Visible to the public Biblio

Found 2363 results

Filters: Keyword is Human Behavior  [Clear All Filters]
2019-12-05
Akhtar, Nabeel, Matta, Ibrahim, Raza, Ali, Wang, Yuefeng.  2018.  EL-SEC: ELastic Management of Security Applications on Virtualized Infrastructure. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :778-783.

The concept of Virtualized Network Functions (VNFs) aims to move Network Functions (NFs) out of dedicated hardware devices into software that runs on commodity hardware. A single NF consists of multiple VNF instances, usually running on virtual machines in a cloud infrastructure. The elastic management of an NF refers to load management across the VNF instances and the autonomic scaling of the number of VNF instances as the load on the NF changes. In this paper, we present EL-SEC, an autonomic framework to elastically manage security NFs on a virtualized infrastructure. As a use case, we deploy the Snort Intrusion Detection System as the NF on the GENI testbed. Concepts from control theory are used to create an Elastic Manager, which implements various controllers - in this paper, Proportional Integral (PI) and Proportional Integral Derivative (PID) - to direct traffic across the VNF Snort instances by monitoring the current load. RINA (a clean-slate Recursive InterNetwork Architecture) is used to build a distributed application that monitors load and collects Snort alerts, which are processed by the Elastic Manager and an Attack Analyzer, respectively. Software Defined Networking (SDN) is used to steer traffic through the VNF instances, and to block attack traffic. Our results show that virtualized security NFs can be easily deployed using our EL-SEC framework. With the help of real-time graphs, we show that PI and PID controllers can be used to easily scale the system, which leads to quicker detection of attacks.

Bertino, Elisa, Nabeel, Mohamed.  2018.  Securing Named Data Networks: Challenges and the Way Forward. Proceedings of the 23Nd ACM on Symposium on Access Control Models and Technologies. :51-59.

Despite decades of research on the Internet security, we constantly hear about mega data breaches and malware infections affecting hundreds of millions of hosts. The key reason is that the current threat model of the Internet relies on two assumptions that no longer hold true: (1) Web servers, hosting the content, are secure, (2) each Internet connection starts from the original content provider and terminates at the content consumer. Internet security is today merely patched on top of the TCP/IP protocol stack. In order to achieve comprehensive security for the Internet, we believe that a clean-slate approach must be adopted where a content based security model is employed. Named Data Networking (NDN) is a step in this direction which is envisioned to be the next generation Internet architecture based on a content centric communication model. NDN is currently being designed with security as a key requirement, and thus to support content integrity, authenticity, confidentiality and privacy. However, in order to meet such a requirement, one needs to overcome several challenges, especially in either large operational environments or resource constrained networks. In this paper, we explore the security challenges in achieving comprehensive content security in NDN and propose a research agenda to address some of the challenges.

Hanford, Nathan, Ahuja, Vishal, Farrens, Matthew K., Tierney, Brian, Ghosal, Dipak.  2018.  A Survey of End-System Optimizations for High-Speed Networks. ACM Comput. Surv.. 51:54:1-54:36.

The gap is widening between the processor clock speed of end-system architectures and network throughput capabilities. It is now physically possible to provide single-flow throughput of speeds up to 100 Gbps, and 400 Gbps will soon be possible. Most current research into high-speed data networking focuses on managing expanding network capabilities within datacenter Local Area Networks (LANs) or efficiently multiplexing millions of relatively small flows through a Wide Area Network (WAN). However, datacenter hyper-convergence places high-throughput networking workloads on general-purpose hardware, and distributed High-Performance Computing (HPC) applications require time-sensitive, high-throughput end-to-end flows (also referred to as ``elephant flows'') to occur over WANs. For these applications, the bottleneck is often the end-system and not the intervening network. Since the problem of the end-system bottleneck was uncovered, many techniques have been developed which address this mismatch with varying degrees of effectiveness. In this survey, we describe the most promising techniques, beginning with network architectures and NIC design, continuing with operating and end-system architectures, and concluding with clean-slate protocol design.

