Entries Tagged "academic papers"

Page 39 of 86

Using Neural Networks to Identify Blurred Faces

Neural networks are good at identifying faces, even if they’re blurry:

In a paper released earlier this month, researchers at UT Austin and Cornell University demonstrate that faces and objects obscured by blurring, pixelation, and a recently-proposed privacy system called P3 can be successfully identified by a neural network trained on image datasets­—in some cases at a more consistent rate than humans.

“We argue that humans may no longer be the ‘gold standard’ for extracting information from visual data,” the researchers write. “Recent advances in machine learning based on artificial neural networks have led to dramatic improvements in the state of the art for automated image recognition. Trained machine learning models now outperform humans on tasks such as object recognition and determining the geographic location of an image.”

Research paper

Posted on September 27, 2016 at 9:39 AM • View Comments

Hacking Wireless Tire-Pressure Monitoring System

Research paper: “Security and Privacy Vulnerabilities of In-Car Wireless Networks: A Tire Pressure Monitoring System Case Study,” by Ishtiaq Rouf, Rob Miller, Hossen Mustafa, Travis Taylor, Sangho Oh, Wenyuan Xu, Marco Gruteser, Wade Trapper, Ivan Seskar:

Abstract: Wireless networks are being integrated into the modern automobile. The security and privacy implications of such in-car networks, however, have are not well understood as their transmissions propagate beyond the confines of a car’s body. To understand the risks associated with these wireless systems, this paper presents a privacy and security evaluation of wireless Tire Pressure Monitoring Systems using both laboratory experiments with isolated tire pressure sensor modules and experiments with a complete vehicle system. We show that eavesdropping is easily possible at a distance of roughly 40m from a passing vehicle. Further, reverse-engineering of the underlying protocols revealed static 32 bit identifiers and that messages can be easily triggered remotely, which raises privacy concerns as vehicles can be tracked through these identifiers. Further, current protocols do not employ authentication and vehicle implementations do not perform basic input validation, thereby allowing for remote spoofing of sensor messages. We validated this experimentally by triggering tire pressure warning messages in a moving vehicle from a customized software radio attack platform located in a nearby vehicle. Finally, the paper concludes with a set of recommendations for improving the privacy and security of tire pressure monitoring systems and other forthcoming in-car wireless sensor networks.

Posted on September 16, 2016 at 8:59 AM • View Comments

Recovering an iPhone 5c Passcode

Remember the San Bernardino killer’s iPhone, and how the FBI maintained that they couldn’t get the encryption key without Apple providing them with a universal backdoor? Many of us computer-security experts said that they were wrong, and there were several possible techniques they could use. One of them was manually removing the flash chip from the phone, extracting the memory, and then running a brute-force attack without worrying about the phone deleting the key.

The FBI said it was impossible. We all said they were wrong. Now, Sergei Skorobogatov has proved them wrong. Here’s his paper:

Abstract: This paper is a short summary of a real world mirroring attack on the Apple iPhone 5c passcode retry counter under iOS 9. This was achieved by desoldering the NAND Flash chip of a sample phone in order to physically access its connection to the SoC and partially reverse engineering its proprietary bus protocol. The process does not require any expensive and sophisticated equipment. All needed parts are low cost and were obtained from local electronics distributors. By using the described and successful hardware mirroring process it was possible to bypass the limit on passcode retry attempts. This is the first public demonstration of the working prototype and the real hardware mirroring process for iPhone 5c. Although the process can be improved, it is still a successful proof-of-concept project. Knowledge of the possibility of mirroring will definitely help in designing systems with better protection. Also some reliability issues related to the NAND memory allocation in iPhone 5c are revealed. Some future research directions are outlined in this paper and several possible countermeasures are suggested. We show that claims that iPhone 5c NAND mirroring was infeasible were ill-advised.

Susan Landau explains why this is important:

The moral of the story? It’s not, as the FBI has been requesting, a bill to make it easier to access encrypted communications, as in the proposed revised Burr-Feinstein bill. Such “solutions” would make us less secure, not more so. Instead we need to increase law enforcement’s capabilities to handle encrypted communications and devices. This will also take more funding as well as redirection of efforts. Increased security of our devices and simultaneous increased capabilities of law enforcement are the only sensible approach to a world where securing the bits, whether of health data, financial information, or private emails, has become of paramount importance.

