Biblio

This paper proposes a lightweight digital signing scheme for supporting document signing on mobile devices connected to cloud computing. We employ elliptic curve (ECC) digital signature algorithm (ECDSA) for key pair generation done at mobile device and introduce outsourced proxy (OSP) to decrypt the encrypted file and compute hash value of the files stored in the cloud system. In our model, a mobile client invokes fixed-sized message digests to be signed with a private key stored in the device and produces the digital signature. Then, the signature is returned to the proxy for embedding it onto the original file. To this end, the trust between proxy and mobile devices is guaranteed by PKI technique. Based on the lightweight property of ECC and the modular design of our OSP, our scheme delivers the practical solution that allows mobile users to create their own digital signatures onto documents in a secure and efficient way. We also present the implementation details including system development and experimental evaluation to demonstrate the efficiency of our proposed system.

This study used the support vector machine (SVM) algorithm to predict Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks on a proxy server. Proxy-servers are prone to attacks such as DoS and DDoS and existing detection and prediction systems are inefficient. Three convex optimization problems using the Gaussian, linear and non-linear kernel methods were solved using the SVM module to detect the attacks. The SVM module and proxy server were implemented in Python and javascript respectively and made to run on a local network. Four other computers running on the same network where made to each communicate with the proxy server (two dedicated to attack the server). The server was able to detect and filter out the malicious requests from the attacking clients. Hence, the SVM module can effectively predict cyber attacks and can be integrated into any server to detect such attacks for improved security.

Over the last decade, a technology called Internet of Things (IoT) has been evolving at a rapid pace. It enables the development of endless applications in view of availability of affordable components which provide smart ecosystems. The IoT devices are constrained devices which are connected to the internet and perform sensing tasks. Each device is identified by their unique address and also makes use of the Constrained Application Protocol (CoAP) as one of the main web transfer protocols. It is an application layer protocol which does not maintain secure channels to transfer information. For authentication and end-to-end security, Datagram Transport Layer Security (DTLS) is one of the possible approaches to boost the security aspect of CoAP, in addition to which there are many suggested ways to protect the transmission of sensitive information. CoAP uses DTLS as a secure protocol and UDP as a transfer protocol. Therefore, the attacks on UDP or DTLS could be assigned as a CoAP attack. An attack on DTLS could possibly be launched in a single session and a strong authentication mechanism is needed. Man-In-The-Middle attack is one the peak security issues in CoAP as cited by Request For Comments(RFC) 7252, which encompasses attacks like Sniffing, Spoofing, Denial of Service (DoS), Hijacking, Cross-Protocol attacks and other attacks including Replay attacks and Relay attacks. In this work, a client-server architecture is setup, whose end devices communicate using CoAP. Also, a proxy system was installed across the client side to launch an active interception between the client and the server. The work will further be enhanced to provide solutions to mitigate these attacks.

The privacy of people on internet is getting reduced day by day. Data records of many prestigious organizations are getting corrupted due to computer malwares. Computer viruses are becoming more advanced. Hackers are able penetrate into a network and able to manipulate data. In this paper, describes the types of malwares like Trojans, boot sector virus, polymorphic virus, etc., and some of the hacking techniques which include DOS attack, DDoS attack, brute forcing, man in the middle attack, social engineering, information gathering tools, spoofing, sniffing. Counter measures for cyber attacks include VPN, proxy, tor (browser), firewall, antivirus etc., to understand the need of cyber security.

Smart Grids require extensive communication to enable safe and stable energy supply in the age of decentralized and dynamic energy production and consumption. To protect the communication in this critical infrastructure, public authorities mandate smart meter gateways (SMGWs) to be in control of the communication security. To this end, the SMGW intercepts all inbound and outbound communication of its premise, e.g., a factory or smart home, and forwards it on secure channels that the SMGW established itself. However, using the SMGW as proxy, local devices can neither review the security of these remote connections established by the SMGW nor enforce higher security guarantees than established by the all in one configuration of the SMGW which does not allow for use case-specific security settings. We present mechanisms that enable local devices to regain this insight and control over the full connection, i.e., up to the final receiver, while retaining the SMGW's ability to ensure a suitable security level. Our evaluation shows modest computation and transmission overheads for this increased security in the critical smart grid infrastructure.

The data transmitted from the wearable device commonly includes sensitive data. So, application service using the data collected from the unauthorized wearable devices can cause serious problems. Also, it is important to authenticate any wearable device and then, protect the transmitted data between the wearable devices and the application server. In this paper, we propose an authentication protocol, which is designed by using the Transport Layer Security (TLS) handshake protocol combined with a mobile authentication proxy. By using the proposed authentication protocol, we can authenticate the wearable device. And we can secure data transmission since session key is shared between the wearable device and the application server. In addition, the proposed authentication protocol is secure even when the mobile authentication proxy is unreliable.

