Intelligent User Interface for Interactive Multimedia: Emerging Techniques and ServicesView this Special Issue
Research Article | Open Access
Jae Dong Lee, Young-Sik Jeong, Jong Hyuk Park, "A Rhythm-Based Authentication Scheme for Smart Media Devices", The Scientific World Journal, vol. 2014, Article ID 781014, 9 pages, 2014. https://doi.org/10.1155/2014/781014
A Rhythm-Based Authentication Scheme for Smart Media Devices
In recent years, ubiquitous computing has been rapidly emerged in our lives and extensive studies have been conducted in a variety of areas related to smart devices, such as tablets, smartphones, smart TVs, smart refrigerators, and smart media devices, as a measure for realizing the ubiquitous computing. In particular, smartphones have significantly evolved from the traditional feature phones. Increasingly higher-end smartphone models that can perform a range of functions are now available. Smart devices have become widely popular since they provide high efficiency and great convenience for not only private daily activities but also business endeavors. Rapid advancements have been achieved in smart device technologies to improve the end users’ convenience. Consequently, many people increasingly rely on smart devices to store their valuable and important data. With this increasing dependence, an important aspect that must be addressed is security issues. Leaking of private information or sensitive business data due to loss or theft of smart devices could result in exorbitant damage. To mitigate these security threats, basic embedded locking features are provided in smart devices. However, these locking features are vulnerable. In this paper, an original security-locking scheme using a rhythm-based locking system (RLS) is proposed to overcome the existing security problems of smart devices. RLS is a user-authenticated system that addresses vulnerability issues in the existing locking features and provides secure confidentiality in addition to convenience.
Recently, extensive studies have been conducted on smart devices with touch screens in various fields. Some of the examples of smart devices with touch screens include tablets, smartphones, smart TVs, smart refrigerators, and smart media devices. In particular, a smartphone is a representative example of the capability of smart devices to provide a range of functionality despite device miniaturization. This has happened because smartphones continuously evolve as more smartphones with advanced performance capabilities are introduced in the market. Smart devices provide not only several basic functions such as a telephone, alarm clock, notes, schedule, and health management but also additional entertainment features such as books, movies, music, and shopping. They also provide various business functions such as mobile office, real-time SNS, and payment manager to improve business efficiency, and, in particular, big data processing based on smart devices with mobile cloud computing infrastructure. Although miniaturization and the lightweight feature of smart devices can provide users with the convenience of portability, smart device has potential risks of being lost or stolen [1–9]. Accordingly, a countermeasure to mitigate risks on smart devices loss or theft is required now more than ever. Smart devices also have critical data. Hence, they expose users to potential losses due to data leakage and malicious attacks. To protect confidential data, smart devices provide many forms of locking features such as drag, motion, pattern, password, personal identification number (PIN), and face, fingerprint, or a combination of face and voice recognition. However, they are less secure and highly vulnerable to shoulder surfing or smudge attacks [10–16].
In this paper, a novel locking scheme called rhythm locking system (RLS) is proposed to provide a convenient locking activity using rhythm while overcoming the vulnerability of basic locking functions. RLS is a user authentication system that provides secured confidentiality and convenience using unique rhythms set by the user. It also provides a simple interface, thereby enabling easy locking and fast unlocking.
The remainder of this paper is organized as follows. In Section 2, security authentication systems and basic embedded locking features are discussed. In Section 3, the locking scheme of RLS, proposed in this paper, is explained. Next, in Section 4, the design of RLS is detailed and its implementation is described in Section 5, followed by its performance evaluation in Section 6. Finally, the conclusion and future research activities are described in Section 7.
2. Related Works
In this section, we discuss the basic embedded locking features used in smart devices and various secure authentication systems. Security authentication systems and their descriptions are summarized in Table 1. Basic locking features embedded in smart devices are summarized in Table 2.
3. Locking Scheme of RLS
3.1. Key Generation
In this paper, we propose a RLS which uses touch rhythm as a secret pattern in a smart media device, which is dependent on auditory and behavior memory. The RLS receives a touch rhythm via a touch screen from a user and defines a track to record this rhythm. At the same time, unit time is measured for tracks. The measurement period ranges from first touching time to the configured time. Figure 1 shows a single key created for a touch button called A.
Figure 2 shows the generation of a union key using multiple tracks to increase the complexity of the secret pattern stored internally. A union key for tracks is generated through the touch recognition of four buttons, A, B, C, and D, using the matching table summarized in Table 3.
