Emerging threats in mobile communication system

Vashisht, Swati; Gupta, Shubhi; Singh, D Asir Antony Gnana; Mudgal, Ankush

doi:10.1109/iciccs.2016.7542341

Cited by 22 publications

(8 citation statements)

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“…Cyber intrusions are evolving exponentially as computer systems advance [22], [34]. The articles [22] [23] [35] all observed a consistent rise of threats and vulnerabilities that are constantly challenging the updated computers, mobile phones, and other products. Specifically, [23] stated that threats faced today go beyond the well-recognized email spam, and have advanced to more complex threats such as botnets, or ransomware.…”

Section: Surveys On Emerging Threatsmentioning

confidence: 99%

“…Attackers have also been observed to still be using older, simple tools for intrusion as well as the modern, advanced threats, so security professionals require a more diverse and tiered knowledge on how to approach the potential threats [23]. The article [35] highlighted that some threats are so discreet that many times they are not recognized until they have fully infiltrated the target network.…”

Section: Surveys On Emerging Threatsmentioning

confidence: 99%

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Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Hillier¹,

Karroubi²

2022

Preprint

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The threat hunting lifecycle is a complex atmosphere that requires special attention from professionals to maintain security. This paper is a collection of recent work that gives a holistic view of the threat hunting ecosystem, identifies challenges, and discusses the future with the integration of artificial intelligence (AI). We specifically establish a life cycle and ecosystem for privacy-threat hunting in addition to identifying the related challenges. We also discovered how critical the use of AI is in threat hunting. This work paves the way for future work in this area as it provides the foundational knowledge to make meaningful advancements for threat hunting.

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Section: Surveys On Emerging Threatsmentioning

confidence: 99%

Section: Surveys On Emerging Threatsmentioning

confidence: 99%

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Hillier¹,

Karroubi²

2022

Preprint

View full text Add to dashboard Cite

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“…Outdated or unpatched third-party applications in mobile devices poses risks as hackers may exploit the vulnerabilities within those applications. Users that are still using an old mobile device that has a lack of software updates, an untimely patch update or a cease in support for older operating systems are at risk of being compromised by hackers through the holes within the software or the OS itself [4,6,7]. Additionally, there are various application-based threats which consist of (a) Malware, (b) Spyware, (c) Privacy threats and (d) Vulnerable applications.…”

Section: Definition Of a Mobile Devicementioning

confidence: 99%

“…The mobile device will display an image of a malicious system that is trying to install a harmful software or attempting for data extraction. Further added that mobile devices could be misused by attackers in several ways such as creating a fake identity by using the personal information contained inside the mobile device or by selling sensitive or confidential data to the black-market for profit motives [7,8].…”

Section: Definition Of a Mobile Devicementioning

confidence: 99%

Revealing Cyber Threat of Smart Mobile Devices within Digital Ecosystem: User Information Security Awareness

Susanto¹

2021

Data Integrity and Quality

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In recent years, the number of mobile device users has increased at a significant rate due to the rapid technological advancement in mobile technology. While mobile devices are providing more useful features to its users, it has also made it possible for cyber threats to migrate from desktops to mobile devices. Thus, it is important for mobile device users to be aware that their mobile device could be exposed to cyber threats and that users could protect their devices by employing cyber security measures. This study discusses how users in responded to the smart mobile devices (SMD) breaches. A number of behavioural model theories are used to understand the user behaviour towards security features of smart mobile devices. To assess the impact of smart mobile devices (SMD) security and privacy, surveys had been conducted with users, stressing on product preferences, user behaviour of SMD, as well as perceptions on the security aspect of SMD. The results was very interesting, where the findings revealed that there were a lack of positive relationships between SMD users and their level of SMD security awareness. A new framework approach to securing SMD is proposed to ensure that users have strong protection over their data within SMD.

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“…The LVC network initially classifies known malware sites XML API calls as it uses supervised learning, which in turn identifies patterns related to further training in order to identify unknown clusters as malicious or benign information [6]. After training, the LVQ can recognize an unidentified pattern in different classes (webpages) that will help the antimalware detector determine the pattern from each pool of webpages XML API Call requests [5].The Euclidean separation was used to evaluate the inter-and intra-callduration of each webpage XML requests yield unit. D ( j ) = ∑ 〖(xi-wij)^2〗…”

Section: Delineation Of Supervised Deep Learning Vector Quantization To Detect Iotmalwareirmentioning

confidence: 99%

Malicious SPAM Injection Attack Detection on Social Webpage Posts

Arul¹,

Punidha²

2020

Intelligent Systems and Computer Technology

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The social media platforms for teens and genz are highly influential; 39% state that they will use’ buy buttons’ and 25% use smartphones for shopping images. In the meantime, 28 percent of US internet users between 18 and 55 years of age said their aim is to buy via social media during holidays. As these channels become more central to our everyday lives, social media platforms have now become a key vector of attack that businesses cannot neglect anymore. Social media Platforms provide up to 20% more options for delivering malware for consumers, such as advertising, social engineering, equities and plug-ins compare to eCommerce and corporate websites. The suggested version Supervised SD-LVQ used to detect malicious firmware on various social media sites. LVQ classifies the different service calls attacks associated with XML, HTML, JavaScript files and different forms of malicious attacks on social networks. The test results show that 98.70% is genuinely positive and 0.02% is falsely negative.

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Emerging threats in mobile communication system

Cited by 22 publications

References 0 publications

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Revealing Cyber Threat of Smart Mobile Devices within Digital Ecosystem: User Information Security Awareness

Malicious SPAM Injection Attack Detection on Social Webpage Posts

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