Visible to the public Biblio

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2020-01-02
Yu, Jianguo, Tian, Pei, Feng, Haonan, Xiao, Yan.  2018.  Research and Design of Subway BAS Intrusion Detection Expert System. 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). :152–156.
The information security of urban rail transit system faces great challenges. As a subsystem of the subway, BAS is short for Building Automation System, which is used to monitor and manage subway equipment and environment, also facing the same problem. Based on the characteristics of BAS, this paper designed a targeted intrusion detection expert system. This paper focuses on the design of knowledge base and the inference engine of intrusion detection system based on expert system. This study laid the foundation for the research on information security of the entire rail transit system.
2019-12-16
Chen, Ping, Yu, Han, Zhao, Min, Wang, Jinshuang.  2018.  Research and Implementation of Cross-site Scripting Defense Method Based on Moving Target Defense Technology. 2018 5th International Conference on Systems and Informatics (ICSAI). :818–822.

The root cause of cross-site scripting(XSS) attack is that the JavaScript engine can't distinguish between the JavaScript code in Web application and the JavaScript code injected by attackers. Moving Target Defense (MTD) is a novel technique that aim to defeat attacks by frequently changing the system configuration so that attackers can't catch the status of the system. This paper describes the design and implement of a XSS defense method based on Moving Target Defense technology. This method adds a random attribute to each unsafe element in Web application to distinguish between the JavaScript code in Web application and the JavaScript code injected by attackers and uses a security check function to verify the random attribute, if there is no random attribute or the random attribute value is not correct in a HTML (Hypertext Markup Language) element, the execution of JavaScript code will be prevented. The experiment results show that the method can effectively prevent XSS attacks and have little impact on the system performance.

2019-12-05
Campioni, Lorenzo, Hauge, Mariann, Landmark, Lars, Suri, Niranjan, Tortonesi, Mauro.  2019.  Considerations on the Adoption of Named Data Networking (NDN) in Tactical Environments. 2019 International Conference on Military Communications and Information Systems (ICMCIS). :1-8.

Mobile military networks are uniquely challenging to build and maintain, because of their wireless nature and the unfriendliness of the environment, resulting in unreliable and capacity limited performance. Currently, most tactical networks implement TCP/IP, which was designed for fairly stable, infrastructure-based environments, and requires sophisticated and often application-specific extensions to address the challenges of the communication scenario. Information Centric Networking (ICN) is a clean slate networking approach that does not depend on stable connections to retrieve information and naturally provides support for node mobility and delay/disruption tolerant communications - as a result it is particularly interesting for tactical applications. However, despite ICN seems to offer some structural benefits for tactical environments over TCP/IP, a number of challenges including naming, security, performance tuning, etc., still need to be addressed for practical adoption. This document, prepared within NATO IST-161 RTG, evaluates the effectiveness of Named Data Networking (NDN), the de facto standard implementation of ICN, in the context of tactical edge networks and its potential for adoption.

