Traffic classification has been studied for two decades and applied to a wide range of applications from QoS provisioning billing in ISPs security-related firewalls intrusion detection systems. Port-based, data packet inspection, classical machine learning methods have used extensively the past, but their accuracy declined due dramatic changes Internet traffic, particularly increase encrypted traffic. With proliferation deep methods, researchers recently investigated these traffic reported...

Many network services and tools (e.g. monitors, malware-detection systems, routing billing policy enforcement modules in ISPs) depend on identifying the type of traffic that passes through network. With widespread use mobile devices, vast diversity apps, massive adoption encryption protocols (such as TLS), large-scale encrypted classification becomes increasingly difficult. In this paper, we propose a deep learning model for app identification works even with traffic. The proposed only needs...

Network traffic classification, which has numerous applications from security to billing and network provisioning, become a cornerstone of today's computer networks. Previous studies have developed classification techniques using classical machine learning algorithms deep methods when large quantities labeled data are available. However, capturing datasets is cumbersome time-consuming process. In this paper, we propose semi-supervised approach that obviates the need for datasets. We first...

Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes ISPs to firewall malware detection clients. Classical machine learning algorithms deep models have been widely used solve the traffic task. However, training such requires a large amount of labeled data. Labeling data is often most difficult time-consuming process building classifier. To this challenge, we reformulate into multi-task framework where bandwidth requirement...

Recent studies propose membership inference (MI) attacks on deep models, where the goal is to infer if a sample has been used in training process. Despite their apparent success, these only report accuracy, precision, and recall of positive class (member class). Hence, performance have not clearly reported negative (non-member In this paper, we show that way MI attack often misleading because they suffer from high false rate or alarm (FAR) reported. FAR shows how model mislabel non-training...

Due to insufficient training data and the high computational cost train a deep neural network from scratch, transfer learning has been extensively used in many deep-neural-network-based applications. A commonly approach involves taking part of pre-trained model, adding few layers at end, re-training new with small dataset. This approach, while efficient widely used, imposes security vulnerability because model is usually publicly available, including potential attackers. In this paper, we...

Western diet (WD) intake, aging, and inactivation of farnesoid X receptor (FXR) are risk factors for metabolic chronic inflammation-related health issues ranging from dysfunction-associated steatotic liver disease (MASLD) to dementia. The progression MASLD can be escalated when those risks combined. Inactivation FXR, the bile acid (BA), is cancer prone in both humans mice. current study used multi-omics including hepatic transcripts, liver, serum, urine metabolites, BAs, as well gut...

Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect tackling AMR rapid accurate detection emergence spread food animal production, which requires routine surveillance. However, can be expensive time-consuming considering growth rate bacteria most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict future burden bacterial...

The end-to-end throughput of multi-hop communication in wireless ad hoc networks is affected by the conflict between forwarding nodes. It has been shown that sending more packets than maximum achievable not only fails to increase but also decreases owing high contention and collision. Accordingly, it crucial importance for a source node know throughput. depends on multiple factors, such as physical layer limitations, medium access control (MAC) protocol properties, routing policy, nodes'...

Membership inference (MI) determines if a sample was part of victim model training set. Recent development MI attacks focus on record-level membership which limits their application in many real-world scenarios. For example, the person re-identification task, attacker (or investigator) is interested determining user's images have been used during or not. However, exact might not be accessible to attacker. In this paper, we develop user-level attack where goal find any from target user has...

Introduction Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder where hyperactivity often manifests as fidgeting, non-goal-directed motoric action. Many studies demonstrate fidgeting varies under different conditions self-regulating mechanism for attention and alertness during cognitively demanding tasks. Fidgeting has also been associated with reaction time variability. However, lack of standard variables to define quantify can lead discrepancies in data...

Stroke, as a leading cause of death around the globe, has become heavy burden on our society. Studies show that stroke can be predicted and prevented if person's blood pressure (BP) status is appropriately monitored via an ambulatory monitor (ABPM) system. However, currently there exists no efficient user-friendly ABPM system to provide early warning for risk in real-time. Moreover, most existing devices measure BP during deflation cuff, which fails reflect accurately.In this study, we...

Porcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread PRRSV classification field strains a key for successful prevention. Restriction fragment length polymorphism targeting Open reading frame 5 (ORF5) genes classify but showed unstable accuracy. Phylogenetic analysis powerful tool with consistent accuracy it demands large computational power as number sequences...

In many wireless communication protocols, small and random delay, called jitter, is imposed before packet transmission so as to reduce collisions. Jitter recommended for routing protocols such AODV LOADng. can be exploited change the order of routes about which destination informed and, a result, make better routes, regarding their metric, more likely choose by destination. However, best our knowledge, there only one mechanism, adaptive jitter proposed metric-based protocols. this paper, we...

Despite the plethora of studies about security vulnerabilities and defenses deep learning models, aspects methodologies, such as transfer learning, have been rarely studied. In this article, we highlight challenges research opportunities these focusing on attacks unique to them.

As multi-hop wireless networks are attracting more attention, the need to evaluate their performance becomes essential. In order metrics of networks, including sending and receiving rates a node as well collision probability, model based on Stochastic Reward Nets (SRNs) is proposed. The proposed SRN models typical in such considered general template be applied any node. single designed take transmission effects all neighboring nodes into account, while ignoring ones whose has no effect...

The popularity of deep learning methods in the time series domain boosts interest interpretability studies, including counterfactual (CF) methods. CF identify minimal changes instances to alter model predictions. Despite extensive research, no existing work benchmarks domain. Additionally, results reported literature are inconclusive due limited number datasets and inadequate metrics. In this work, we redesign quantitative metrics accurately capture desirable characteristics CFs. We...

Deep ensemble learning has been shown to improve accuracy by training multiple neural networks and averaging their outputs. Ensemble also suggested defend against membership inference attacks that undermine privacy. In this paper, we empirically demonstrate a trade-off between these two goals, namely privacy (in terms of attacks), in deep ensembles. Using wide range datasets model architectures, show the effectiveness increases when ensembling improves accuracy. We analyze impact various...

Human understandable explanation of deep learning models is necessary for many critical and sensitive applications. Unlike image or tabular data where the importance each input feature (for classifier's decision) can be directly projected into input, time series distinguishable features (e.g. dominant frequency) are often hard to manifest in domain a user easily understand. Moreover, most methods require baseline value as an indication absence any feature. However, notion lack feature, which...

Cognitive Radio (CR) technology enables Dynamic Spectrum Access (DSA) to ameliorate the eciency of under-utilized licensed bands and overcrowded unlicensed bands. However, providing an acceptable service for cognitive users requires more sophisticated approaches due existence Primary Users (PU) with high priority over Additionally, reducing interference PUs so that they can communicate without interruption is paramount importance. In order meet requirements as much possible reduce PUs, a...