Or: The FBI needs computer-security expertise, not backdoors.

Patrick Ball writes about the dangers of backdoors.

EDITED TO ADD (9/23): Good article from the Economist.

Posted on September 15, 2016 at 8:54 AM • View Comments

Using Wi-Fi Signals to Identify People by Body Shape

Another paper on using Wi-Fi for surveillance. This one is on identifying people by their body shape. “FreeSense:Indoor Human Identification with WiFi Signals“:

Abstract: Human identification plays an important role in human-computer interaction. There have been numerous methods proposed for human identification (e.g., face recognition, gait recognition, fingerprint identification, etc.). While these methods could be very useful under different conditions, they also suffer from certain shortcomings (e.g., user privacy, sensing coverage range). In this paper, we propose a novel approach for human identification, which leverages WIFI signals to enable non-intrusive human identification in domestic environments. It is based on the observation that each person has specific influence patterns to the surrounding WIFI signal while moving indoors, regarding their body shape characteristics and motion patterns. The influence can be captured by the Channel State Information (CSI) time series of WIFI. Specifically, a combination of Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT) and Dynamic Time Warping (DTW) techniques is used for CSI waveform-based human identification. We implemented the system in a 6m*5m smart home environment and recruited 9 users for data collection and evaluation. Experimental results indicate that the identification accuracy is about 88.9% to 94.5% when the candidate user set changes from 6 to 2, showing that the proposed human identification method is effective in domestic environments.

EDITED TO ADD (9/13): Related paper.

Posted on August 30, 2016 at 12:57 PM • View Comments

Keystroke Recognition from Wi-Fi Distortion

This is interesting research: “Keystroke Recognition Using WiFi Signals.” Basically, the user’s hand positions as they type distorts the Wi-Fi signal in predictable ways.

Abstract: Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals
can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of Channel State Information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal based keystroke recognition system called WiKey. WiKey consists of two Commercial Off-The-Shelf (COTS) WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves more than 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%.

News article.

Posted on August 30, 2016 at 6:04 AM • View Comments

Collision Attacks Against 64-Bit Block Ciphers

We’ve long known that 64 bits is too small for a block cipher these days. That’s why new block ciphers like AES have 128-bit, or larger, block sizes. The insecurity of the smaller block is nicely illustrated by a new attack called “Sweet32.” It exploits the ability to find block collisions in Internet protocols to decrypt some traffic, even though the attackers never learn the key.

Paper here. Matthew Green has a nice explanation of the attack. And some news articles. Hacker News thread.

Posted on August 26, 2016 at 2:19 PM • View Comments

Confusing Security Risks with Moral Judgments

Interesting research that shows we exaggerate the risks of something when we find it morally objectionable.

From an article about and interview with the researchers:

To get at this question experimentally, Thomas and her collaborators created a series of vignettes in which a parent left a child unattended for some period of time, and participants indicated the risk of harm to the child during that period. For example, in one vignette, a 10-month-old was left alone for 15 minutes, asleep in the car in a cool, underground parking garage. In another vignette, an 8-year-old was left for an hour at a Starbucks, one block away from her parent’s location.

To experimentally manipulate participants’ moral attitude toward the parent, the experimenters varied the reason the child was left unattended across a set of six experiments with over 1,300 online participants. In some cases, the child was left alone unintentionally (for example, in one case, a mother is hit by a car and knocked unconscious after buckling her child into her car seat, thereby leaving the child unattended in the car seat). In other cases, the child was left unattended so the parent could go to work, do some volunteering, relax or meet a lover.

Not surprisingly, the parent’s reason for leaving a child unattended affected participants’ judgments of whether the parent had done something immoral: Ratings were over 3 on a 10-point scale even when the child was left unattended unintentionally, but they skyrocketed to nearly 8 when the parent left to meet a lover. Ratings for the other cases fell in between.