This method is distinctively different from the existing button pressing password setup. The available number of rhythm patterns for key generation increases exponentially depending on the time precision setup. Thus, even if malicious users know the positions of the buttons, it is very difficult to infer a user’s unique rhythm pattern.
3.2. Authentication Process
The RLS authentication consists of four steps conducted using four modules. Figure 3 shows the RLS authentication process. In Step 1, a single key is generated through user input value from the interface. In Step 2, a single union key is generated from the keys generated in Step 1. In Step 3, after the key generation process, improper noise is filtered for authentication. In Step 4, authentication is performed by comparing the stored rhythm pattern with a noise-filtered union key from Step 3.
4. Design of RLS
The RLS consists of six main components. The first component is largely in terms of functionality and the second is the user interface. The third component is the time resolution inspector (TRI) that measures time precision of the RLS. The fourth component is a key manager (K-manager) that manages the entered rhythm patterns. The fifth component is a lock service (L-service) that maintains and manages the locking service of the RLS, and the final component is a handler that delivers information for the visualization of activities. Figure 4 shows the overall architecture of the RLS.
The user interface component is divided into two modules, that is, rhythm and setting. Rhythm consists of four interfaces, Note A, Note B, Note C, and Note D, to set up rhythm patterns from the user. Settings are configured to select one of the three levels, high (H), medium (M), and low (L), depending on the acceptable error range and precision that are set for rhythm pattern input.
TRI measures the time interval for which the input is detected, according to the time precision set in the user interface. These measured values consist of a pair of note types and times followed by the inputs of Note A, Note B, Note C, and Note D, which are transferred to a K-manager.
K-manager consists of four modules, that are, key analysis (K-analysis), single key generation module (SKGM), union key generation module (UKGM), noise filter module (NFM), and user authentication module (UAM). K-analysis analyzes a pair of data received from the TRI and classifies them according to the note type. SKGM converts a classified note from K-analysis into a single key. UKGM transforms the converted single key from notes to a single complex union key. NFM performs filtering in three steps, NF-1, NF-2, and NF-3, with respect to the raw union key (R-U), converted in the UKGM, according to the acceptable error range set in the user interface. UAM performs either confirmation, when rhythm pattern authentication is set up, or comparison with the existing rhythm patterns to unlock the system when the RLS is executed on a smart device.
L-service consists of a screen check that provides a screen according to execution of the RLS operation, a lock analysis that analyzes a locking status, a lock that starts the RLS, and an unlock that stops the RLS.
Handler is responsible for delivering data synchronization and control messages between activity and user interface and between activity and L-service. Message analysis, in handler, analyzes received data and delivers visual information about the activity.
Activity consists of the following modules: register activity for running the RLS by receiving the rhythm pattern values from a user; dummy activity for visualization while the RLS is running; and set activity for input, confirmation of time precision of the RLS, and other setup activities of a user.
5. Implementation of the RLS
The initial screen of the RLS proposed in this paper is shown in Figure 5. Pressing ①, as shown in Figure 5, deletes a previously set rhythm pattern, while pressing ② moves to activity, by which a user can set the noise and precision of rhythm patterns. Pressing ③ moves to activity, by which the user’s unique rhythm pattern can be registered.
Figure 6 shows the setup of activity, which sets an acceptable error of noise and rhythm of the RLS. For noise sensitivity, a level of H, M, and L can be selected according to the filtering level of noise. For rhythm sensitivity, a level of H, M, and L can be selected according to the acceptable error range, which is recognized when a user enters a rhythm. Setup values selected in noise and rhythm sensitivity are applied to the input sensitivity for setting up rhythm patterns and unlocking the screen when the RLS is running. The default setup value is M for both noise and rhythm sensitivity.
Figure 7 shows a screen for input of rhythm patterns to execute the RLS. In Figure 7, ① is the register activity, which consists of Note A, Note B, Note C, and Note D. ② displays the success or failure of recognition in the system when a user enters a rhythm on Note A, Note B, Note C, or Note D. Here, a blue-colored timer progress bar is shown.
Figure 8 shows rhythm inputs of C, D, C, A, and B in order as entered by a user. In Figure 8, ① shows the status that initial input has not been detected, while ② shows that a timer progress bar is displayed as input C is recognized, and ③ shows the status that input D is recognized while a timer progress bar is running. As such, the timer progress bar runs independently while the initial input is recognized. While the timer progress bar is running, each single key for A, B, C, and D is internally generated. Once the timer progress bar terminates, single keys entered up to now are composed into a union key. Since the RLS adds not only the physical Interfaces A, B, C, and D entered by a user but also the logical time, it provides an enhanced security functionality.
