Portfolio-Based Incentive Mechanism Design for Cross-Device Federated Learning

In recent years, there has been a significant increase in attention towards designing incentive mechanisms for federated learning (FL). Tremendous existing studies attempt to design the solutions using various approaches (e.g., game theory, reinforcement learning) under different settings. Yet the design of incentive mechanism could be significantly biased in that clients' performance in many applications is stochastic and hard to estimate. Properly handling this stochasticity motivates this research, as it is not well addressed in pioneering literature. In this paper, we focus on cross-device FL and propose a multi-level FL architecture under the real scenarios. Considering the two properties of clients' situations: uncertainty, correlation, we propose FL Incentive Mechanism based on Portfolio theory (FL-IMP). As far as we are aware, this is the pioneering application of portfolio theory to incentive mechanism design aimed at resolving FL resource allocation problem. In order to more accurately reflect practical FL scenarios, we introduce the Federated Learning Agent-Based Model (FL-ABM) as a means of simulating autonomous clients. FL-ABM enables us to gain a deeper understanding of the factors that influence the system's outcomes. Experimental evaluations of our approach have extensively validated its effectiveness and superior performance in comparison to the benchmark methods

An Effective Conversation-Based Botnet Detection Method

A botnet is one of the most grievous threats to network security since it can evolve into many attacks, such as Denial-of-Service (DoS), spam, and phishing. However, current detection methods are inefficient to identify unknown botnet. The high-speed network environment makes botnet detection more difficult. To solve these problems, we improve the progress of packet processing technologies such as New Application Programming Interface (NAPI) and zero copy and propose an efficient quasi-real-time intrusion detection system. Our work detects botnet using supervised machine learning approach under the high-speed network environment. Our contributions are summarized as follows: (1) Build a detection framework using PF_RING for sniffing and processing network traces to extract flow features dynamically. (2) Use random forest model to extract promising conversation features. (3) Analyze the performance of different classification algorithms. The proposed method is demonstrated by well-known CTU13 dataset and nonmalicious applications. The experimental results show our conversation-based detection approach can identify botnet with higher accuracy and lower false positive rate than flow-based approach

Liraglutide Attenuates the Depressive- and Anxiety-like Behaviour in the Corticosterone Induced Depression Model Via Improving Hippocampal Neural Plasticity

Recent studies indicate that metabolic disorders such as diabetes and obesity are a major risk factor of psychiatric diseases. This relationship opens the opportunity to develop new antidepressant drugs by repurposing antidiabetic drugs. Previous research has demonstrated that GLP-1 analogs are neuroprotective in several neurological disease models including Alzheimer’s disease (AD), Parkinson’s disease (PD), and stroke. In addition, the GLP-1 analog liraglutide has been shown to promote neurogenesis, which is seen to play important roles in memory formation and cognitive and emotional processing. However, whether liraglutide is an effective antidepressant remains unknown. Therefore, we tested this hypothesis in the depression model of chronic administration of corticosterone (CORT) in mice and treated the animals daily with liraglutide (5 or 20nmol/kg ip.) to assess its therapeutic potential as an antidepressant. Behavioral studies showed that liraglutide administration attenuated depressive- and anxiety- like behaviors in this depression mouse model, and attenuated the hyperactivity induced by the stress hormone. Additionally, liraglutide treatment protected synaptic plasticity and reversed the suppression of hippocampal long-term potentiation induced by CORT administration, demonstrating synaptic protective effects of liraglutide. We also found that liraglutide treatment increased the cell density of immature neurons in the subgranular dentate gyrus region of the hippocampus. In addition, liraglutide prevented the CORT induced impairments and simultaneously increased the level of phosphorylated GSK3β in the hippocampus, which may be instrumental in the anti-depressant activity of liraglutide treatment. Taken together, liraglutide has the potential to act as a therapeutic treatment of depression

Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E-Nose Systems

Machine learning approaches have been widely used to tackle the problem of sensor array drift in E-Nose systems. However, labeled data are rare in practice, which makes supervised learning methods hard to be applied. Meanwhile, current solutions require updating the analytical model in an offline manner, which hampers their uses for online scenarios. In this paper, we extended Target Domain Adaptation Extreme Learning Machine (DAELM_T) to achieve high accuracy with less labeled samples by proposing a Weighted Domain Transfer Extreme Learning Machine, which uses clustering information as prior knowledge to help select proper labeled samples and calculate sensitive matrix for weighted learning. Furthermore, we converted DAELM_T and the proposed method into their online learning versions under which scenario the labeled data are selected beforehand. Experimental results show that, for batch learning version, the proposed method uses around 20% less labeled samples while achieving approximately equivalent or better accuracy. As for the online versions, the methods maintain almost the same accuracies as their offline counterparts do, but the time cost remains around a constant value while that of offline versions grows with the number of samples

Identifying APT Malware Domain Based on Mobile DNS Logging

Advanced Persistent Threat (APT) is a serious threat against sensitive information. Current detection approaches are time-consuming since they detect APT attack by in-depth analysis of massive amounts of data after data breaches. Specifically, APT attackers make use of DNS to locate their command and control (C&C) servers and victims’ machines. In this paper, we propose an efficient approach to detect APT malware C&C domain with high accuracy by analyzing DNS logs. We first extract 15 features from DNS logs of mobile devices. According to Alexa ranking and the VirusTotal’s judgement result, we give each domain a score. Then, we select the most normal domains by the score metric. Finally, we utilize our anomaly detection algorithm, called Global Abnormal Forest (GAF), to identify malware C&C domains. We conduct a performance analysis to demonstrate that our approach is more efficient than other existing works in terms of calculation efficiency and recognition accuracy. Compared with Local Outlier Factor (LOF), k-Nearest Neighbor (KNN), and Isolation Forest (iForest), our approach obtains more than 99% F-M and R for the detection of C&C domains. Our approach not only can reduce data volume that needs to be recorded and analyzed but also can be applicable to unsupervised learning

Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy

A deep learning based static taint analysis approach for IoT software vulnerability location

Computer system vulnerabilities, computer viruses, and cyber attacks are rooted in software vulnerabilities. Reducing software defects, improving software reliability and security are urgent problems in the development of software. The core content is the discovery and location of software vulnerability. However, traditional human experts-based approaches are labor-consuming and time-consuming. Thus, some automatic detection approaches are proposed to solve the problem. But, they have a high false negative rate. In this paper, a deep learning based static taint analysis approach is proposed to automatically locate Internet of Things (IoT) software vulnerability, which can relieve tedious manual analysis and improve detection accuracy. Deep learning is used to detect vulnerability since it considers the program context. Firstly, the taint from the difference file between the source program and its patched program selection rules are designed. Secondly, the taint propagation paths are got using static taint analysis. Finally, the detection model based on two-stage Bidirectional Long Short Term Memory (BLSTM) is applied to discover and locate software vulnerabilities. The Code Gadget Database is used to evaluate the proposed approach, which includes two types of vulnerabilities in C/C++ programs, buffer error vulnerability (CWE-119) and resource management error vulnerability (CWE-399). Experimental results show that our proposed approach can achieve an accuracy of 0.9732 for CWE-119 and 0.9721 for CWE-399, which is higher than that of the other three models (the accuracy of RNN, LSTM, and BLSTM is under than 0.97) and achieve a lower false negative rate and false positive rate than the other approaches.This work was supported in part by the National Key R&D Plan under Grant CNS 2016QY06X1205 ,in part by the Basic Research Business Fees of Central Colleges under Grant CNS 20826041B4252 , in part by the National Natural Science Foundation (NSFC) under Grant CNS 61572115 , and in part by the Science and Technology Project of State Grid Corporation of China under Grant CNS 522722180007 . Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the funding agencies