This paper presents an approach to formalizing and enforcing a class of use privacy properties in data-driven systems. In contrast to prior work, we focus on use restrictions on proxies (i.e. strong predictors) of protected information types. Our definition relates proxy use to intermediate computations that occur in a program, and identify two essential properties that characterize this behavior: 1) its result is strongly associated with the protected information type in question, and 2) it is likely to causally affect the final output of the program. For a specific instantiation of this definition, we present a program analysis technique that detects instances of proxy use in a model, and provides a witness that identifies which parts of the corresponding program exhibit the behavior. Recognizing that not all instances of proxy use of a protected information type are inappropriate, we make use of a normative judgment oracle that makes this inappropriateness determination for a given witness. Our repair algorithm uses the witness of an inappropriate proxy use to transform the model into one that provably does not exhibit proxy use, while avoiding changes that unduly affect classification accuracy. Using a corpus of social datasets, our evaluation shows that these algorithms are able to detect proxy use instances that would be difficult to find using existing techniques, and subsequently remove them while maintaining acceptable classification performance.

Internet-of-Things devices often collect and transmit sensitive information like camera footage, health monitoring data, or whether someone is home. These devices protect data in transit with end-to-end encryption, typically using TLS connections between devices and associated cloud services. But these TLS connections also prevent device owners from observing what their own devices are saying about them. Unlike in traditional Internet applications, where the end user controls one end of a connection (e.g., their web browser) and can observe its communication, Internet-of-Things vendors typically control the software in both the device and the cloud. As a result, owners have no way to audit the behavior of their own devices, leaving them little choice but to hope that these devices are transmitting only what they should. This paper presents TLS–Rotate and Release (TLS-RaR), a system that allows device owners (e.g., consumers, security researchers, and consumer watchdogs) to authorize devices, called auditors, to decrypt and verify recent TLS traffic without compromising future traffic. Unlike prior work, TLS-RaR requires no changes to TLS's wire format or cipher suites, and it allows the device's owner to conduct a surprise inspection of recent traffic, without prior notice to the device that its communications will be audited.

Mobile Devices are part of our lives and we store a lot of private information on it as well as use services that handle sensitive information (e.g. mobile health apps). Whenever users install an application on their smartphones they have to decide whether to trust the applications and share private and sensitive data with at least the developer-owned services. But almost all modern apps not only transmit data to the developer owned servers but also send information to advertising-, analyzing and tracking partners. This paper presents an approach for a "privacy- proxy" which enables to filter unwanted data traffic to third party services without installing additional applications on the smartphone. It is based on a firewall using a black list of tracking- and analyzing networks which is automatically updated on a daily basis. The proof of concept has been implemented with open source components on a Raspberry Pi.

Botnets have become one of the most significant cyber threats over the last decade. The diffusion of the "Internet of Things" and its for-profit exploitation, contributed to botnets spread and sophistication, thus providing real, efficient and profitable criminal cyber-services. Recent research on botnet detection focuses on traffic pattern-based detection, and on analyzing the network traffic generated by the infected hosts, in order to find behavioral patterns independent from the specific payloads, architectures and protocols. In this paper we address the periodic behavioral patterns of infected hosts communicating with their Command-and-Control servers. The main novelty introduced is related to the traffic analysis in the frequency domain without using the well-known Fast Fourier Transform. Moreover, the mentioned analysis is performed through the exploitation of the proxy logs, easily deployable on almost every real-world scenario, from enterprise networks to mobile devices.

We measure the prevalence and uses of TLS proxies using a Flash tool deployed with a Google AdWords campaign. We generate 2.9 million certificate tests and find that 1 in 250 TLS connections are TLS-proxied. The majority of these proxies appear to be benevolent, however we identify over 1,000 cases where three malware products are using this technology nefariously. We also find numerous instances of negligent, duplicitous, and suspicious behavior, some of which degrade security for users without their knowledge. Distinguishing these types of practices is challenging in practice, indicating a need for transparency and user awareness.

Botnets are considered one of the most dangerous species of network-based attack today because they involve the use of very large coordinated groups of hosts simultaneously. The behavioral analysis of computer networks is at the basis of the modern botnet detection methods, in order to intercept traffic generated by malwares for which signatures do not exist yet. Defining a pattern of features to be placed at the basis of behavioral analysis, puts the emphasis on the quantity and quality of information to be caught and used to mark data streams as normal or abnormal. The problem is even more evident if we consider extensive computer networks or clouds. With the present paper we intend to show how heuristics applied to large-scale proxy logs, considering a typical phase of the life cycle of botnets such as the search for C&C Servers through AGDs (Algorithmically Generated Domains), may provide effective and extremely rapid results. The present work will introduce some novel paradigms. The first is that some of the elements of the supply chain of botnets could be completed without any interaction with the Internet, mostly in presence of wide computer networks and/or clouds. The second is that behind a large number of workstations there are usually "human beings" and it is unlikely that their behaviors will cause marked changes in the interaction with the Internet in a fairly narrow time frame. Finally, AGDs can highlight, at the moment, common lexical features, detectable quickly and without using any black/white list.