Biblio

In this paper, internet is among the basic necessities of life. Internet has changed each and everybody's lives. So confidentiality of messages is very important over the internet. Steganography is the science of sending secret messages between the sender and intended receiver. It is such a technique that makes the exchange of covert messages possible. Each time a carrier is to be used for achieving steganography. The carrier plays a major role in establishing covert communication channel. This survey paper introduces steganography and its carriers. This paper concentrates on network protocols to be used as a carrier of steganograms. There are a number of protocols available to do so in the networks. Network steganography describes various methods used for transmitting data over a network without it being detected. Most of the methods proposed for hiding data in a network do not offer an additional protection to the covert data as it is sent as plain text. This paper presents a framework that offers the protection to the covert data by encrypting it and compresses it for gain in efficiency.

An attack graph is a method used to enumerate the possible paths that an attacker can take in the organizational network. MulVAL is a known open-source framework used to automatically generate attack graphs. MulVAL's default modeling has two main shortcomings. First, it lacks the ability to represent network protocol vulnerabilities, and thus it cannot be used to model common network attacks, such as ARP poisoning. Second, it does not support advanced types of communication, such as wireless and bus communication, and thus it cannot be used to model cyber-attacks on networks that include IoT devices or industrial components. In this paper, we present an extended network security model for MulVAL that: (1) considers the physical network topology, (2) supports short-range communication protocols, (3) models vulnerabilities in the design of network protocols, and (4) models specific industrial communication architectures. Using the proposed extensions, we were able to model multiple attack techniques including: spoofing, man-in-the-middle, and denial of service attacks, as well as attacks on advanced types of communication. We demonstrate the proposed model in a testbed which implements a simplified network architecture comprised of both IT and industrial components

In the communication model of wired and wireless Adhoc networks, the most needed requirement is the integration of security. Mobile Adhoc networks are more aroused with the attacks compared to the wired environment. Subsequently, the characteristics of Mobile Adhoc networks are also influenced by the vulnerability. The pre-existing unfolding solutions are been obtained for infrastructure-less networks. However, these solutions are not always necessarily suitable for wireless networks. Further, the framework of wireless Adhoc networks has uncommon vulnerabilities and due to this behavior it is not protected by the same solutions, therefore the detection mechanism of intrusion is combinedly used to protect the Manets. Several intrusion detection techniques that have been developed for a fixed wired network cannot be applied in this new environment. Furthermore, The issue of intensity in terms of energy is of a major kind due to which the life of the working battery is very limited. The objective this research work is to detect the Anomalous behavior of nodes in Manet's and Experimental analysis is done by making use of Network Simulator-2 to do the comparative analysis for the existing algorithm, we enhanced the previous algorithm in order to improve the Energy efficiency and results shown the improvement of energy of battery life and Throughput is checked with respect to simulation of test case analysis. In this paper, the proposed algorithm is compared with the existing approach.

The exponential growth of Internet-of-Things (IoT) devices not only brings convenience but also poses numerous challenging safety and security issues. IoT devices are distributed, highly heterogeneous, and more importantly, directly interact with the physical environment. In IoT systems, the bugs in device firmware, the defects in network protocols, and the design flaws in system configurations all may lead to catastrophic accidents, causing severe threats to people's lives and properties. The challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers, rendering the current vulnerability analysis inapplicable. In this paper, we present ForeSee, a cross-layer formal framework to comprehensively unveil the vulnerabilities in IoT systems. ForeSee generates a novel attack graph that depicts all of the essential components in IoT, from low-level physical surroundings to high-level decision-making processes. The corresponding graph-based analysis then enables ForeSee to precisely capture potential attack paths. An optimization algorithm is further introduced to reduce the computational complexity of our analysis. The illustrative case studies show that our multilayer modeling can capture threats ignored by the previous approaches.