Wilcox, James R., Flanagan, Cormac, Freund, Stephen N..  2018.  VerifiedFT: A Verified, High-Performance Precise Dynamic Race Detector. Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. :354-367.

Dynamic data race detectors are valuable tools for testing and validating concurrent software, but to achieve good performance they are typically implemented using sophisticated concurrent algorithms. Thus, they are ironically prone to the exact same kind of concurrency bugs they are designed to detect. To address these problems, we have developed VerifiedFT, a clean slate redesign of the FastTrack race detector [19]. The VerifiedFT analysis provides the same precision guarantee as FastTrack, but is simpler to implement correctly and efficiently, enabling us to mechanically verify an implementation of its core algorithm using CIVL [27]. Moreover, VerifiedFT provides these correctness guarantees without sacrificing any performance over current state-of-the-art (but complex and unverified) FastTrack implementations for Java.

Ott, David E..  2018.  Software Defined Infrastructure: Rethinking Cybersecurity with a More Capable Toolset. SIGOPS Oper. Syst. Rev.. 52:129-133.

In Software Defined Infrastructure (SDI), virtualization techniques are used to decouple applications and higher-level services from their underlying physical compute, storage, and network resources. The approach offers a set of powerful new capabilities (isolation, encapsulation, portability, interposition), including the formation of a software-based, infrastructure-wide control plane for orchestrated management. In this position paper, we identify opportunities for revisiting ongoing cybersecurity challenges using SDI as a powerful new toolset. Benefits of this approach can be broadly utilized in public, private, and hybrid clouds, data centers, enterprise computing, IoT deployments, and more. The discussion motivates the research challenge underlying VMware's partnership with the National Science Foundation to fund novel and foundational research in this area. Known as the NSF/VMware Partnership on Software Defined Infrastructure as a Foundation for Clean-Slate Computing Security (SDI-CSCS), the jointly funded university research program is set to begin in the fall of 2017.

Leißa, Roland, Boesche, Klaas, Hack, Sebastian, Pérard-Gayot, Arsène, Membarth, Richard, Slusallek, Philipp, Müller, André, Schmidt, Bertil.  2018.  AnyDSL: A Partial Evaluation Framework for Programming High-Performance Libraries. Proc. ACM Program. Lang.. 2:119:1-119:30.

This paper advocates programming high-performance code using partial evaluation. We present a clean-slate programming system with a simple, annotation-based, online partial evaluator that operates on a CPS-style intermediate representation. Our system exposes code generation for accelerators (vectorization/parallelization for CPUs and GPUs) via compiler-known higher-order functions that can be subjected to partial evaluation. This way, generic implementations can be instantiated with target-specific code at compile time. In our experimental evaluation we present three extensive case studies from image processing, ray tracing, and genome sequence alignment. We demonstrate that using partial evaluation, we obtain high-performance implementations for CPUs and GPUs from one language and one code base in a generic way. The performance of our codes is mostly within 10%, often closer to the performance of multi man-year, industry-grade, manually-optimized expert codes that are considered to be among the top contenders in their fields.

Sahu, Abhijeet, Goulart, Ana.  2019.  Implementation of a C-UNB Module for NS-3 and Validation for DLMS-COSEM Application Layer Protocol. 2019 IEEE ComSoc International Communications Quality and Reliability Workshop (CQR). :1-6.

The number of sensors and embedded devices in an urban area can be on the order of thousands. New low-power wide area (LPWA) wireless network technologies have been proposed to support this large number of asynchronous, low-bandwidth devices. Among them, the Cooperative UltraNarrowband (C-UNB) is a clean-slate cellular network technology to connect these devices to a remote site or data collection server. C-UNB employs small bandwidth channels, and a lightweight random access protocol. In this paper, a new application is investigated - the use of C-UNB wireless networks to support the Advanced Metering Infrastructure (AMI), in order to facilitate the communication between smart meters and utilities. To this end, we adapted a mathematical model for C-UNB, and implemented a network simulation module in NS-3 to represent C-UNB's physical and medium access control layer. For the application layer, we implemented the DLMS-COSEM protocol, or Device Language Message Specification - Companion Specification for Energy Metering. Details of the simulation module are presented and we conclude that it supports the results of the mathematical model.

