Biblio

In the cyber crime huge log data, transactional data occurs which tends to plenty of data for storage and analyze them. It is difficult for forensic investigators to play plenty of time to find out clue and analyze those data. In network forensic analysis involves network traces and detection of attacks. The trace involves an Intrusion Detection System and firewall logs, logs generated by network services and applications, packet captures by sniffers. In network lots of data is generated in every event of action, so it is difficult for forensic investigators to find out clue and analyzing those data. In network forensics is deals with analysis, monitoring, capturing, recording, and analysis of network traffic for detecting intrusions and investigating them. This paper focuses on data collection from the cyber system and web browser. The FTK 4.0 is discussing for memory forensic analysis and remote system forensic which is to be used as evidence for aiding investigation.
 

The EIIM model for ER allows for creation and maintenance of persistent entity identity structures. It accomplishes this through a collection of batch configurations that allow updates and asserted fixes to be made to the Identity knowledgebase (IKB). The model also provides a batch IR configuration that provides no maintenance activity but instead allows access to the identity information. This batch IR configuration is limited in a few ways. It is driven by the same rules used for maintaining the IKB, has no inherent method to identity "close" matches, and can only identify and return the positive matches. Through the decoupling of this configuration and its movements into an interactive role under the umbrella of an Identity Management Service, a more robust access method can be provided for the use of identity information. This more robust access to the information improved the quality of the information along multiple Information Quality dimensions.

Rapid advances in wireless ad hoc networks lead to increase their applications in real life. Since wireless ad hoc networks have no centralized infrastructure and management, they are vulnerable to several security threats. Malicious packet dropping is a serious attack against these networks. In this attack, an adversary node tries to drop all or partial received packets instead of forwarding them to the next hop through the path. A dangerous type of this attack is called black hole. In this attack, after absorbing network traffic by the malicious node, it drops all received packets to form a denial of service (DOS) attack. In this paper, a dynamic trust model to defend network against this attack is proposed. In this approach, a node trusts all immediate neighbors initially. Getting feedback from neighbors' behaviors, a node updates the corresponding trust value. The simulation results by NS-2 show that the attack is detected successfully with low false positive probability.

The need for increased surveillance due to increase in flight volume in remote or oceanic regions outside the range of traditional radar coverage has been fulfilled by the advent of space-based Automatic Dependent Surveillance — Broadcast (ADS-B) Surveillance systems. ADS-B systems have the capability of providing air traffic controllers with highly accurate real-time flight data. ADS-B is dependent on digital communications between aircraft and ground stations of the air route traffic control center (ARTCC); however these communications are not secured. Anyone with the appropriate capabilities and equipment can interrogate the signal and transmit their own false data; this is known as spoofing. The possibility of this type of attacks decreases the situational awareness of United States airspace. The purpose of this project is to design a secure transmission framework that prevents ADS-B signals from being spoofed. Three alternative methods of securing ADS-B signals are evaluated: hashing, symmetric encryption, and asymmetric encryption. Security strength of the design alternatives is determined from research. Feasibility criteria are determined by comparative analysis of alternatives. Economic implications and possible collision risk is determined from simulations that model the United State airspace over the Gulf of Mexico and part of the airspace under attack respectively. The ultimate goal of the project is to show that if ADS-B signals can be secured, the situational awareness can improve and the ARTCC can use information from this surveillance system to decrease the separation between aircraft and ultimately maximize the use of the United States airspace.

Artificial monitoring is no longer able to match the rapid growth of cybercrime, it is in great need to develop a new spatial analysis technology which allows emergency events to get rapidly and accurately locked in real environment, furthermore, to establish correlative analysis model for cybercrime prevention strategy. On the other hand, Geography information system has been changed virtually in data structure, coordinate system and analysis model due to the “uncertainty and hyper-dimension” characteristics of network object and behavior. In this paper, the spatial rules of typical cybercrime are explored on base of GIS with Internet searching and IP tracking technology: (1) Setup spatial database through IP searching based on criminal evidence. (2)Extend GIS data-structure and spatial models, add network dimension and virtual attribution to realize dynamic connection between cyber and real space. (3)Design cybercrime monitoring and prevention system to discover the cyberspace logics based on spatial analysis.
 

