Antimicrobial peptides (AMPs) have been widely recognized as a promising solution to combat antimicrobial resistance of microorganisms due to the increasing abuse of antibiotics in medicine and agriculture around the globe. In this study, we propose UniAMP, a systematic prediction framework for discovering AMPs. We observe that feature vectors used in various existing studies constructed from peptide information, such as sequence, composition, and structure, can be augmented and even replaced by information inferred by deep learning models. Specifically, we use a feature vector with 2924 values inferred by two deep learning models, UniRep and ProtT5, to demonstrate that such inferred information of peptides suffice for the task, with the help of our proposed deep neural network model composed of fully connected layers and transformer encoders for predicting the antibacterial activity of peptides. Evaluation results demonstrate superior performance of our proposed model on both balanced benchmark datasets and imbalanced test datasets compared with existing studies. Subsequently, we analyze the relations among peptide sequences, manually extracted features, and automatically inferred information by deep learning models, leading to observations that the inferred information is more comprehensive and non-redundant for the task of predicting AMPs. Moreover, this approach alleviates the impact of the scarcity of positive data and demonstrates great potential in future research and applications.

In this paper, we present a framework, SocialWatch, to detect attacker-created accounts and hijacked accounts for online services at a large scale. SocialWatch explores a set of social graph properties that effectively model the overall social activity and connectivity patterns of online users, including degree, PageRank, and social affinity features. These features are hard to mimic and robust to attacker counter strategies. We evaluate SocialWatch using a large, real dataset with more than 682 million users and over 5.75 billion directional relationships. SocialWatch successfully detects 56.85 million attacker-created accounts with a low false detection rate of 0.75% and a low false negative rate of 0.61%. In addition, SocialWatch detects 1.95 million hijacked accounts---among which 1.23 million were not detected previously---with a low false detection rate of 2%. Our work demonstrates the practicality and effectiveness of using large social graphs with billions of edges to detect real attacks.

With lower latency and higher bandwidth than its predecessor 3G networks, the latest cellular technology 4G LTE has been attracting many new users. However, the interactions among applications, network transport protocol, and the radio layer still remain unexplored. In this work, we conduct an in-depth study of these interactions and their impact on performance, using a combination of active and passive measurements. We observed that LTE has significantly shorter state promotion delays and lower RTTs than those of 3G networks. We discovered various inefficiencies in TCP over LTE such as undesired slow start. We further developed a novel and lightweight passive bandwidth estimation technique for LTE networks. Using this tool, we discovered that many TCP connections significantly under-utilize the available bandwidth. On average, the actually used bandwidth is less than 50% of the available bandwidth. This causes data downloads to be longer, and incur additional energy overhead. We found that the under-utilization can be caused by both application behavior and TCP parameter setting. We found that 52.6% of all downlink TCP flows have been throttled by limited TCP receive window, and that data transfer patterns for some popular applications are both energy and network unfriendly. All these findings highlight the need to develop transport protocol mechanisms and applications that are more LTE-friendly.

This paper presents the design and implementation of Souche, a system that recognizes legitimate users early in online services. This early recognition contributes to both usability and security. Souche leverages social connections established over time. Legitimate users help identify other legitimate users through an implicit vouching process, strategically controlled within vouching trees. Souche is lightweight and fully transparent to users. In our evaluation on a real dataset of several hundred million users, Souche can efficiently identify 85% of legitimate users early, while reducing the percentage of falsely admitted malicious users from 44% to 2.4%. Our evaluation further indicates that Souche is robust in the presence of compromised accounts. It is generally applicable to enhance usability and security for a wide class of online services.

Today's cellular systems operate under diverse resource constraints: limited frequency spectrum, network processing capability, and handset battery life. We consider a novel and important factor, handset screen status, i.e., whether the screen is on or off, which was ignored by previous approaches for optimizing cellular resource utilization. Based on analyzing real smartphone traffic collected from 20 users over five months, we find that off-screen traffic accounts for 58.5% of the total radio energy consumption although their traffic volume contribution is much smaller. Such unexpected results are attributed to the unique cellular resource management policy that is not well understood by developers, leading to cellular-unfriendly mobile apps. We then make a further step by proposing screen-aware optimization, by leveraging the key observation that screen-off traffic is much more delay-tolerant than its screen-on counterpart due to a lack of user interaction. Our proposal can better balance the key tradeoffs in cellular networks. It saves up to 60.92% of the network energy and reduces signaling and delay overhead by 25.33% and 30.59%, respectively.