The operation and management of network is facing increasing complexities brought by the evolution of network protocols and the demands of rapid service delivery. In this paper, we propose a programmable network management architecture, which manages network based on NETCONF protocol and provides REST APIs to upper layer so that further programming can be done based on the APIs to implement flexible management. Functions of devices can be modeled based on YANG language, and the models can be translated into REST APIs. We apply it to the management of ADN (Address Driven Network), an innovative network architecture proposed by Tsinghua University to inhibit IP spoofing, improve network security and provide high service quality. We model the functions of ADN based on YANG language, and implement the network management functions based on the REST APIs. We deploy and evaluate it in a laboratory environment. Test result shows that the programmable network management architecture is flexible to implement management for new network services.

Blockchain networks which employ Proof-of-Work in their consensus mechanism may face inconsistencies in the form of forks. These forks are usually resolved through the application of block selection rules (such as the Nakamoto consensus). In this paper, we investigate the cause and length of forks for the Bitcoin network. We develop theoretical formulas which model the Bitcoin consensus and network protocols, based on an Erdös-Rényi random graph construction of the overlay network of peers. Our theoretical model addresses the effect of key parameters on the fork occurrence probability, such as block propagation delay, network bandwidth, and block size. We also leverage this model to estimate the weight of fork branches. Our model is implemented using the network simulator OMNET++ and validated by historical Bitcoin data. We show that under current conditions, Bitcoin will not benefit from increasing the number of connections per node.

A covert channel is a communication channel that is subjugated for illegal flow of information in a way that violates system security policies. It is a dangerous, invisible, undetectable, and developed security attack. Recently, Packet length covert channel has motivated many researchers as it is a one of the most undetectable network covert channels. Packet length covert channel generates a covert traffic that is very similar to normal terrific which complicates the detection of such type of covert channels. This motivates us to introduce a machine learning based detection scheme. Recently, a machine learning approach has proved its capability in many different fields especially in security field as it usually brings up a reliable and realistic results. Based in our developed content and frequency-based features, the developed detection scheme has been fully trained and tested. Our detection scheme has gained an excellent degree of detection accuracy which reaches 98% (zero false negative rate and 0.02 false positive rate).

The growing prevalence of cyber threats in the world are affecting every network user. Numerous security monitoring systems are being employed to protect computer networks and resources from falling victim to cyber-attacks. There is a pressing need to have an efficient security monitoring system to monitor the large network datasets generated in this process. A large network datasets representing Malware attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides to the weight of each attribute and forms a scoring system by annotating the log history. We adopted a special semi supervise method to classify cyber security log into attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally train the neural network classifier multilayer perceptron (MLP) base on the manually labelled data. By doing so, our results is very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection. The method of including Artificial Intelligence (AI) and Analyst Intuition (AI) is also known as AI2. The classification results are encouraging in segregating the types of attacks.

Hidden Markov Models (HMM) are probabilistic models that can be used for forecasting time series data. It has seen success in various domains like finance [1-5], bioinformatics [6-8], healthcare [9-11], agriculture [12-14], artificial intelligence[15-17]. However, the use of HMM in cyber security found to date is numbered. We believe the properties of HMM being predictive, probabilistic, and its ability to model different naturally occurring states form a good basis to model cyber security data. It is hence the motivation of this work to provide the initial results of our attempts to predict security attacks using HMM. A large network datasets representing cyber security attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides the weight of each attribute and forms a scoring system by annotating the log history. We applied HMM to distinguish between a cyber security attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally use HMM state-based approach. By doing so, our results are very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection.