Yu, Yiding, Wang, Taotao, Liew, Soung Chang.  2018.  Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks. 2018 IEEE International Conference on Communications (ICC). :1-7.

This paper investigates the use of deep reinforcement learning (DRL) in the design of a "universal" MAC protocol referred to as Deep-reinforcement Learning Multiple Access (DLMA). The design framework is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that "best share spectrum with any network(s), in any environment, without prior knowledge, leveraging on machine-learning technique". While the scope of DARPA SC2 is broad and involves the redesign of PHY, MAC, and Network layers, this paper's focus is narrower and only involves the MAC design. In particular, we consider the problem of sharing time slots among a multiple of time-slotted networks that adopt different MAC protocols. One of the MAC protocols is DLMA. The other two are TDMA and ALOHA. The DRL agents of DLMA do not know that the other two MAC protocols are TDMA and ALOHA. Yet, by a series of observations of the environment, its own actions, and the rewards - in accordance with the DRL algorithmic framework - a DRL agent can learn the optimal MAC strategy for harmonious co-existence with TDMA and ALOHA nodes. In particular, the use of neural networks in DRL (as opposed to traditional reinforcement learning) allows for fast convergence to optimal solutions and robustness against perturbation in hyper- parameter settings, two essential properties for practical deployment of DLMA in real wireless networks.

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.

2019-11-26
Tian, Ke, Jan, Steve T. K., Hu, Hang, Yao, Danfeng, Wang, Gang.  2018.  Needle in a Haystack: Tracking Down Elite Phishing Domains in the Wild. Proceedings of the Internet Measurement Conference 2018. :429-442.

Today's phishing websites are constantly evolving to deceive users and evade the detection. In this paper, we perform a measurement study on squatting phishing domains where the websites impersonate trusted entities not only at the page content level but also at the web domain level. To search for squatting phishing pages, we scanned five types of squatting domains over 224 million DNS records and identified 657K domains that are likely impersonating 702 popular brands. Then we build a novel machine learning classifier to detect phishing pages from both the web and mobile pages under the squatting domains. A key novelty is that our classifier is built on a careful measurement of evasive behaviors of phishing pages in practice. We introduce new features from visual analysis and optical character recognition (OCR) to overcome the heavy content obfuscation from attackers. In total, we discovered and verified 1,175 squatting phishing pages. We show that these phishing pages are used for various targeted scams, and are highly effective to evade detection. More than 90% of them successfully evaded popular blacklists for at least a month.

Cuzzocrea, Alfredo, Martinelli, Fabio, Mercaldo, Francesco.  2018.  Applying Machine Learning Techniques to Detect and Analyze Web Phishing Attacks. Proceedings of the 20th International Conference on Information Integration and Web-Based Applications & Services. :355-359.

Phishing is a technique aimed to imitate an official websites of any company such as banks, institutes, etc. The purpose of phishing is to theft private and sensitive credentials of users such as password, username or PIN. Phishing detection is a technique to deal with this kind of malicious activity. In this paper we propose a method able to discriminate between web pages aimed to perform phishing attacks and legitimate ones. We exploit state of the art machine learning algorithms in order to build models using indicators that are able to detect phishing activities.

Vrban\v ci\v c, Grega, Fister, Jr., Iztok, Podgorelec, Vili.  2018.  Swarm Intelligence Approaches for Parameter Setting of Deep Learning Neural Network: Case Study on Phishing Websites Classification. Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. :9:1-9:8.