The more surprising result was that perceptions of risk followed precisely the same pattern. Although the details of the cases were otherwise the same -­ that is, the age of the child, the duration and location of the unattended period, and so on -­ participants thought children were in significantly greater danger when the parent left to meet a lover than when the child was left alone unintentionally. The ratings for the other cases, once again, fell in between. In other words, participants’ factual judgments of how much danger the child was in while the parent was away varied according to the extent of their moral outrage concerning the parent’s reason for leaving.

Posted on August 25, 2016 at 11:12 AM • View Comments

Research on the Timing of Security Warnings

fMRI experiments show that we are more likely to ignore security warnings when they interrupt other tasks.

A new study from BYU, in collaboration with Google Chrome engineers, finds the status quo of warning messages appearing haphazardly­—while people are typing, watching a video, uploading files, etc.­—results in up to 90 percent of users disregarding them.

Researchers found these times are less effective because of “dual task interference,” a neural limitation where even simple tasks can’t be simultaneously performed without significant performance loss. Or, in human terms, multitasking.

“We found that the brain can’t handle multitasking very well,” said study coauthor and BYU information systems professor Anthony Vance. “Software developers categorically present these messages without any regard to what the user is doing. They interrupt us constantly and our research shows there’s a high penalty that comes by presenting these messages at random times.”

[…]

For part of the study, researchers had participants complete computer tasks while an fMRI scanner measured their brain activity. The experiment showed neural activity was substantially reduced when security messages interrupted a task, as compared to when a user responded to the security message itself.

The BYU researchers used the functional MRI data as they collaborated with a team of Google Chrome security engineers to identify better times to display security messages during the browsing experience.

Research paper. News article.

Posted on August 22, 2016 at 7:03 AM • View Comments

Prisoner's Dilemma Experiment Illustrates Four Basic Phenotypes

If you’ve read my book Liars and Outliers, you know I like the prisoner’s dilemma as a way to think about trust and security. There is an enormous amount of research—both theoretical and experimental—about the dilemma, which is why I found this new research so interesting. Here’s a decent summary:

The question is not just how people play these games­—there are hundreds of research papers on that­—but instead whether people fall into behavioral types that explain their behavior across different games. Using standard statistical methods, the researchers identified four such player types: optimists (20 percent), who always go for the highest payoff, hoping the other player will coordinate to achieve that goal; pessimists (30 percent), who act according to the opposite assumption; the envious (21 percent), who try to score more points than their partners; and the trustful (17 percent), who always cooperate. The remaining 12 percent appeared to make their choices completely at random.

Posted on August 18, 2016 at 5:36 AM • View Comments

Powerful Bit-Flipping Attack

New research: “Flip Feng Shui: Hammering a Needle in the Software Stack,” by Kaveh Razavi, Ben Gras, Erik Bosman Bart Preneel, Cristiano Giuffrida, and Herbert Bos.

Abstract: We introduce Flip Feng Shui (FFS), a new exploitation vector which allows an attacker to induce bit flips over arbitrary physical memory in a fully controlled way. FFS relies on hardware bugs to induce bit flips over memory and on the ability to surgically control the physical memory layout to corrupt attacker-targeted data anywhere in the software stack. We show FFS is possible today with very few constraints on the target data, by implementing an instance using the Rowhammer bug and memory deduplication (an OS feature widely deployed in production). Memory deduplication allows an attacker to reverse-map any physical page into a virtual page she owns as long as the page’s contents are known. Rowhammer, in turn, allows an attacker to flip bits in controlled (initially unknown) locations in the target page.

We show FFS is extremely powerful: a malicious VM in a practical cloud setting can gain unauthorized access to a co-hosted victim VM running OpenSSH. Using FFS, we exemplify end-to-end attacks breaking OpenSSH public-key authentication, and forging GPG signatures from trusted keys, thereby compromising the Ubuntu/Debian update mechanism. We conclude by discussing mitigations and future directions for FFS attacks.

Posted on August 16, 2016 at 7:09 AM • View Comments

1 … 37 38 39 40 41 … 86

Sidebar photo of Bruce Schneier by Joe MacInnis.