Subliminal channels in digital signatures provide a very effective method to clandestinely leak information from inside a system to a third party outside. Information can be hidden in signature parameters in a way that both network operators and legitimate receivers would not notice any suspicious traces. Subliminal channels have previously been discovered in other signatures, such as ElGamal and ECDSA. Those signatures are usually just sparsely exchanged in network protocols, e.g. during authentication, and their usability for leaking information is therefore limited. With the advent of high-speed signatures such as EdDSA, however, scenarios become feasible where numerous packets with individual signatures are transferred between communicating parties. This significantly increases the bandwidth for transmitting subliminal information. Examples are broadcast clock synchronization or signed sensor data export. A subliminal channel in signatures appended to numerous packets allows the transmission of a high amount of hidden information, suitable for large scale data exfiltration or even the operation of command and control structures. In this paper, we show the existence of a broadband subliminal channel in the EdDSA signature scheme. We then discuss the implications of the subliminal channel in practice using thee different scenarios: broadcast clock synchronization, signed sensor data export, and classic TLS. We perform several experiments to show the use of the subliminal channel and measure the actual bandwidth of the subliminal information that can be leaked. We then discuss the applicability of different countermeasures against subliminal channels from other signature schemes to EdDSA but conclude that none of the existing solutions can sufficiently protect against data exfiltration in network protocols secured by EdDSA.

We present and explore a model of stateless and self-stabilizing distributed computation, inspired by real-world applications such as routing on today's Internet. Processors in our model do not have an internal state, but rather interact by repeatedly mapping incoming messages ("labels") to outgoing messages and output values. While seemingly too restrictive to be of interest, stateless computation encompasses both classical game-theoretic notions of strategic interaction and a broad range of practical applications (e.g., Internet protocols, circuits, diffusion of technologies in social networks). Our main technical contribution is a general impossibility result for stateless self-stabilization in our model, showing that even modest asynchrony (with wait times that are linear in the number of processors) can prevent a stateless protocol from reaching a stable global configuration. Furthermore, we present hardness results for verifying stateless self-stabilization. We also address several aspects of the computational power of stateless protocols. Most significantly, we show that short messages (of length that is logarithmic in the number of processors) yield substantial computational power, even on very poorly connected topologies.

Underwater acoustic networks is an enabling technology for a range of applications such as mine countermeasures, intelligence and reconnaissance. Common for these applications is a need for robust information distribution while minimizing energy consumption. In terrestrial wireless networks topology information is often used to enhance the efficiency of routing, in terms of higher capacity and less overhead. In this paper we asses the effects of topology information on routing in underwater acoustic networks. More specifically, the interplay between long propagation delays, contention-based channels access and dissemination of varying degrees of topology information is investigated. The study is based on network simulations of a number of network protocols that make use of varying amounts of topology information. The results indicate that, in the considered scenario, relying on local topology information to reduce retransmissions may have adverse effects on the reliability. The difficult channel conditions and the contention-based channels access methods create a need for an increased amount of diversity, i.e., more retransmissions. In the scenario considered, an opportunistic flooding approach is a better, both in terms of robustness and energy consumption.

Traffic from mobile wireless networks has been growing at a fast pace in recent years and is expected to surpass wired traffic very soon. Service providers face significant challenges at such scales including providing seamless mobility, efficient data delivery, security, and provisioning capacity at the wireless edge. In the Mobility First project, we have been exploring clean slate enhancements to the network protocols that can inherently provide support for at-scale mobility and trustworthiness in the Internet. An extensible data plane using pluggable compute-layer services is a key component of this architecture. We believe these extensions can be used to implement in-network services to enhance mobile end-user experience by either off-loading work and/or traffic from mobile devices, or by enabling en-route service-adaptation through context-awareness (e.g., Knowing contemporary access bandwidth). In this work we present details of the architectural support for in-network services within Mobility First, and propose protocol and service-API extensions to flexibly address these pluggable services from end-points. As a demonstrative example, we implement an in network service that does rate adaptation when delivering video streams to mobile devices that experience variable connection quality. We present details of our deployment and evaluation of the non-IP protocols along with compute-layer extensions on the GENI test bed, where we used a set of programmable nodes across 7 distributed sites to configure a Mobility First network with hosts, routers, and in-network compute services.