2019-12-02
Li, Congwu, Lin, Jingqiang, Cai, Quanwei, Luo, Bo.  2018.  Peapods: OS-Independent Memory Confidentiality for Cryptographic Engines. 2018 IEEE Intl Conf on Parallel Distributed Processing with Applications, Ubiquitous Computing Communications, Big Data Cloud Computing, Social Computing Networking, Sustainable Computing Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom). :862–869.
Cryptography is widely adopted in computer systems to protect the confidentiality of sensitive information. The security relies on the assumption that cryptography keys are never leaked, which may be broken by the memory disclosure attacks, e.g., the Heartbleed and coldboot attacks. Various schemes are proposed to defend against memory disclosure attacks, e.g., performing the cryptographic computations in registers, or adopting the hardware features (e.g., Intel TSX and Intel SGX) to ensure that the plaintext of the cryptography key never appears in memory. However, these schemes are still not widely deployed due to the following limitations: (a) Most of the schemes are deployed in the OS kernel and require the root (or administrator) privileges of the host; and (b) They require the programmers to integrate these protection schemes in the implementation of different cryptography algorithms on different platforms. In this paper, we propose a tool implemented in Clang/LLVM, named Peapods, which provides the user-mode protection for cryptographic keys in software engines. It introduces one qualifier and three intrinsics for the programmers to specify the sensitive variables and code fragments to be protected, making it easier to be deployed. Peapods adopts transactional memory to protect cryptographic keys, while it is OS-independent and does not require the cryptographic computation performed in the OS kernel. Peapods supports the automatic protection between transactions for better performance. We have implemented the prototype of Peapods. Evaluation results demonstrate that Peapods achieves the design goals with a modest overhead (less than 10%).
2019-11-11
Martiny, Karsten, Elenius, Daniel, Denker, Grit.  2018.  Protecting Privacy with a Declarative Policy Framework. 2018 IEEE 12th International Conference on Semantic Computing (ICSC). :227–234.
This article describes a privacy policy framework that can represent and reason about complex privacy policies. By using a Common Data Model together with a formal shareability theory, this framework enables the specification of expressive policies in a concise way without burdening the user with technical details of the underlying formalism. We also build a privacy policy decision engine that implements the framework and that has been deployed as the policy decision point in a novel enterprise privacy prototype system. Our policy decision engine supports two main uses: (1) interfacing with user interfaces for the creation, validation, and management of privacy policies; and (2) interfacing with systems that manage data requests and replies by coordinating privacy policy engine decisions and access to (encrypted) databases using various privacy enhancing technologies.
2019-10-22
Deb Nath, Atul Prasad, Bhunia, Swarup, Ray, Sandip.  2018.  ArtiFact: Architecture and CAD Flow for Efficient Formal Verification of SoC Security Policies. 2018 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). :411–416.
Verification of security policies represents one of the most critical, complex, and expensive steps of modern SoC design validation. SoC security policies are typically implemented as part of functional design flow, with a diverse set of protection mechanisms sprinkled across various IP blocks. An obvious upshot is that their verification requires comprehension and analysis of the entire system, representing a scalability bottleneck for verification tools. The scale and complexity of industrial SoC is far beyond the analysis capacity of state-of-the-art formal tools; even simulation-based security verification is severely limited in effectiveness because of the need to exercise subtle corner-cases across the entire system. We address this challenge by developing a novel security architecture that accounts for verification needs from the ground up. Our framework, ArtiFact, provides an alternative architecture for security policy implementation that exploits a flexible, centralized, infrastructure IP and enables scalable, streamlined verification of these policies. With our architecture, verification of system-level security policies reduces to analysis of this single IP and its interfaces, enabling off-the-shelf formal tools to successfully verify these policies. We introduce a CAD flow that supports both formal and dynamic (simulation-based) verification, and is built on top of such off-the-shelf tools. Our approach reduces verification time by over 62X and bug detection time by 34X for illustrative policies.
2019-06-24
Stokes, J. W., Wang, D., Marinescu, M., Marino, M., Bussone, B..  2018.  Attack and Defense of Dynamic Analysis-Based, Adversarial Neural Malware Detection Models. MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM). :1–8.

Recently researchers have proposed using deep learning-based systems for malware detection. Unfortunately, all deep learning classification systems are vulnerable to adversarial learning-based attacks, or adversarial attacks, where miscreants can avoid detection by the classification algorithm with very few perturbations of the input data. Previous work has studied adversarial attacks against static analysis-based malware classifiers which only classify the content of the unknown file without execution. However, since the majority of malware is either packed or encrypted, malware classification based on static analysis often fails to detect these types of files. To overcome this limitation, anti-malware companies typically perform dynamic analysis by emulating each file in the anti-malware engine or performing in-depth scanning in a virtual machine. These strategies allow the analysis of the malware after unpacking or decryption. In this work, we study different strategies of crafting adversarial samples for dynamic analysis. These strategies operate on sparse, binary inputs in contrast to continuous inputs such as pixels in images. We then study the effects of two, previously proposed defensive mechanisms against crafted adversarial samples including the distillation and ensemble defenses. We also propose and evaluate the weight decay defense. Experiments show that with these three defenses, the number of successfully crafted adversarial samples is reduced compared to an unprotected baseline system. In particular, the ensemble defense is the most resilient to adversarial attacks. Importantly, none of the defenses significantly reduce the classification accuracy for detecting malware. Finally, we show that while adding additional hidden layers to neural models does not significantly improve the malware classification accuracy, it does significantly increase the classifier's robustness to adversarial attacks.