In last decades, the web and online services have revolutionized the modern world. However, by increasing our dependence on online services, as a result, online security threats are also increasing rapidly. One of the most common online security threats is a so-called Phishing attack, the purpose of which is to mimic a legitimate website such as online banking, e-commerce or social networking website in order to obtain sensitive data such as user-names, passwords, financial and health-related information from potential victims. The problem of detecting phishing websites has been addressed many times using various methodologies from conventional classifiers to more complex hybrid methods. Recent advancements in deep learning approaches suggested that the classification of phishing websites using deep learning neural networks should outperform the traditional machine learning algorithms. However, the results of utilizing deep neural networks heavily depend on the setting of different learning parameters. In this paper, we propose a swarm intelligence based approach to parameter setting of deep learning neural network. By applying the proposed approach to the classification of phishing websites, we were able to improve their detection when compared to existing algorithms.

Hassanpour, Reza, Dogdu, Erdogan, Choupani, Roya, Goker, Onur, Nazli, Nazli.  2018.  Phishing E-Mail Detection by Using Deep Learning Algorithms. Proceedings of the ACMSE 2018 Conference. :45:1-45:1.

Phishing e-mails are considered as spam e-mails, which aim to collect sensitive personal information about the users via network. Since the main purpose of this behavior is mostly to harm users financially, it is vital to detect these phishing or spam e-mails immediately to prevent unauthorized access to users' vital information. To detect phishing e-mails, using a quicker and robust classification method is important. Considering the billions of e-mails on the Internet, this classification process is supposed to be done in a limited time to analyze the results. In this work, we present some of the early results on the classification of spam email using deep learning and machine methods. We utilize word2vec to represent emails instead of using the popular keyword or other rule-based methods. Vector representations are then fed into a neural network to create a learning model. We have tested our method on an open dataset and found over 96% accuracy levels with the deep learning classification methods in comparison to the standard machine learning algorithms.

Shirazi, Hossein, Bezawada, Bruhadeshwar, Ray, Indrakshi.  2018.  "Kn0W Thy Doma1N Name": Unbiased Phishing Detection Using Domain Name Based Features. Proceedings of the 23Nd ACM on Symposium on Access Control Models and Technologies. :69-75.

Phishing websites remain a persistent security threat. Thus far, machine learning approaches appear to have the best potential as defenses. But, there are two main concerns with existing machine learning approaches for phishing detection. The first is the large number of training features used and the lack of validating arguments for these feature choices. The second concern is the type of datasets used in the literature that are inadvertently biased with respect to the features based on the website URL or content. To address these concerns, we put forward the intuition that the domain name of phishing websites is the tell-tale sign of phishing and holds the key to successful phishing detection. Accordingly, we design features that model the relationships, visual as well as statistical, of the domain name to the key elements of a phishing website, which are used to snare the end-users. The main value of our feature design is that, to bypass detection, an attacker will find it very difficult to tamper with the visual content of the phishing website without arousing the suspicion of the end user. Our feature set ensures that there is minimal or no bias with respect to a dataset. Our learning model trains with only seven features and achieves a true positive rate of 98% and a classification accuracy of 97%, on sample dataset. Compared to the state-of-the-art work, our per data instance classification is 4 times faster for legitimate websites and 10 times faster for phishing websites. Importantly, we demonstrate the shortcomings of using features based on URLs as they are likely to be biased towards specific datasets. We show the robustness of our learning algorithm by testing on unknown live phishing URLs and achieve a high detection accuracy of \$99.7%\$.

Scheitle, Quirin, Gasser, Oliver, Nolte, Theodor, Amann, Johanna, Brent, Lexi, Carle, Georg, Holz, Ralph, Schmidt, Thomas C., Wählisch, Matthias.  2018.  The Rise of Certificate Transparency and Its Implications on the Internet Ecosystem. Proceedings of the Internet Measurement Conference 2018. :343-349.

In this paper, we analyze the evolution of Certificate Transparency (CT) over time and explore the implications of exposing certificate DNS names from the perspective of security and privacy. We find that certificates in CT logs have seen exponential growth. Website support for CT has also constantly increased, with now 33% of established connections supporting CT. With the increasing deployment of CT, there are also concerns of information leakage due to all certificates being visible in CT logs. To understand this threat, we introduce a CT honeypot and show that data from CT logs is being used to identify targets for scanning campaigns only minutes after certificate issuance. We present and evaluate a methodology to learn and validate new subdomains from the vast number of domains extracted from CT logged certificates.