6. Performance Evaluation
6.1. Evaluation of Security Strength
We conducted an experiment using a prototype of our proposed scheme. The prototype was developed on Android 4.3 Jelly Bean. In the experiment, we used a smart media device with a Qualcomm Snapdragon 800 2.3 GHz CPU and DDR3 3 GB RAM.
We performed experiments to determine the false acceptance rate (FAR) and false rejection rate (FRR) of the RLS to evaluate its security strength. The standard keys for FAR and FRR are shown in Table 4. For FAR, an arbitrary control key with the same length as the original key was created to perform the comparison. For FRR, an arbitrary control key was created by considering falsely rejected circumstances to perform the comparison. In Key 1, 85/1:10/0:5/2:10/0:5/3:10/0:5/2:10/0:5/3:10/0:5/4:10, in Table 4, “85” refers to a key length. Next, 1:10 indicates that Interface A, in Figure 7, was entered 10 s after input began, while 1 refers to the converted value obtained from the matching table. That is, the first number, 1, indicates the entered interface, while the following number, 10, after colon, refers to the time which elapsed while the input is received.
Figure 9 shows a graph of FAR and FRR with Key 1 in Table 4. As the value length tolerance in the lower left area becomes larger, the allowable error range becomes less when the length of each interface is examined. As the noise recognition range in the lower right area becomes larger, false recognition due to noise becomes more frequent. As numbers with respect to these two increase sequentially, an error rate is calculated by comparing the control key, which is created dynamically, and the standard key. As shown in Figure 9, a 0% error rate was obtained irrespective of the effect of the value length tolerance and the noise recognition range.
Figure 10 shows a graph of FAR and FRR with Key 2 in Table 4. It is configured in the same manner as in Figure 9, reaffirming that error rate decreases as the allowable range of the value length tolerance becomes smaller with respect to Key 2. It also shows that, as the noise recognition range becomes larger, error rate becomes smaller.
Figure 11 shows a graph of FAR and FRR with Key 3 in Table 4. As with Figure 10, as the value length tolerance and noise recognition range become larger, the error rate becomes smaller. Thus, if a user sets a rhythm pattern of the RLS to one more than a specific threshold value, strong security can be ensured.
6.2. Comparison with Existing Locking Schemes
In this section, existing locking schemes such as pattern lock, PIN, and password are compared with the RLS proposed in this paper, with respect to various attacking techniques.
Against a brute force attack, the number of patterns that can be set for locking determines security strength. PIN provides relatively weak security compared to other locking schemes, because it has a limited input length as well as the restriction that only a number can be used. Pattern lock has an advantage in terms of input of various patterns; it provides security stronger than PIN but weaker than password and the proposed RLS. Password and the RLS have similar security strength against brute force attacks.
The shoulder surfing attack begins when a user enters a pattern to unlock the screen. Pattern lock, which uses various patterns but is vulnerable to visual memory, and PIN, which uses fixed arrangement of numbers, both, therefore, provide weak security. Password is robust against the shoulder surfing attack owing to the large number of possible patterns. The RLS also has robust security, with a rhythm-based locking scheme using a logical time.
The dictionary attack is a method that employs all meaningful words or sentences in a dictionary. The pattern lock and the RLS provide robust security against dictionary attack because they use an entirely different method to set the locking pattern. However, PIN and password are moderately vulnerable because a user may employ meaningful numbers, symbols, or words.
The smudge attack uses a simple trace to discern a locking pattern. A trace is deployed to infer a locking pattern while the user’s input is entered to unlock the screen. Pattern lock and PIN show weak security because of easy collection of trace owing to the fixed arrangement on the screen. If a password is set with a long and complicated pattern, it can provide robust security; however, if it is set with a short and simple pattern, it provides weak security. The RLS provides robust security against smudge attacks because it combines physical and logical schemes.
Table 5 shows relative security of the existing locking systems and the RLS against various attack methods. The proposed RLS displays the strongest security against the brute force attack, shoulder surfing attack, dictionary attack, and smudge attack, which are some of the widely used attacks for touch-screen-based smart devices.
