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NR: Architecture, Protocol, Challenges, and Applications
Published in Mangesh M. Ghonge, Ramchandra Sharad Mangrulkar, Pradip M. Jawandhiya, Nitin Goje, Future Trends in 5G and 6G, 2021
Virendra A. Uppalwar, Trupti S. Pandilwar
Why the user device is threatened? The answer is a variation of connectivity preferences over multiple technologies and the online presence of more users. 5G devices are not just connected with the 5G network but can connect with WiFi, IMS, Bluetooth, NFV. This inter-connectivity is vulnerable to a security threat. The popularity of unsecured devices is more because of the low cost of devices and this is also a cupcake for hackers. These low-cost devices accept the open market operating systems (OS) and third-party applications, which is also a loophole in the security process. Mobile malware is a personal security threat. The user always prefers the free and unsecured applications downloaded from an untrusted app store, which creates a space for the malware attack. This malware attack targets users to exploit or steal personal information like photos, contact, bank details, etc.
A Bio-inspired Approach To Cyber Security
Published in Brij B. Gupta, Michael Sheng, Machine Learning for Computer and Cyber Security, 2019
Siyakha N. Mthunzi, Elhadj Benkhelifa, Tomasz Bosakowski, Salim Hariri
Ant colonies have been applied for routing traffic optimization; for instance, in works by [116] who evaluate an optimization algorithm, AntNet, in which agents concurrently traverse a network and exchange information synonymous with stigmergy in insects. According to the authors, this algorithm exhibited superior performance in contrast to its competitors [135]. [136] proposed FBeeAd-Hoc as a security framework for routing problems in mobile ad hoc networks (MANET) using fuzzy set theory and digital signature [136]. Works by [80] extend on previous work on the predator model, to propose countermeasures against automated mobile malware in networks. The author proposes additional entities, including immunization, persistent and seeking predators, modeled from the self-propagating, self-defending and mobility attributes found in predating animals as solutions to challenges mentioned above. Their works premise on the notion that traditional countermeasures, which are generally centralized, fail adequately to solve security challenges existing in distributed systems [80]. According to the authors, their model does not only counter effect malware attacks on a computer, but effectively distributes updates and patches to the infected computer, which in essence immunizes it from future attacks [80]. [137] suggest the use of predator models as inspirational solutions against viruses and worms.
A Review of Intrusion Detection and Prevention on Mobile Devices: The Last Decade
Published in Georgios Kambourakis, Asaf Shabtai, Constantinos Kolias, Dimitrios Damopoulos, Intrusion Detection and Prevention for Mobile Ecosystems, 2017
Weizhi Meng, Jianying Zhou, Lam-For Kwok
Smartphone vendors will ship more than 450 million devices in 2011. At the same period, mobile malware was propagated in a quick manner; therefore, many IDSs/IPSs were built aiming to perform the detection of malware. Chaugule et al. [28] found that mobile malware always attempts to access sensitive system services on the mobile phone in an unobtrusive and stealthy fashion. For example, the malware may send messages automatically or stealthily interface with the audio peripherals on the device without the user's awareness and authorization. They then presented SBIDF, a Specification Based Intrusion Detection Framework, which utilizes the keypad or touchscreen interrupts to differentiate between malware and human activity. In the system, they utilized an application-independent specification, written in Temporal Logic of Causal Knowledge (TLCK), to describe the normal behavior pattern, and enforced this specification to all third-party applications on the mobile phone during runtime by monitoring the intercomponent communication pattern among critical components.
Understanding the inward emotion-focused coping strategies of individual users in response to mobile malware threats
Published in Behaviour & Information Technology, 2022
Tong Xin, Mikko Siponen, Sihua Chen
This study was conducted in the mobile malware context. Because smartphones have been commercially available for decades, users may believe that the security features of these devices block most cyberattacks, which causes them to relax their vigilance regarding malware threats. Moreover, malware threats are not new, and their repeated exposure may also desensitise users. Therefore, users may have a lower-than-expected threat appraisal of malware threats. If an individual believes that the consequences of a threat are not severe, even if PFC were not adopted, they may employ inward EFC strategies that are less demanding, such as ignoring a threat or distorting the threat perception. In contrast, wishful thinking has been considered conceptually similar to unrealistic optimism (Liang et al. 2019). Individuals who are unrealistically optimistic usually have the mistaken belief that their chances of encountering negative events or threats are lower than those of others. This reaction is in response to the reluctance to accept their vulnerability to the threat (Weinstein 1980, 1982), which could be the reason that wishful thinking was shown to be negatively related to the perceived threat vulnerability of individuals (McMath and Prentice-Dunn 2005).
A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques
Published in International Journal of Computers and Applications, 2022
Drebin, Contagio, and Genome are popular malware datasets, which are used to detect and classify widespread malware [26]. Genome dataset contains different types of Android malware (Collection duration: August 2010- October 2011) [27]. Drebin dataset contains real Android malware and real Android application samples from different websites (Collection duration: August 2010- October 2012) [28]. The Contagio dataset contains mobile malware samples as well as benign samples. This dataset is online accessible at Contagio Malware Dump [29].