Lyashenko, Vyacheslav, Kobylin, Oleg, Minenko, Mykyta.  2018.  Tools for Investigating the Phishing Attacks Dynamics. 2018 International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S T). :43-46.

We are exploring new ways to analyze phishing attacks. To do this, we investigate the change in the dynamics of the power of phishing attacks. We also analyze the effectiveness of detection of phishing attacks. We are considering the possibility of using new tools for analyzing phishing attacks. As such tools, the methods of chaos theory and the ideology of wavelet coherence are used. The use of such analysis tools makes it possible to investigate the peculiarities of the phishing attacks occurrence, as well as methods for their identification effectiveness. This allows you to expand the scope of the analysis of phishing attacks. For analysis, we use real data about phishing attacks.

Baykara, Muhammet, Gürel, Zahit Ziya.  2018.  Detection of Phishing Attacks. 2018 6th International Symposium on Digital Forensic and Security (ISDFS). :1-5.

Phishing is a form of cybercrime where an attacker imitates a real person / institution by promoting them as an official person or entity through e-mail or other communication mediums. In this type of cyber attack, the attacker sends malicious links or attachments through phishing e-mails that can perform various functions, including capturing the login credentials or account information of the victim. These e-mails harm victims because of money loss and identity theft. In this study, a software called ``Anti Phishing Simulator'' was developed, giving information about the detection problem of phishing and how to detect phishing emails. With this software, phishing and spam mails are detected by examining mail contents. Classification of spam words added to the database by Bayesian algorithm is provided.

Zabihimayvan, Mahdieh, Doran, Derek.  2019.  Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection. 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). :1-6.

Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.

Patil, Srushti, Dhage, Sudhir.  2019.  A Methodical Overview on Phishing Detection along with an Organized Way to Construct an Anti-Phishing Framework. 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS). :588-593.

Phishing is a security attack to acquire personal information like passwords, credit card details or other account details of a user by means of websites or emails. Phishing websites look similar to the legitimate ones which make it difficult for a layman to differentiate between them. As per the reports of Anti Phishing Working Group (APWG) published in December 2018, phishing against banking services and payment processor was high. Almost all the phishy URLs use HTTPS and use redirects to avoid getting detected. This paper presents a focused literature survey of methods available to detect phishing websites. A comparative study of the in-use anti-phishing tools was accomplished and their limitations were acknowledged. We analyzed the URL-based features used in the past to improve their definitions as per the current scenario which is our major contribution. Also, a step wise procedure of designing an anti-phishing model is discussed to construct an efficient framework which adds to our contribution. Observations made out of this study are stated along with recommendations on existing systems.

Samaila, Musa G., Sequeiros, João B. F., Freire, Mário M., Inácio, Pedro R. M..  2018.  Security Threats and Possible Countermeasures in IoT Applications Covering Different Industry Domains. Proceedings of the 13th International Conference on Availability, Reliability and Security. :16:1-16:9.

The world is witnessing the emerging role of Internet of Things (IoT) as a technology that is transforming different industries, global community and its economy. Currently a plethora of interconnected smart devices have been deployed for diverse pervasive applications and services, and billions more are expected to be connected to the Internet in the near future. The potential benefits of IoT include improved quality of life, convenience, enhanced energy efficiency, and more productivity. Alongside these potential benefits, however, come increased security risks and potential for abuse. Arguably, this is partly because many IoT start-ups and electronics hobbyists lack security expertise, and some established companies do not make security a priority in their designs, and hence they produce IoT devices that are often ill-equipped in terms of security. In this paper, we discuss different IoT application areas, and identify security threats in IoT architecture. We consider security requirements and present typical security threats for each of the application domains. Finally, we present several possible security countermeasures, and introduce the IoT Hardware Platform Security Advisor (IoT-HarPSecA) framework, which is still under development. IoT-HarPSecA is aimed at facilitating the design and prototyping of secure IoT devices.