Memristors are an attractive option for use in future memory architectures due to their non-volatility, high density and low power operation. Notwithstanding these advantages, memristors and memristor-based memories are prone to high defect densities due to the non-deterministic nature of nanoscale fabrication. The typical approach to fault detection and diagnosis in memories entails testing one memory cell at a time. This is time consuming and does not scale for the dense, memristor-based memories. In this paper, we integrate solutions for detecting and locating faults in memristors, and ensure post-silicon recovery from memristor failures. We propose a hybrid diagnosis scheme that exploits sneak-paths inherent in crossbar memories, and uses March testing to test and diagnose multiple memory cells simultaneously, thereby reducing test time. We also provide a repair mechanism that prevents faults in the memory from being activated. The proposed schemes enable and leverage sneak paths during fault detection and diagnosis modes, while still maintaining a sneak-path free crossbar during normal operation. The proposed hybrid scheme reduces fault detection and diagnosis time by ~44%, compared to traditional March tests, and repairs the faulty cell with minimal overhead.
 

Since the past 20 years the uses of web in daily life is increasing and becoming trend now. As the use of the web is increasing, the use of web application is also increasing. Apparently most of the web application exists up to today have some vulnerability that could be exploited by unauthorized person. Some of well-known web application vulnerabilities are Structured Query Language (SQL) Injection, Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF). By compromising with these web application vulnerabilities, the system cracker can gain information about the user and lead to the reputation of the respective organization. Usually the developers of web applications did not realize that their web applications have vulnerabilities. They only realize them when there is an attack or manipulation of their code by someone. This is normal as in a web application, there are thousands of lines of code, therefore it is not easy to detect if there are some loopholes. Nowadays as the hacking tools and hacking tutorials are easier to get, lots of new hackers are born. Even though SQL injection is very easy to protect against, there are still large numbers of the system on the internet are vulnerable to this type of attack because there will be a few subtle condition that can go undetected. Therefore, in this paper we propose a detection model for detecting and recognizing the web vulnerability which is; SQL Injection based on the defined and identified criteria. In addition, the proposed detection model will be able to generate a report regarding the vulnerability level of the web application. As the consequence, the proposed detection model should be able to decrease the possibility of the SQL Injection attack that can be launch onto the web application.

Deadlock freedom is a key challenge in the design of communication networks. Wormhole switching is a popular switching technique, which is also prone to deadlocks. Deadlock analysis of routing functions is a manual and complex task. We propose an algorithm that automatically proves routing functions deadlock-free or outputs a minimal counter-example explaining the source of the deadlock. Our algorithm is the first to automatically check a necessary and sufficient condition for deadlock-free routing. We illustrate its efficiency in a complex adaptive routing function for torus topologies. Results are encouraging. Deciding deadlock freedom is co-NP-Complete for wormhole networks. Nevertheless, our tool proves a 13 × 13 torus deadlock-free within seconds. Finding minimal deadlocks is more difficult. Our tool needs four minutes to find a minimal deadlock in a 11 × 11 torus while it needs nine hours for a 12 × 12 network.

Nearest neighbor search is a fundamental problem in various research fields like machine learning, data mining and pattern recognition. Recently, hashing-based approaches, for example, locality sensitive hashing (LSH), are proved to be effective for scalable high dimensional nearest neighbor search. Many hashing algorithms found their theoretic root in random projection. Since these algorithms generate the hash tables (projections) randomly, a large number of hash tables (i.e., long codewords) are required in order to achieve both high precision and recall. To address this limitation, we propose a novel hashing algorithm called density sensitive hashing (DSH) in this paper. DSH can be regarded as an extension of LSH. By exploring the geometric structure of the data, DSH avoids the purely random projections selection and uses those projective functions which best agree with the distribution of the data. Extensive experimental results on real-world data sets have shown that the proposed method achieves better performance compared to the state-of-the-art hashing approaches.

Nowadays, the design of a secure access authentication protocol in heterogeneous networks achieving seamless roaming across radio access technologies for mobile users (MUs) is a major technical challenge. This paper proposes a Distributed Anonymous Authentication (DAA) protocol to resolve the problems of heavy signaling overheads and long signaling delay when authentication is executed in a centralized manner. By applying MUs and point of attachments (PoAs) as group members, the adopted group signature algorithms provide identity verification directly without sharing secrets in advance, which significantly reduces signaling overheads. Moreover, MUs sign messages on behalf of the group, so that anonymity and unlinkability against PoAs are provided and thus privacy is preserved. Performance analysis confirm the advantages of DAA over existing solutions.

Smart Grid is the trend of next generation power distribution and network management that enable a two -- way interactive communication and operation between consumers and suppliers, so as to achieve intelligent resource management and optimization. The wireless mesh network technology is a promising infrastructure solution to support these smart functionalities, while it has some inherent vulnerabilities and cyber-attack risks to be addressed. As Smart Grid is heavily relying on the underlie communication networks, which makes their security and dependability issues critical to the entire smart grid technology. Several studies have been conducted in the field of Smart Grid security, but few works were focused on the dependability and its associated resource analysis of the control center networks. In this paper, we have investigated the dependability modeling and also resource allocation in redundant communication networks by adopting two mathematical approaches, Reliability Block Diagrams (RBD) and Stochastic Petri Nets (SPNs), to analyze the dependability of control center networks in Smart Grid environment. We have applied our proposed modeling approach in an extensive case study to evaluate the availability of smart gird networks with different redundancy mechanisms. A combination of dependability models and reliability importance are used to analyze the network availability according to the most important components. We also show the variation of network availability in accordance with Mean Time to Failure (MTTF) in different network architectures.

