Biblio
P2P botnet has become one of the most serious threats to today's network security. It can be used to launch kinds of malicious activities, ranging from spamming to distributed denial of service attack. However, the detection of P2P botnet is always challenging because of its decentralized architecture. In this paper, we propose a two-stage P2P botnet detection method which only relies on several traffic statistical features. This method first detects P2P hosts based on three statistical features, and then distinguishes P2P bots from benign P2P hosts by means of another two statistical features. Experimental evaluations on real-world traffic datasets shows that our method is able to detect hidden P2P bots with a detection accuracy of 99.7% and a false positive rate of only 0.3% within 5 minutes.
Among the various challenges faced by the P2P file sharing systems like BitTorrent, the most common attack on the basic foundation of such systems is: Free-riding. Generally, free-riders are the users in the file sharing network who avoid contributing any resources but tend to consume the resources unethically from the P2P network whereas white-washers are more specific category of free-riders that voluntarily leave the system in a frequent fashion and appearing again and again with different identities to escape from the penal actions imposed by the network. BitTorrent being a collaborative distributed platform requires techniques for discouraging and punishing such user behavior. In this paper, we propose that ``Instead of punishing, we may focus more on rewarding the honest peers''. This approach could be presented as an alternative to other mechanisms of rewarding the peers like tit-for-tat [10], reciprocity based etc., built for the BitTorrent platform. The prime objective of BitTrusty is: providing incentives to the cooperative peers by rewarding in terms of cryptocoins based on blockchain. We have anticipated three ways of achieving the above defined objective. We are further investigating on how to integrate these two technologies of distributed systems viz. P2P file sharing systems and blockchain, and with this new paradigm, interesting research areas can be further developed, both in the field of P2P cryptocurrency networks and also when these networks are combined with other distributed scenarios.
Given the centralized architecture of cloud computing, there is a genuine concern about its ability to adequately cope with the demands of connecting devices which are sharply increasing in number and capacity. This has led to the emergence of edge computing technologies, including but not limited to mobile edge-clouds. As a branch of Peer-to-Peer (P2P) networks, mobile edge-clouds inherits disturbing security concerns which have not been adequately addressed in previous methods. P2P security systems have featured many trust-based methods owing to their suitability and cost advantage, but these approaches still lack in a number of ways. They mostly focus on protecting client nodes from malicious service providers, but downplay the security of service provider nodes, thereby creating potential loopholes for bandwidth attack. Similarly, trust bootstrapping is often via default scores, or based on heuristics that does not reflect the identity of a newcomer. This work has patched these inherent loopholes and improved fairness among participating peers. The use cases of mobile edge-clouds have been particularly considered and a scalable reputation based security mechanism was derived to suit them. BitTorrent protocol was modified to form a suitable test bed, using Peersim simulator. The proposed method was compared to some related methods in the literature through detailed simulations. Results show that the new method can foster trust and significantly improve network security, in comparison to previous similar systems.
Botnet on a mobile platform is one of the severe problems for the Internet security. It causes damages to both individual users and the economic system. Botnet detection is required to stop these damages. However, botmasters keep developing their botnets. Peer-to-peer (P2P) connection and encryption are used in the botnet communication to avoid the exposure and takedown. To tackle this problem, we propose the P2P mobile botnet detection by using communication patterns. A graph representation called "graphlet" is used to capture the natural communication patterns of a P2P mobile botnet. The graphlet-based detection does not violate the user privacy, and also effective with encrypted traffic. Furthermore, a machine learning technique with graphlet-based features can detect the P2P mobile botnet even it runs simultaneously with other applications such as Facebook, Line, Skype, YouTube, and Web. Moreover, we employ the Principal Components Analysis (PCA) to analyze graphlet's features to leverage the detection performance when the botnet coexists with dense traffic such as Web traffic. Our work focuses on the real traffic of an advanced P2P mobile botnet named "NotCompatible.C". The detection performance shows high F-measure scores of 0.93, even when sampling only 10% of traffic in a 3-minute duration.