Chollet, Stéphanie, Pion, Laurent, Barbot, Nicolas, Michel, Clément.  2018.  Secure IoT for a Pervasive Platform. 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). :113-118.

Nowadays, the proliferation of smart, communication-enable devices is opening up many new opportunities of pervasive applications. A major requirement of pervasive applications is to be secured. The complexity to secure pervasive systems is to address a end-to-end security level: from the device to the services according to the entire life cycle of devices, applications and platform. In this article, we propose a solution combining both hardware and software elements to secure communications between devices and pervasive platform based on certificates issued from a Public Key Infrastructure. Our solution is implemented and validated with a real device extended by a secure element and our own Public Key Infrastructure.

Pradhan, Srikanta, Tripathy, Somanath, Nandi, Sukumar.  2018.  Blockchain Based Security Framework for P2P Filesharing System. 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). :1-6.

Peer to Peer (P2P) is a dynamic and self-organized technology, popularly used in File sharing applications to achieve better performance and avoids single point of failure. The popularity of this network has attracted many attackers framing different attacks including Sybil attack, Routing Table Insertion attack (RTI) and Free Riding. Many mitigation methods are also proposed to defend or reduce the impact of such attacks. However, most of those approaches are protocol specific. In this work, we propose a Blockchain based security framework for P2P network to address such security issues. which can be tailored to any P2P file-sharing system.

Tapsell, James, Naeem Akram, Raja, Markantonakis, Konstantinos.  2018.  An Evaluation of the Security of the Bitcoin Peer-To-Peer Network. 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). :1057-1062.

Underpinning the operation of Bitcoin is a peer-to-peer (P2P) network [1] that facilitates the execution of transactions by end users, as well as the transaction confirmation process known as bitcoin mining. The security of this P2P network is vital for the currency to function and subversion of the underlying network can lead to attacks on bitcoin users including theft of bitcoins, manipulation of the mining process and denial of service (DoS). As part of this paper the network protocol and bitcoin core software are analysed, with three bitcoin message exchanges (the connection handshake, GETHEADERS/HEADERS and MEMPOOL/INV) found to be potentially vulnerable to spoofing and use in distributed denial of service (DDoS) attacks. Possible solutions to the identified weaknesses and vulnerabilities are evaluated, such as the introduction of random nonces into network messages exchanges.

Wang, Pengfei, Wang, Fengyu, Lin, Fengbo, Cao, Zhenzhong.  2018.  Identifying Peer-to-Peer Botnets Through Periodicity Behavior Analysis. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :283-288.

Peer-to-Peer botnets have become one of the significant threat against network security due to their distributed properties. The decentralized nature makes their detection challenging. It is important to take measures to detect bots as soon as possible to minimize their harm. In this paper, we propose PeerGrep, a novel system capable of identifying P2P bots. PeerGrep starts from identifying hosts that are likely engaged in P2P communications, and then distinguishes P2P bots from P2P hosts by analyzing their active ratio, packet size and the periodicity of connection to destination IP addresses. The evaluation shows that PeerGrep can identify all P2P bots with quite low FPR even if the malicious P2P application and benign P2P application coexist within the same host or there is only one bot in the monitored network.

Acharjamayum, Irani, Patgiri, Ripon, Devi, Dhruwajita.  2018.  Blockchain: A Tale of Peer to Peer Security. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). :609-617.

The underlying or core technology of Bitcoin cryptocurrency has become a blessing for human being in this era. Everything is gradually changing to digitization in this today's epoch. Bitcoin creates virtual money using Blockchain that's become popular over the world. Blockchain is a shared public ledger, and it includes all transactions which are confirmed. It is almost impossible to crack the hidden information in the blocks of the Blockchain. However, there are certain security and technical challenges like scalability, privacy leakage, selfish mining, etc. which hampers the wide application of Blockchain. In this paper, we briefly discuss this emerging technology namely Blockchain. In addition, we extrapolate in-depth insight on Blockchain technology.