O: strong, △: medium, and X: weak.|
Smart media devices provide users with a variety of functions leading to their wide use. Most vendors have developed a variety of functions to provide better services to the end users. In particular, smart media devices have made significant advancement in terms of weight reduction, miniaturization, and various functions offered, but the most basic security issues have been ignored. As a result, although many basic locking features are embedded, smart media devices are vulnerable to a number of attacks.
In this paper, we proposed a rhythm-based locking system which considers rhythm as logical behavior. The proposed system provides not only strong security against malicious attackers but also convenience of memory to users. In addition, it is composed of a simple interface structure so that all ages can use it conveniently. Even if pressing positions are exposed, it is extremely difficult to predict precise timings, thereby ensuring high security strength.
In the future, a user authentication structure utilizing various sensors embedded in smart media devices will be studied. Stronger locking functions will be provided by considering the angle or the number of slopes. A unique type of user authentication system structured through unique recognition media will also be researched.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (2011-0024052).
- H.-W. Kim, J.-H. Kim, D. Ko, E.-H. Song, and Y.-S. Jeong, “8-Way lock for personal privacy of smart devices based on human-centric,” in Proceedings of the 40th Conference of the KIPS, vol. 20, pp. 735–737, KIPS, November 2013.
- K. Peng, “A secure network for mobile wireless service,” Journal of Information Processing Systems, vol. 9, no. 2, pp. 247–258, 2013.
- J. Ahn and R. Han , “An indoor augmented-reality evacuation system for the smartphone using personalized pedometry,” Human-Centric Computing and Information Sciences, vol. 2, article 18, 2012.
- C.-L. Tsai, C.-J. Chen, and D.-J. Zhuang, “Trusted M-banking Verification Scheme based on a combination of OTP and Biometrics,” Journal of Convergence, vol. 3, no. 3, pp. 23–29, 2012.
- G. Wang, W. Zhou, and L. T. Yang, “Trust, security and privacy for pervasive applications,” Journal of Supercomputing, vol. 64, no. 3, pp. 661–663, 2013.
- Y. Gong, Implications and Agreement of Smartphone, vol. 22, no. 4, Korea Information Society Development Institute, Seoul, Republic of Korea, 2010.
- ITU-T, “Security aspects of mobile phones,” T09 SG17 100407 TD PLEN 1012, April 2010.
- C. Mulliner, G. Vigna, D. Dagon, and W. Lee, “Using labeling to prevent cross-service attacks against smart phones,” in Detection of Intrusions and Malware & Vulnerability Assessment: Proceedings of the 3rd International Conference, DIMVA 2006, Berlin, Germany, July 13-14, 2006, vol. 4064 of Lecture Notes in Computer Science, pp. 91–108, Springer, Berlin, Germany, 2006.
- M. Park, The evolution of the mobile phones with touchscreen and the prospect of future: focused on the SRI-Tech [M.S. thesis], Incheon University, 2011.
- E. Chin, A. P. Felt, V. Sekar, and D. Wagner, “Measuring user confidence in smartphone security and privacy,” in Proceedings of the 8th Symposium on Usable Privacy and Security (SOUPS '12), 16, p. 1, Washington, DC, USA, July 2012.
- A. J. Aviv, K. Gibson, E. Mossop, M. Blaze, and J. M. Smith, “Smudge attacks on smartphone touch screens,” in Proceedings of the 4th USENIX Conference on Offensive Technologies, pp. 1–10, August 2010.
- B. Chojar, D. Lal, K. Gandhi, and K. Salariya, “Study of smartphone attacks and defenses,” International Journal of Engineering and Computer Science, vol. 2, no. 4, pp. 1018–1022, 2013.
- A. Mylonas, S. Dritsas, B. Tsoumas, and D. Gritzalis, “Smartphone security evaluation—the malware attack case,” in Proceedings of the 8th International Conference on Security and Cryptography (SECRYPT '11), pp. 25–36, Seville, Spain, July 2011.
- B. Kim and Y. Kim, “A study on emotional interface design based on each Smart-phone application category,” Korea Design Knowledge Society, vol. 20, pp. 181–192, 2011.
- D.-R. Kim and K.-H. Han, “A study on multi-media contents security using smart phone,” The Journal of Digital Policy and Management, vol. 11, no. 11, pp. 675–682, 2013.
- G. Kim and S. Cho, “Security vulnerability trends in smartphones,” in Proceedings of the Korea Computer Congress (KCC '11), vol. 37, no. 2, pp. 90–94, November 2011.
Copyright © 2014 Jae Dong Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.