Graphical passwords that allow a user to unlock a smartphone's screen are one of the Android operating system's features and many users prefer them instead of traditional textbased codes. A variety of attacks has been proposed against this mechanism, of which notable are methods that recover the lock patterns using the oily residues left on screens when people move their fingers to reproduce the unlock code. In this paper we present a pilot study on user habits when setting a pattern lock and on their perceptions regarding what constitutes a secure pattern. We use our survey's results to establish a scheme, which combines a behaviour-based attack and a physical attack on graphical lock screen methods, aiming to reduce the search space of possible combinations forming a pattern, to make it partially or fully retrievable.

Abstract-Flash-crowd attacks are the most vicious form of distributed denial of service (DDoS). They flood the victim with service requests generated from numerous bots. Attack requests are identical in content to those generated by legitimate, human users, and bots send at a low rate to appear non-aggressivethese features defeat many existing DDoS defenses.We propose defenses against flash-crowd attacks via human behavior modeling, which differentiate DDoS bots from human users. Current approaches to human-vs-bot differentiation, such as graphical puzzles, are insufficient and annoying to humans, whereas our defenses are highly transparent. We model three aspects of human behavior: a) request dynamics, by learning several chosen features of human interaction dynamics, and detecting bots that exhibit higher aggressiveness in one or more of these features, b) request semantics, by learning transitional probabilities of user requests, and detecting bots that generate valid but low-probability sequences, and c) ability to process visual cues, by embedding into server replies human-invisible objects, which cannot be detected by automated analysis, and flagging users that visit them as bots. We evaluate our defenses' performance on a series of web traffic logs, interlaced with synthetically generated attacks, and conclude that they raise the bar for a successful, sustained attack to botnets whose size is larger than the size observed in 1-5% of DDoS attacks today.

Increasing use of the Internet for critical services makes flooding distributed denial-of-service (DDoS) a top security threat. A distributed nature of DDoS suggests that a distributed mechanism is necessary for a successful defense. Three main DDoS defense functionalities -attack detection, rate limiting and traffic differentiation -are most effective when performed at the victim-end, core and sourceend respectively. Many existing systems are successful in one aspect of defense, but none offers a comprehensive solution and none has seen a wide deployment. We propose to harvest the strengths of existing defenses by organizing them into a collaborative overlay, called DefCOM, and augmenting them with communication and collaboration functionalities. Nodes collaborate during the attack to spread alerts and protect legitimate traffic, while rate limiting the attack. DefCOM can accommodate existing defenses, provide synergistic response to attacks and naturally lead to an Internet-wide response to DDoS threat.

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Wearable technologies are valuable tools that can encourage people to monitor their own well-being and facilitate timely health interventions. In this paper, we present SPW-2; a low-profile versatile wearable sensor that employs two ultra low power accelerometers and an optional gyroscope. Designed for minimum maintenance and a long-term operation outside the laboratory, SPW-2 is able to offer a battery lifetime of multiple months. Measurements on its wireless performance in a real residential environment with thick brick walls, demonstrate that SPW-2 can fully cover a room and -in most cases -the adjacent room, as well.