2019-03-22
Kumar, A., Abdelhadi, A., Clancy, C..  2018.  Novel Anomaly Detection and Classification Schemes for Machine-to-Machine Uplink. 2018 IEEE International Conference on Big Data (Big Data). :1284-1289.

Machine-to-Machine (M2M) networks being connected to the internet at large, inherit all the cyber-vulnerabilities of the standard Information Technology (IT) systems. Since perfect cyber-security and robustness is an idealistic construct, it is worthwhile to design intrusion detection schemes to quickly detect and mitigate the harmful consequences of cyber-attacks. Volumetric anomaly detection have been popularized due to their low-complexity, but they cannot detect low-volume sophisticated attacks and also suffer from high false-alarm rate. To overcome these limitations, feature-based detection schemes have been studied for IT networks. However these schemes cannot be easily adapted to M2M systems due to the fundamental architectural and functional differences between the M2M and IT systems. In this paper, we propose novel feature-based detection schemes for a general M2M uplink to detect Distributed Denial-of-Service (DDoS) attacks, emergency scenarios and terminal device failures. The detection for DDoS attack and emergency scenarios involves building up a database of legitimate M2M connections during a training phase and then flagging the new M2M connections as anomalies during the evaluation phase. To distinguish between DDoS attack and emergency scenarios that yield similar signatures for anomaly detection schemes, we propose a modified Canberra distance metric. It basically measures the similarity or differences in the characteristics of inter-arrival time epochs for any two anomalous streams. We detect device failures by inspecting for the decrease in active M2M connections over a reasonably large time interval. Lastly using Monte-Carlo simulations, we show that the proposed anomaly detection schemes have high detection performance and low-false alarm rate.

2019-02-14
Sun, A., Gao, G., Ji, T., Tu, X..  2018.  One Quantifiable Security Evaluation Model for Cloud Computing Platform. 2018 Sixth International Conference on Advanced Cloud and Big Data (CBD). :197-201.

Whatever one public cloud, private cloud or a mixed cloud, the users lack of effective security quantifiable evaluation methods to grasp the security situation of its own information infrastructure on the whole. This paper provides a quantifiable security evaluation system for different clouds that can be accessed by consistent API. The evaluation system includes security scanning engine, security recovery engine, security quantifiable evaluation model, visual display module and etc. The security evaluation model composes of a set of evaluation elements corresponding different fields, such as computing, storage, network, maintenance, application security and etc. Each element is assigned a three tuple on vulnerabilities, score and repair method. The system adopts ``One vote vetoed'' mechanism for one field to count its score and adds up the summary as the total score, and to create one security view. We implement the quantifiable evaluation for different cloud users based on our G-Cloud platform. It shows the dynamic security scanning score for one or multiple clouds with visual graphs and guided users to modify configuration, improve operation and repair vulnerabilities, so as to improve the security of their cloud resources.