With the recent advent of 4G LTE networks, there has been increasing interest to better understand the performance and power characteristics, compared with 3G/WiFi networks. In this paper, we take one of the first steps in this direction.

Web caching in mobile networks is critical due to the unprecedented cellular traffic growth that far exceeds the deployment of cellular infrastructures. Caching on handsets is particularly important as it eliminates all network-related overheads. We perform the first network-wide study of the redundant transfers caused by inefficient web caching on handsets, using a dataset collected from 3 million smartphone users of a large commercial cellular carrier, as well as another five-month-long trace contributed by 20 smartphone users. Our findings suggest that redundant transfers contribute 18% and 20% of the total HTTP traffic volume in the two datasets. Also they are responsible for 17% of the bytes, 7% of the radio energy consumption, 6% of the signaling load, and 9% of the radio resource utilization of all cellular data traffic in the second dataset. Most of such redundant transfers are caused by the smartphone web caching implementation that does not fully support or strictly follow the protocol specification, or by developers not fully utilizing the caching support provided by the libraries. This is further confirmed by our caching tests of 10 popular HTTP libraries and mobile browsers. Improving the cache implementation will bring considerable reduction of network traffic volume, cellular resource consumption, handset energy consumption, and user-perceived latency, benefiting both cellular carriers and customers.

Cellular networks employ a specific radio resource management policy distinguishing them from wired and Wi-Fi networks. A lack of awareness of this important mechanism potentially leads to resource-inefficient mobile applications. We perform the first network-wide, large-scale investigation of a particular type of application traffic pattern called periodic transfers where a handset periodically exchanges some data with a remote server every t seconds. Using packet traces containing 1.5 billion packets collected from a commercial cellular carrier, we found that periodic transfers are very prevalent in today's smartphone traffic. However, they are extremely resource-inefficient for both the network and end-user devices even though they predominantly generate very little traffic. This somewhat counter-intuitive behavior is a direct consequence of the adverse interaction between such periodic transfer patterns and the cellular network radio resource management policy. For example, for popular smartphone applications such as Facebook, periodic transfers account for only 1.7% of the overall traffic volume but contribute to 30% of the total handset radio energy consumption. We found periodic transfers are generated for various reasons such as keep-alive, polling, and user behavior measurements. We further investigate the potential of various traffic shaping and resource control algorithms. Depending on their traffic patterns, applications exhibit disparate responses to optimization strategies. Jointly using several strategies with moderate aggressiveness can eliminate almost all energy impact of periodic transfers for popular applications such as Facebook and Pandora.

Despite the tremendous growth in the cellular data network usage due to the popularity of smartphones, so far there is rather limited understanding of the network infrastructure of various cellular carriers. Understanding the infrastructure characteristics such as the network topology, routing design, address allocation, and DNS service configuration is essential for predicting, diagnosing, and improving cellular network services, as well as for delivering content to the growing population of mobile wireless users. In this work, we propose a novel approach for discovering cellular infrastructure by intelligently combining several data sources, i.e., server logs from a popular location search application, active measurements results collected from smartphone users, DNS request logs from a DNS authoritative server, and publicly available routing updates. We perform the first comprehensive analysis to characterize the cellular data network infrastructure of four major cellular carriers within the U.S. in our study.

The use of cellular data networks is increasingly popular due to the widespread deployment of 3G technologies and the rapid adoption of smartphones, such as iPhone and GPhone. Besides email and web browsing, a variety of network applications are now available, rendering smartphones potentially useful substitutes for their desktop counterparts. Nevertheless, the performance of smartphone applications in the wild is still poorly understood due to a lack of systematic measurement methodology.