By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of smart-grid systems, with techniques such as denial-of-service (DoS) attack, random attack, and data-injection attack. In this paper, we present a mathematical model of the system to study these pitfalls and propose a robust security framework for the smart grid. Our framework adopts the Kalman filter to estimate the variables of a wide range of state processes in the model. The estimates from the Kalman filter and the system readings are then fed into the χ2-detector or the proposed Euclidean detector. The χ2-detector is a proven effective exploratory method used with the Kalman filter for the measurement of the relationship between dependent variables and a series of predictor variables. The χ2-detector can detect system faults/attacks, such as DoS attack, short-term, and long-term random attacks. However, the studies show that the χ2-detector is unable to detect the statistically derived false data-injection attack. To overcome this limitation, we prove that the Euclidean detector can effectively detect such a sophisticated injection attack.

IP spoofing based DDoS attack that relies on multiple compromised hosts in the network to attack the victim. In IP spoofing, IP addresses can be forged easily, thus, makes it difficult to filter illegitimate packets from legitimate one out of aggregated traffic. A number of mitigation techniques have been proposed in the literature by various researchers. The conventional Hop Count Filtering or probabilistic Hop Count Filtering based research work indicates the problems related to higher computational time and low detection rate of illegitimate packets. In this paper, DPHCF-RTT technique has been implemented and analysed for variable number of hops. Goal is to improve the limitations of Conventional HCF or Probabilistic HCF techniques by maximizing the detection rate of illegitimate packets and reducing the computation time. It is based on distributed probabilistic HCF using RTT. It has been used in an intermediate system. It has the advantage for resolving the problems of network bandwidth jam and host resources exhaustion. MATLAB 7 has been used for simulations. Mitigation of DDoS attacks have been done through DPHCF-RTT technique. It has been shown a maximum detection rate up to 99% of malicious packets.

Botnet is one of the most widespread and serious malware which occur frequently in today's cyber attacks. A botnet is a group of Internet-connected computer programs communicating with other similar programs in order to perform various attacks. HTTP-based botnet is most dangerous botnet among all the different botnets available today. In botnets detection, in particularly, behavioural-based approaches suffer from the unavailability of the benchmark datasets and this lead to lack of precise results evaluation of botnet detection systems, comparison, and deployment which originates from the deficiency of adequate datasets. Most of the datasets in the botnet field are from local environment and cannot be used in the large scale due to privacy problems and do not reflect common trends, and also lack some statistical features. To the best of our knowledge, there is not any benchmark dataset available which is infected by HTTP-based botnet (HBB) for performing Distributed Denial of Service (DDoS) attacks against Web servers by using HTTP-GET flooding method. In addition, there is no Web access log infected by botnet is available for researchers. Therefore, in this paper, a complete test-bed will be illustrated in order to implement a real time HTTP-based botnet for performing variety of DDoS attacks against Web servers by using HTTP-GET flooding method. In addition to this, Web access log with http bot traces are also generated. These real time datasets and Web access logs can be useful to study the behaviour of HTTP-based botnet as well as to evaluate different solutions proposed to detect HTTP-based botnet by various researchers.
 

This paper proposes a new network-based cyber intrusion detection system (NIDS) using multicast messages in substation automation systems (SASs). The proposed network-based intrusion detection system monitors anomalies and malicious activities of multicast messages based on IEC 61850, e.g., Generic Object Oriented Substation Event (GOOSE) and Sampled Value (SV). NIDS detects anomalies and intrusions that violate predefined security rules using a specification-based algorithm. The performance test has been conducted for different cyber intrusion scenarios (e.g., packet modification, replay and denial-of-service attacks) using a cyber security testbed. The IEEE 39-bus system model has been used for testing of the proposed intrusion detection method for simultaneous cyber attacks. The false negative ratio (FNR) is the number of misclassified abnormal packets divided by the total number of abnormal packets. The results demonstrate that the proposed NIDS achieves a low fault negative rate.
 