2019-01-21
Kafash, S. H., Giraldo, J., Murguia, C., Cárdenas, A. A., Ruths, J..  2018.  Constraining Attacker Capabilities Through Actuator Saturation. 2018 Annual American Control Conference (ACC). :986–991.
For LTI control systems, we provide mathematical tools - in terms of Linear Matrix Inequalities - for computing outer ellipsoidal bounds on the reachable sets that attacks can induce in the system when they are subject to the physical limits of the actuators. Next, for a given set of dangerous states, states that (if reached) compromise the integrity or safe operation of the system, we provide tools for designing new artificial limits on the actuators (smaller than their physical bounds) such that the new ellipsoidal bounds (and thus the new reachable sets) are as large as possible (in terms of volume) while guaranteeing that the dangerous states are not reachable. This guarantees that the new bounds cut as little as possible from the original reachable set to minimize the loss of system performance. Computer simulations using a platoon of vehicles are presented to illustrate the performance of our tools.
2019-01-16
Zhang, R., Yang, G., Wang, Y..  2018.  Propagation Characteristics of Acoustic Emission Signals in Multi Coupling Interface of the Engine. 2018 IEEE 3rd International Conference on Integrated Circuits and Microsystems (ICICM). :254–258.
The engine is a significant and dynamic component of the aircraft. Because of the complicated structure and severe operating environment, the fault detection of the engine has always been the key and difficult issue in the field of reliability. Based on an engine and the acoustic emission technology, we propose a method of identifying fault types and determining different components in the engine by constructing the attenuation coefficient. There are several common faults of engines, and three different types of fault sources are generated experimentally in this work. Then the fault signal of the above fault sources propagating in different engine components are obtained. Finally, the acoustic emission characteristics of the fault signal are extracted and judged by the attenuation coefficient. The work effectively identifies different types of faults and studies the effects of different structural components on the propagation of fault acoustic emission signals, which provides a method for the use of acoustic emission technology to identify the faults types of the engine and to study the propagation characteristics of AE signals on the engine.*
Shaukat, S. K., Ribeiro, V. J..  2018.  RansomWall: A layered defense system against cryptographic ransomware attacks using machine learning. 2018 10th International Conference on Communication Systems Networks (COMSNETS). :356–363.

Recent worldwide cybersecurity attacks caused by Cryptographic Ransomware infected systems across countries and organizations with millions of dollars lost in paying extortion amounts. This form of malicious software takes user files hostage by encrypting them and demands a large ransom payment for providing the decryption key. Signature-based methods employed by Antivirus Software are insufficient to evade Ransomware attacks due to code obfuscation techniques and creation of new polymorphic variants everyday. Generic Malware Attack vectors are also not robust enough for detection as they do not completely track the specific behavioral patterns shown by Cryptographic Ransomware families. This work based on analysis of an extensive dataset of Ran-somware families presents RansomWall, a layered defense system for protection against Cryptographic Ransomware. It follows a Hybrid approach of combined Static and Dynamic analysis to generate a novel compact set of features that characterizes the Ransomware behavior. Presence of a Strong Trap Layer helps in early detection. It uses Machine Learning for unearthing zero-day intrusions. When initial layers of RansomWall tag a process for suspicious Ransomware behavior, files modified by the process are backed up for preserving user data until it is classified as Ransomware or Benign. We implemented RansomWall for Microsoft Windows operating system (the most attacked OS by Cryptographic Ransomware) and evaluated it against 574 samples from 12 Cryptographic Ransomware families in real-world user environments. The testing of RansomWall with various Machine Learning algorithms evaluated to 98.25% detection rate and near-zero false positives with Gradient Tree Boosting Algorithm. It also successfully detected 30 zero-day intrusion samples (having less than 10% detection rate with 60 Security Engines linked to VirusTotal).

2018-11-14
Fayyad, S., Noll, J..  2017.  A Framework for Measurability of Security. 2017 8th International Conference on Information and Communication Systems (ICICS). :302–309.

Having an effective security level for Embedded System (ES), helps a reliable and stable operation of this system. In order to identify, if the current security level for a given ES is effective or not, we need a proactive evaluation for this security level. The evaluation of the security level for ESs is not straightforward process, things like the heterogeneity among the components of ES complicate this process. One of the productive approaches, which overcame the complexity of evaluation for Security, Privacy and Dependability (SPD) is the Multi Metrics (MM). As most of SPD evaluation approaches, the MM approach bases on the experts knowledge for the basic evaluation. Regardless of its advantages, experts evaluation has some drawbacks, which foster the need for less experts-dependent evaluation. In this paper, we propose a framework for security measurability as a part of security, privacy and dependability evaluation. The security evaluation based on Multi Metric (MM) approach as being an effective approach for evaluations, thus, we call it MM framework. The art of evaluation investigated within MM framework, based also on systematic storing and retrieving of experts knowledge. Using MM framework, the administrator of the ES could evaluate and enhance the S-level of their system, without being an expert in security.

