How Secure Are Android and Apple’s Operating Systems and Based Applications Against Cyber Attacks and Cyber Crime

Smartphone has become an important part of our everyday life. Android and apple are the two most used operating system (OS) for smart phones. We usually store important information in our smart phone, e.g.: credit card, bank account, driving ID, SSN. As a result, Android and Apple operating systems and applications have both been subject to a wide number of vulnerabilities and attacks. This directly effects many people being that they are the global leaders of users within their platforms reaching billions of people daily. It is important that smartphones receive better defense and security. In this paper, we aim to analyze the defense structures in place for both operating systems, possible solutions, and compare the two and examine if one is truly more secure than the other. Android and Apple both have not been as secure with their operating systems as expected leaving many users exposed to cybercrime

Multimedia Forensic Analysis of TikTok Application Using National Institute of Justice (NIJ) Method

The advancement of technology, especially in mobile devices like smartphones, has had a significant impact on human life, particularly during the COVID-19 pandemic, leading to the growth of online activities, especially on social media platforms like TikTok. TikTok is a highly popular social media platform, primarily known for its focus on short videos and images often accompanied by music. However, this has also opened up opportunities for misuse, including the spread of false information and defamation. To address this issue, this research utilizes mobile forensic analysis with Error Level Analysis (ELA) to collect digital evidence related to crimes on TikTok. This research contributes by applying digital forensic techniques, specifically Error Level Analysis (ELA), to detect image manipulation on TikTok. By using forensic methods, this research helps uncover digital crimes occurring on TikTok and provides essential insights to combat misuse and criminal activities on this social media platform. The research aims to collect digital evidence from TikTok on mobile devices using MOBILedit Forensic Express Pro and authenticate it with ELA through tools like FotoForensics and Forensically, as well as manual examination. This research follows the National Institute of Justice (NIJ) methodology with ten stages of mobile forensic investigation, including scenario creation, identification, collection, investigation, and analysis. The research yields manipulated digital evidence from TikTok, primarily concerning upload times. Error Level Analysis (ELA) is used to assess the authenticity of images, revealing signs of manipulation in digital evidence. The research's contribution is to produce or collect manipulated digital evidence from TikTok, primarily concerning upload times, and to apply the Error Level Analysis (ELA) approach or technique to assess the authenticity of images, uncovering signs of manipulation in digital evidence

Encouraging password manager adoption by meeting adopter self-determination needs

Password managers are a potential solution to the password conundrum, but adoption is paltry. We investigated the impact of a recommender application that harnessed the tenets of self-determination theory to encourage adoption of password managers. This theory argues that meeting a person's autonomy, relatedness and competence needs will make them more likely to act. To test the power of meeting these needs, we conducted a factorial experiment, in the wild. We satisfied each of the three self determination factors, and all individual combinations thereof, and observed short-term adoption of password managers. The Android recommender application was used by 470 participants, who were randomly assigned to one of the experimental or control conditions. Our analysis revealed that when all self-determination factors were satisfied, adoption was highest, while meeting only the autonomy or relatedness needs individually significantly improved the likelihood of adoption

Smartphone data evaluation model : identifying authentic smartphone data

Ever improving smartphone technology, along with the widespread use of the devices to accomplish daily tasks, leads to the collection of rich sources of smartphone data. Smartphone data are, however, susceptible to change and can be altered intentionally or accidentally by end-users or installed applications. It becomes, therefore, important to establish the authenticity of smartphone data, confirming the data refer to actual events, before submitting the data as potential evidence. This paper focuses on data created by smartphone applications and the techniques that can be used to establish the authenticity of the data. To identify authentic smartphone data, a better understanding of the smartphone, related smartphone applications and the environment in which the smartphone operates are required. From the gathered knowledge and insight, requirements are identified that authentic smartphone data must adhere to. These requirements are captured in a new model to assist digital forensic professionals with the evaluation of smartphone data. Experiments, involving different smartphones, are conducted to determine the practicality of the new evaluation model with the identification of authentic smartphone data. The presented results provide preliminary evidence that the suggested model offers the necessary guidance to identify authentic smartphone data.http://www.elsevier.com/locate/diinhj2019Computer Scienc

Anti-Hijack: Runtime Detection of Malware Initiated Hijacking in Android

AbstractAccording to studies, Android is having the highest market share in smartphone operating systems. The number of Android apps (i.e. applications) are increasing day by day. Consequent threats and attacks on Android are also rising. There are a large number of apps which bypass users by hiding their functionalities and send users sensitive information and data across the network. Due to flexibility and openness of Android operating system, attack surfaces are being introduced every other day.In this paper, we are addressing detection of two fatal malware attacks; intent based hijacking and authenticated session hijacking. We have used the concept of honey-pot in detection of these two authentication hijacking problems. In order to achieve this, we have tested various apps and their interaction with the honey-pot maintained by real device or an emulator. We have designed benign app as a honey framed app. We argue that hijacking malware can be detected with higher accuracy using our method at run-time as compared to the traditional machine learning methods. Our approach, Anti-Hijack, which has provided the detection accuracy as high as 96%. This has been highly accurate to detect the unwanted interaction between hijacking malware and designed benign app. We have tested our approach on a strong data-set of Android apps for experiment and identifying vulnerable points. Our detection method Anti-Hijack is a novel contribution in this area which provides light weight, device operated run-time detection at hijacking malware

Target and Spacing Sizes for Smartphone User Interfaces for Older Adults: Design Patterns Based on an Evaluation with Users

The use of smartphones is becoming widespread among all sectors of the population. However, developers and designers do not have access to guidance in designing for specific audiences such as older adults. This study investigated optimal target sizes, and spacing sizes between targets, for smartphones user interfaces intended for older adults. Two independent variables were studied – target sizes and spacing between targets – for two common smartphone gestures – tap and swipe. Dependent variables were accuracy rates, task completion times, and participants’ subjective preferences. 40 older adults recruited from several daycare centers participated in both tasks and a post-session questionnaire. The recommendations drawn from the authors’ research support two interaction design patterns relative to touch target sizes for older adults, and are presented in this paper