Data mining is the process of finding correlations in the relational databases. There are different techniques for identifying malicious database transactions. Many existing approaches which profile is SQL query structures and database user activities to detect intrusion, the log mining approach is the automatic discovery for identifying anomalous database transactions. Mining of the Data is very helpful to end users for extracting useful business information from large database. Multi-level and multi-dimensional data mining are employed to discover data item dependency rules, data sequence rules, domain dependency rules, and domain sequence rules from the database log containing legitimate transactions. Database transactions that do not comply with the rules are identified as malicious transactions. The log mining approach can achieve desired true and false positive rates when the confidence and support are set up appropriately. The implemented system incrementally maintain the data dependency rule sets and optimize the performance of the intrusion detection process.
 

Communicating vehicles will change road traffic as we know it. With current versions of European and US standards in mind, the authors discuss privacy and traffic surveillance issues in vehicular network technology and outline research directions that could address these issues.

Datacenter-based Cloud computing has induced new disruptive trends in networking, key among which is network virtualization. Software-Defined Networking overlays aim to improve the efficiency of the next generation multitenant datacenters. While early overlay prototypes are already available, they focus mainly on core functionality, with little being known yet about their impact on the system level performance. Using query completion time as our primary performance metric, we evaluate the overlay network impact on two representative datacenter workloads, Partition/Aggregate and 3-Tier. We measure how much performance is traded for overlay's benefits in manageability, security and policing. Finally, we aim to assist the datacenter architects by providing a detailed evaluation of the key overlay choices, all made possible by our accurate cross-layer hybrid/mesoscale simulation platform.
 

We propose a novel phishing detection architecture based on transparent virtualization technologies and isolation of the own components. The architecture can be deployed as a security extension for virtual machines (VMs) running in the cloud. It uses fine-grained VM introspection (VMI) to extract, filter and scale a color-based fingerprint of web pages which are processed by a browser from the VM's memory. By analyzing the human perceptual similarity between the fingerprints, the architecture can reveal and mitigate phishing attacks which are based on redirection to spoofed web pages and it can also detect “Man-in-the-Browser” (MitB) attacks. To the best of our knowledge, the architecture is the first anti-phishing solution leveraging virtualization technologies. We explain details about the design and the implementation and we show results of an evaluation with real-world data.

This paper designs a secure transmission and authorization management system which based on the principles of Public Key Infrastructure and Rose-Based Access Control. It can solve the problems of identity authentication, secure transmission and access control on internet. In the first place, according to PKI principles, certificate authority system is implemented. It can issue and revoke the server-side and client-side digital certificate. Data secure transmission is achieved through the combination of digital certificate and SSL protocol. In addition, this paper analyses access control mechanism and RBAC model. The structure of RBAC model has been improved. The principle of group authority is added into the model and the combination of centralized authority and distributed authority management is adopted, so the model becomes more flexible.
 

In this paper, we consider distributed algorithm based on a continuous-time multi-agent system to solve constrained optimization problem. The global optimization objective function is taken as the sum of agents' individual objective functions under a group of convex inequality function constraints. Because the local objective functions cannot be explicitly known by all the agents, the problem has to be solved in a distributed manner with the cooperation between agents. Here we propose a continuous-time distributed gradient dynamics based on the KKT condition and Lagrangian multiplier methods to solve the optimization problem. We show that all the agents asymptotically converge to the same optimal solution with the help of a constructed Lyapunov function and a LaSalle invariance principle of hybrid systems.

Distributed optimization is an emerging research topic. Agents in the network solve the problem by exchanging information which depicts people's consideration on a optimization problem in real lives. In this paper, we introduce two algorithms in continuous-time to solve distributed optimization problems with equality constraints where the cost function is expressed as a sum of functions and where each function is associated to an agent. We firstly construct a continuous dynamic system by utilizing the Lagrangian function and then show that the algorithm is locally convergent and globally stable under certain conditions. Then, we modify the Lagrangian function and re-construct the dynamic system to prove that the new algorithm will be convergent under more relaxed conditions. At last, we present some simulations to prove our theoretical results.

The electric network frequency (ENF) criterion is a recently developed technique for audio timestamp identification, which involves the matching between extracted ENF signal and reference data. For nearly a decade, conventional matching criterion has been based on the minimum mean squared error (MMSE) or maximum correlation coefficient. However, the corresponding performance is highly limited by low signal-to-noise ratio, short recording durations, frequency resolution problems, and so on. This paper presents a threshold-based dynamic matching algorithm (DMA), which is capable of autocorrecting the noise affected frequency estimates. The threshold is chosen according to the frequency resolution determined by the short-time Fourier transform (STFT) window size. A penalty coefficient is introduced to monitor the autocorrection process and finally determine the estimated timestamp. It is then shown that the DMA generalizes the conventional MMSE method. By considering the mainlobe width in the STFT caused by limited frequency resolution, the DMA achieves improved identification accuracy and robustness against higher levels of noise and the offset problem. Synthetic performance analysis and practical experimental results are provided to illustrate the advantages of the DMA.