Shao, Y., Liu, B., Li, G., Yan, R..  2017.  A Fault Diagnosis Expert System for Flight Control Software Based on SFMEA and SFTA. 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :626–627.
Many accidents occurred frequently in aerospace applications, traditional software reliability analysis methods are not enough for modern flight control software. Developing a comprehensive, effective and intelligent method for software fault diagnosis is urgent for airborne software engineering. Under this background, we constructed a fault diagnosis expert system for flight control software which combines software failure mode and effect analysis with software fault tree analysis. To simplify the analysis, the software fault knowledge of four modules is acquired by reliability analysis methods. Then by taking full advantage of the CLIPS shell, knowledge representation and inference engine can be realized smoothly. Finally, we integrated CLIPS into VC++ to achieve visualization, fault diagnosis and inference for flight control software can be performed in the human-computer interaction interface. The results illustrate that the system is able to diagnose software fault, analysis the reasons and present some reasonable solutions like a human expert.
Sakumoto, S., Kanaoka, A..  2017.  Improvement of Privacy Preserved Rule-Based Risk Analysis via Secure Multi-Party Computation. 2017 12th Asia Joint Conference on Information Security (AsiaJCIS). :15–22.

Currently, when companies conduct risk analysis of own networks and systems, it is common to outsource risk analysis to third-party experts. At that time, the company passes the information used for risk analysis including confidential information such as network configuration to third-party expert. It raises the risk of leakage and abuse of confidential information. Therefore, a method of risk analysis by using secure computation without passing confidential information of company has been proposed. Although Liu's method have firstly achieved secure risk analysis method using multiparty computation and attack tree analysis, it has several problems to be practical. In this paper, improvement of secure risk analysis method is proposed. It can dynamically reduce compilation time, enhance scale of target network and system without increasing execution time. Experimental work is carried out by prototype implementation. As a result, we achieved improved performance in compile time and enhance scale of target with equivalent performance on execution time.

2018-09-12
Renukadevi, B., Raja, S. D. M..  2017.  Deep packet inspection Management application in SDN. 2017 2nd International Conference on Computing and Communications Technologies (ICCCT). :256–259.

DPI Management application which resides on the north-bound of SDN architecture is to analyze the application signature data from the network. The data being read and analyzed are of format JSON for effective data representation and flows provisioned from North-bound application is also of JSON format. The data analytic engine analyzes the data stored in the non-relational data base and provides the information about real-time applications used by the network users. Allows the operator to provision flows dynamically with the data from the network to allow/block flows and also to boost the bandwidth. The DPI Management application allows decoupling of application with the controller; thus providing the facility to run it in any hyper-visor within network. Able to publish SNMP trap notifications to the network operators with application threshold and flow provisioning behavior. Data purging from non-relational database at frequent intervals to remove the obsolete analyzed data.

2018-08-23
Lee, J., Kim, Y. S., Kim, J. H., Kim, I. K..  2017.  Toward the SIEM architecture for cloud-based security services. 2017 IEEE Conference on Communications and Network Security (CNS). :398–399.

Cloud Computing represents one of the most significant shifts in information technology and it enables to provide cloud-based security service such as Security-as-a-service (SECaaS). Improving of the cloud computing technologies, the traditional SIEM paradigm is able to shift to cloud-based security services. In this paper, we propose the SIEM architecture that can be deployed to the SECaaS platform which we have been developing for analyzing and recognizing intelligent cyber-threat based on virtualization technologies.

2018-06-20
Searles, R., Xu, L., Killian, W., Vanderbruggen, T., Forren, T., Howe, J., Pearson, Z., Shannon, C., Simmons, J., Cavazos, J..  2017.  Parallelization of Machine Learning Applied to Call Graphs of Binaries for Malware Detection. 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). :69–77.

