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Register a new userA Survey on Smartphones Security: Software Vulnerabilities, Malware, and Attacks
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Added by
Milad Taleby Ahvanooey
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Nowadays, the usage of smartphones and their applications have become rapidly increasing popular in peoples daily life. Over the last decade, availability of mobile money services such as mobile-payment systems and app markets have significantly increased due to the different forms of apps and connectivity provided by mobile devices such as 3G, 4G, GPRS, and Wi-Fi, etc. In the same trend, the number of vulnerabilities targeting these services and communication networks has raised as well. Therefore, smartphones have become ideal target devices for malicious programmers. With increasing the number of vulnerabilities and attacks, there has been a corresponding ascent of the security countermeasures presented by the researchers. Due to these reasons, security of the payment systems is one of the most important issues in mobile payment systems. In this survey, we aim to provide a comprehensive and structured overview of the research on security solutions for smartphone devices. This survey reviews the state of the art on security solutions, threats, and vulnerabilities during the period of 2011-2017, by focusing on software attacks, such those to smartphone applications. We outline some countermeasures aimed at protecting smartphones against these groups of attacks, based on the detection rules, data collections and operating systems, especially focusing on open source applications. With this categorization, we want to provide an easy understanding for users and researchers to improve their knowledge about the security and privacy of smartphones.
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Increasingly Industrial Control Systems (ICS) systems are being connected to the Internet to minimise the operational costs and provide additional flexibility. These control systems such as the ones used in power grids, manufacturing and utilities operate continually and have long lifespans measured in decades rather than years as in the case of IT systems. Such industrial control systems require uninterrupted and safe operation. However, they can be vulnerable to a variety of attacks, as successful attacks on critical control infrastructures could have devastating consequences to the safety of human lives as well as a nations security and prosperity. Furthermore, there can be a range of attacks that can target ICS and it is not easy to secure these systems against all known attacks let alone unknown ones. In this paper, we propose a software enabled security architecture using Software Defined Networking (SDN) and Network Function Virtualisation (NFV) that can enhance the capability to secure industrial control systems. We have designed such an SDN/NFV enabled security architecture and developed a Control System Security Application (CSSA) in SDN Controller for enhancing security in ICS against certain specific attacks namely denial of service attacks, from unpatched vulnerable control system components and securing the communication flows from the legacy devices that do not support any security functionality. In this paper, we discuss the prototype implementation of the proposed architecture and the results obtained from our analysis.
Smartphones contain information that is more sensitive and personal than those found on computers and laptops. With an increase in the versatility of smartphone functionality, more data has become vulnerable and exposed to attackers. Successful mobile malware attacks could steal a users location, photos, or even banking information. Due to a lack of post-attack strategies firms also risk going out of business due to data theft. Thus, there is a need besides just detecting malware intrusion in smartphones but to also identify the data that has been stolen to assess, aid in recovery and prevent future attacks. In this paper, we propose an accessible, non-intrusive machine learning solution to not only detect malware intrusion but also identify the type of data stolen for any app under supervision. We do this with Android usage data obtained by utilising publicly available data collection framework- SherLock. We test the performance of our architecture for multiple users on real-world data collected using the same framework. Our architecture exhibits less than 9% inaccuracy in detecting malware and can classify with 83% certainty on the type of data that is being stolen.
Internet of Things (IoT) consists of a large number of devices connected through a network, which exchange a high volume of data, thereby posing new security, privacy, and trust issues. One way to address these issues is ensuring data confidentiality using lightweight encryption algorithms for IoT protocols. However, the design and implementation of such protocols is an error-prone task; flaws in the implementation can lead to devastating security vulnerabilities. Here we propose a new verification approach named Encryption-BMC and Fuzzing (EBF), which combines Bounded Model Checking (BMC) and Fuzzing techniques to check for security vulnerabilities that arise from concurrent implementations of cyrptographic protocols, which include data race, thread leak, arithmetic overflow, and memory safety. EBF models IoT protocols as a client and server using POSIX threads, thereby simulating both entities communication. It also employs static and dynamic verification to cover the systems state-space exhaustively. We evaluate EBF against three benchmarks. First, we use the concurrency benchmark from SV-COMP and show that it outperforms other state-of-the-art tools such as ESBMC, AFL, Lazy-CSeq, and TSAN with respect to bug finding. Second, we evaluate an open-source implementation called WolfMQTT. It is an MQTT client implementation that uses the WolfSSL library. We show that tool detects a data race bug, which other approaches are unable to find. Third, to show the effectiveness of EBF, we replicate some known vulnerabilities in OpenSSL and CyaSSL (lately WolfSSL) libraries. EBF can detect the bugs in minimum time.
Malware remains a big threat to cyber security, calling for machine learning based malware detection. While promising, such detectors are known to be vulnerable to evasion attacks. Ensemble learning typically facilitates countermeasures, while attackers can leverage this technique to improve attack effectiveness as well. This motivates us to investigate which kind of robustness the ensemble defense or effectiveness the ensemble attack can achieve, particularly when they combat with each other. We thus propose a new attack approach, named mixture of attacks, by rendering attackers capable of multiple generative methods and multiple manipulation sets, to perturb a malware example without ruining its malicious functionality. This naturally leads to a new instantiation of adversarial training, which is further geared to enhancing the ensemble of deep neural networks. We evaluate defenses using Android malware detectors against 26 different attacks upon two practical datasets. Experimental results show that the new adversarial training significantly enhances the robustness of deep neural networks against a wide range of attacks, ensemble methods promote the robustness when base classifiers are robust enough, and yet ensemble attacks can evade the enhanced malware detectors effectively, even notably downgrading the VirusTotal service.
In todays integrated circuit (IC) ecosystem, owning a foundry is not economically viable, and therefore most IC design houses are now working under a fabless business model. In order to overcome security concerns associated with the outsorcing of IC fabrication, the Split Manufacturing technique was proposed. In Split Manufacturing, the Front End of Line (FEOL) layers (transistors and lower metal layers) are fabricated at an untrusted high-end foundry, while the Back End of Line (BEOL) layers (higher metal layers) are manufactured at a trusted low-end foundry. This approach hides the BEOL connections from the untrusted foundry, thus preventing overproduction and piracy threats. However, many works demonstrate that BEOL connections can be derived by exploiting layout characteristics that are introduced by heuristics employed in typical floorplanning, placement, and routing algorithms. Since straightforward Split Manufacturing may not afford a desirable security level, many authors propose defense techniques to be used along with Split Manufacturing. In our survey, we present a detailed overview of the technique, the many types of attacks towards Split Manufacturing, as well as possible defense techniques described in the literature. For the attacks, we present a concise discussion on the different threat models and assumptions, while for the defenses we classify the studies into three categories: proximity perturbation, wire lifting, and layout obfuscation. The main outcome of our survey is to highlight the discrepancy between many studies -- some claim netlists can be reconstructed with near perfect precision, while others claim marginal success in retrieving BEOL connections. Finally, we also discuss future trends and challenges inherent to Split Manufacturing, including the fundamental difficulty of evaluating the efficiency of the technique.
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