The importance of peer-to-peer (P2P) network overlays produced enormous interest in the research community due to their robustness, scalability, and increase of data availability. P2P networks are overlays of logically connected hosts and other nodes including servers. P2P networks allow users to share their files without the need for any centralized servers. Since P2P networks are largely constructed of end-hosts, they are susceptible to abuse and malicious activity, such as sybil attacks. Impostors perform sybil attacks by assigning nodes multiple addresses, as opposed to a single address, with the goal of degrading network quality. Sybil nodes will spread malicious data and provide bogus responses to requests. To prevent sybil attacks from occurring, a novel defense mechanism is proposed. In the proposed scheme, the DHT key-space is divided and treated in a similar manner to radio frequency allocation incensing. An overlay of trusted nodes is used to detect and handle sybil nodes with the aid of source-destination pairs reporting on each other. The simulation results show that the proposed scheme detects sybil nodes in large sized networks with thousands of interactions.
Social networking sites such as Flickr, YouTube, Facebook, etc. contain huge amount of user contributed data for a variety of real-world events. We describe an unsupervised approach to the problem of automatically detecting subgroups of people holding similar tastes or either taste. Item or taste tags play an important role in detecting group or subgroup, if two or more persons share the same opinion on the item or taste, they tend to use similar content. We consider the latter to be an implicit attitude. In this paper, we have investigated the impact of implicit and explicit attitude in two genres of social media discussion data, more formal wikipedia discussions and a debate discussion forum that is much more informal. Experimental results strongly suggest that implicit attitude is an important complement for explicit attitudes (expressed via sentiment) and it can improve the sub-group detection performance independent of genre. Here, we have proposed taste-based group, which can enhance the quality of service.
Existing compact routing schemes, e.g., Thorup and Zwick [SPAA 2001] and Chechik [PODC 2013], often have no means to tolerate failures, once the system has been setup and started. This paper presents, to our knowledge, the first self-healing compact routing scheme. Besides, our schemes are developed for low memory nodes, i.e., nodes need only O(log2 n) memory, and are thus, compact schemes. We introduce two algorithms of independent interest: The first is CompactFT, a novel compact version (using only O(log n) local memory) of the self-healing algorithm Forgiving Tree of Hayes et al. [PODC 2008]. The second algorithm (CompactFTZ) combines CompactFT with Thorup-Zwick's tree-based compact routing scheme [SPAA 2001] to produce a fully compact self-healing routing scheme. In the self-healing model, the adversary deletes nodes one at a time with the affected nodes self-healing locally by adding few edges. CompactFT recovers from each attack in only O(1) time and Δ messages, with only +3 degree increase and O(logΔ) graph diameter increase, over any sequence of deletions (Δ is the initial maximum degree). Additionally, CompactFTZ guarantees delivery of a packet sent from sender s as long as the receiver t has not been deleted, with only an additional O(y logΔ) latency, where y is the number of nodes that have been deleted on the path between s and t. If t has been deleted, s gets informed and the packet removed from the network.
The main usage pattern of internet is shifting from traditional host-to-host central model to content dissemination model. It leads to the pretty prompt growth in Internet content. CDN and P2P are two mainstream techmologies to provide streaming content services in the current Internet. In recent years, some researchers have begun to focus on CDN-P2P-hybrid architecture and ISP-friendly P2P content delivery technology. Web applications have become one of the fundamental internet services. How to effectively support the popular browser-based web application is one of keys to success for future internet projects. This paper proposes ID based browser with caching in IDNet. IDNet consists of id/locator separation scheme and domain-insulated autonomous network architecture (DIANA) which redesign the future internet in the clean slate basis. Experiment shows that ID web browser with caching function can support how to disseminate content and how to find the closet network in IDNet having identical contents.