performance analysis show that the Broadcast Scheme with Spread Mode is most dangerous in the sense of malware propagation speed in WANETs, and mobility will greatly increase the risk further. The results achieved in this paper not only provide insights on the malware propagation characteristics in WANETs, but also serve as fundamental guidelines on designing defense schemes.

[1]  Eric Mayer,et al.  80211 Wireless Networks The Definitive Guide , 2016 .

[2]  Matthew C. Elder,et al.  On computer viral infection and the effect of immunization , 2000, Proceedings 16th Annual Computer Security Applications Conference (ACSAC'00).

[3]  Alessandro Vespignani,et al.  WiFi networks and malware epidemiology , 2007, Proceedings of the National Academy of Sciences.

[4]  Chuanyi Ji,et al.  Importance-scanning worm using vulnerable-host distribution , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[5]  Maziar Nekovee Modeling the Spread of Worm Epidemics in Vehicular Ad Hoc Networks , 2006, 2006 IEEE 63rd Vehicular Technology Conference.

[6]  A. F. Pacheco,et al.  Epidemic incidence in correlated complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[8]  Panganamala Ramana Kumar,et al.  The Number of Neighbors Needed for Connectivity of Wireless Networks , 2004, Wirel. Networks.

[9]  Ramin Hekmat,et al.  Ad-hoc networks - fundamental properties and network topologies , 2006 .

[10]  Bülent Tavli Broadcast capacity of wireless networks , 2006, IEEE Communications Letters.

[11]  Rose Qingyang Hu,et al.  Neighbor discovery algorithms in directional antenna based synchronous and asynchronous wireless ad hoc networks , 2013, IEEE Wireless Communications.

[12]  Lu Han Wireless Ad Hoc Networks , 2020 .

[13]  Stefan Saroiu,et al.  A preliminary investigation of worm infections in a bluetooth environment , 2006, WORM '06.

[14]  Sajal K. Das,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON MOBILE COMPUTING An Epidemic Theoretic Framework for Vulnerability Analysi , 2022 .

[15]  Maziar Nekovee,et al.  Worm epidemics in wireless ad hoc networks , 2007, ArXiv.

[16]  Stefano Zanero,et al.  BlueBat: Towards Practical Bluetooth Honeypots , 2009, 2009 IEEE International Conference on Communications.

[17]  Jonathan Loo,et al.  The Impact of Rank Attack on Network Topology of Routing Protocol for Low-Power and Lossy Networks , 2013, IEEE Sensors Journal.

[18]  Donald F. Towsley,et al.  Modeling and Simulation Study of the Propagation and Defense of Internet E-mail Worms , 2007, IEEE Transactions on Dependable and Secure Computing.

[19]  Niki Pissinou,et al.  On the Robustness of the Botnet Topology Formed by Worm Infection , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[20]  Yang Xiang,et al.  Modeling the Propagation of Worms in Networks: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[21]  Guanhua Yan,et al.  Modeling Propagation Dynamics of Bluetooth Worms (Extended Version) , 2009, IEEE Transactions on Mobile Computing.

[22]  Carlos Castillo-Chavez,et al.  Mathematical Models in Epidemiology , 2019, Texts in Applied Mathematics.

[23]  Manish Sharma,et al.  Mathematical Models on Epidemiology , 2015 .

[24]  Wanlei Zhou,et al.  Mobility Increases the Risk of Malware Propagations in Wireless Networks , 2015, 2015 IEEE Trustcom/BigDataSE/ISPA.

[25]  Stefano Zanero,et al.  Studying Bluetooth Malware Propagation: The BlueBag Project , 2007, IEEE Security & Privacy.

[26]  George Kesidis,et al.  A model of the spread of randomly scanning Internet worms that saturate access links , 2008, TOMC.

[27]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[28]  Nasser Yazdani,et al.  Critical Transmission Range for Connectivity in Ad-Hoc Networks , 2007 .

[29]  Xun Wang,et al.  Modeling and Detection of Camouflaging Worm , 2011, IEEE Transactions on Dependable and Secure Computing.

[30]  David Tse,et al.  Mobility increases the capacity of ad hoc wireless networks , 2002, TNET.

[31]  Donald F. Towsley,et al.  Code red worm propagation modeling and analysis , 2002, CCS '02.

[32]  Xuemin Shen,et al.  Security and privacy in mobile crowdsourcing networks: challenges and opportunities , 2015, IEEE Communications Magazine.