Malicious applications have become increasingly numerous. This demands adaptive, learning-based techniques for constructing malware detection engines, instead of the traditional manual-based strategies. Prior work in learning-based malware detection engines primarily focuses on dynamic trace analysis and byte-level n-grams. Our approach in this paper differs in that we use compiler intermediate representations, i.e., the callgraph representation of binaries. Using graph-based program representations for learning provides structure of the program, which can be used to learn more advanced patterns. We use the Shortest Path Graph Kernel (SPGK) to identify similarities between call graphs extracted from binaries. The output similarity matrix is fed into a Support Vector Machine (SVM) algorithm to construct highly-accurate models to predict whether a binary is malicious or not. However, SPGK is computationally expensive due to the size of the input graphs. Therefore, we evaluate different parallelization methods for CPUs and GPUs to speed up this kernel, allowing us to continuously construct up-to-date models in a timely manner. Our hybrid implementation, which leverages both CPU and GPU, yields the best performance, achieving up to a 14.2x improvement over our already optimized OpenMP version. We compared our generated graph-based models to previously state-of-the-art feature vector 2-gram and 3-gram models on a dataset consisting of over 22,000 binaries. We show that our classification accuracy using graphs is over 19% higher than either n-gram model and gives a false positive rate (FPR) of less than 0.1%. We are also able to consider large call graphs and dataset sizes because of the reduced execution time of our parallelized SPGK implementation.

Petersen, E., To, M. A., Maag, S..  2017.  A novel online CEP learning engine for MANET IDS. 2017 IEEE 9th Latin-American Conference on Communications (LATINCOM). :1–6.

In recent years the use of wireless ad hoc networks has seen an increase of applications. A big part of the research has focused on Mobile Ad Hoc Networks (MAnETs), due to its implementations in vehicular networks, battlefield communications, among others. These peer-to-peer networks usually test novel communications protocols, but leave out the network security part. A wide range of attacks can happen as in wired networks, some of them being more damaging in MANETs. Because of the characteristics of these networks, conventional methods for detection of attack traffic are ineffective. Intrusion Detection Systems (IDSs) are constructed on various detection techniques, but one of the most important is anomaly detection. IDSs based only in past attacks signatures are less effective, even more if these IDSs are centralized. Our work focuses on adding a novel Machine Learning technique to the detection engine, which recognizes attack traffic in an online way (not to store and analyze after), re-writing IDS rules on the fly. Experiments were done using the Dockemu emulation tool with Linux Containers, IPv6 and OLSR as routing protocol, leading to promising results.

2018-06-07
Appiah, B., Opoku-Mensah, E., Qin, Z..  2017.  SQL injection attack detection using fingerprints and pattern matching technique. 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). :583–587.

Web-Based applications are becoming more increasingly technically complex and sophisticated. The very nature of their feature-rich design and their capability to collate, process, and disseminate information over the Internet or from within an intranet makes them a popular target for attack. According to Open Web Application Security Project (OWASP) Top Ten Cheat sheet-2017, SQL Injection Attack is at peak among online attacks. This can be attributed primarily to lack of awareness on software security. Developing effective SQL injection detection approaches has been a challenge in spite of extensive research in this area. In this paper, we propose a signature based SQL injection attack detection framework by integrating fingerprinting method and Pattern Matching to distinguish genuine SQL queries from malicious queries. Our framework monitors SQL queries to the database and compares them against a dataset of signatures from known SQL injection attacks. If the fingerprint method cannot determine the legitimacy of query alone, then the Aho Corasick algorithm is invoked to ascertain whether attack signatures appear in the queries. The initial experimental results of our framework indicate the approach can identify wide variety of SQL injection attacks with negligible impact on performance.

Whatmough, P. N., Lee, S. K., Lee, H., Rama, S., Brooks, D., Wei, G. Y..  2017.  14.3 A 28nm SoC with a 1.2GHz 568nJ/prediction sparse deep-neural-network engine with \#x003E;0.1 timing error rate tolerance for IoT applications. 2017 IEEE International Solid-State Circuits Conference (ISSCC). :242–243.

This paper presents a 28nm SoC with a programmable FC-DNN accelerator design that demonstrates: (1) HW support to exploit data sparsity by eliding unnecessary computations (4× energy reduction); (2) improved algorithmic error tolerance using sign-magnitude number format for weights and datapath computation; (3) improved circuit-level timing violation tolerance in datapath logic via timeborrowing; (4) combined circuit and algorithmic resilience with Razor timing violation detection to reduce energy via VDD scaling or increase throughput via FCLK scaling; and (5) high classification accuracy (98.36% for MNIST test set) while tolerating aggregate timing violation rates \textbackslashtextgreater10-1. The accelerator achieves a minimum energy of 0.36μJ/pred at 667MHz, maximum throughput at 1.2GHz and 0.57μJ/pred, or a 10%-margined operating point at 1GHz and 0.58μJ/pred.

2018-05-24
HamlAbadi, K. G., Saghiri, A. M., Vahdati, M., TakhtFooladi, M. Dehghan, Meybodi, M. R..  2017.  A Framework for Cognitive Recommender Systems in the Internet of Things (IoT). 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI). :0971–0976.

Internet of Things (IoT) will be emerged over many of devices that are dynamically networked. Because of distributed and dynamic nature of IoT, designing a recommender system for them is a challenging problem. Recently, cognitive systems are used to design modern frameworks in different types of computer applications such as cognitive radio networks and cognitive peer-to-peer networks. A cognitive system can learn to improve its performance while operating under its unknown environment. In this paper, we propose a framework for cognitive recommender systems in IoT. To the best of our knowledge, there is no recommender system based on cognitive systems in the IoT. The proposed algorithm is compared with the existing recommender systems.

2018-03-26
Ma, H., Tao, O., Zhao, C., Li, P., Wang, L..  2017.  Impact of Replacement Policies on Static-Dynamic Query Results Cache in Web Search Engines. 2017 IEEE International Conference on Intelligence and Security Informatics (ISI). :137–139.

Caching query results is an efficient technique for Web search engines. A state-of-the-art approach named Static-Dynamic Cache (SDC) is widely used in practice. Replacement policy is the key factor on the performance of cache system, and has been widely studied such as LIRS, ARC, CLOCK, SKLRU and RANDOM in different research areas. In this paper, we discussed replacement policies for static-dynamic cache and conducted the experiments on real large scale query logs from two famous commercial Web search engine companies. The experimental results show that ARC replacement policy could work well with static-dynamic cache, especially for large scale query results cache.

2018-02-28
Sagisi, J., Tront, J., Marchany, R..  2017.  System architectural design of a hardware engine for moving target IPv6 defense over IEEE 802.3 Ethernet. MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM). :551–556.

The Department of Homeland Security Cyber Security Division (CSD) chose Moving Target Defense as one of the fourteen primary Technical Topic Areas pertinent to securing federal networks and the larger Internet. Moving Target Defense over IPv6 (MT6D) employs an obscuration technique offering keyed access to hosts at a network level without altering existing network infrastructure. This is accomplished through cryptographic dynamic addressing, whereby a new network address is bound to an interface every few seconds in a coordinated manner. The goal of this research is to produce a Register Transfer Level (RTL) network security processor implementation to enable the production of an Application Specific Integrated Circuit (ASIC) variant of MT6D processor for wide deployment. RTL development is challenging in that it must provide system level functions that are normally provided by the Operating System's kernel and supported libraries. This paper presents the architectural design of a hardware engine for MT6D (HE-MT6D) and is complete in simulation. Unique contributions are an inline stream-based network packet processor with a Complex Instruction Set Computer (CISC) architecture, Network Time Protocol listener, and theoretical increased performance over previous software implementations.

2018-02-21
Conti, F., Schilling, R., Schiavone, P. D., Pullini, A., Rossi, D., Gürkaynak, F. K., Muehlberghuber, M., Gautschi, M., Loi, I., Haugou, G. et al..  2017.  An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics. IEEE Transactions on Circuits and Systems I: Regular Papers. 64:2481–2494.

Near-sensor data analytics is a promising direction for internet-of-things endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data are stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a system-on-chip (SoC) based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65-nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep convolutional neural network (CNN) consuming 3.16pJ per equivalent reduced instruction set computer operation, local CNN-based face detection with secured remote recognition in 5.74pJ/op, and seizure detection with encrypted data collection from electroencephalogram within 12.7pJ/op.