Biblio
With the advent of smart devices and lowering prices of sensing devices, adoption of Internet of Things (IoT) is gaining momentum. These IoT devices come with greater threat of being attacked or compromised that could lead to Denial of Service (DoS) and Distributed Denial of Service (DDoS). The high volume of IoT devices with high level of heterogeneity, magnify the possibility of security threats. So far, there is no protocol to guarantee the security of IoT devices. But to enable resilience, continuous monitoring is required along with adaptive decision making. These challenges can be addressed with the help of Software Defined Networking (SDN) which can effectively handle the security threats to the IoT devices in dynamic and adaptive manner without any burden on the IoT devices. In this paper, we propose an SDN-based secure IoT framework called SoftThings to detect abnormal behaviors and attacks as early as possible and mitigate as appropriate. Machine Learning is used at the SDN controller to monitor and learn the behavior of IoT devices over time. We have conducted experiments on Mininet emulator. Initial results show that this framework is capable to detect attacks on IoT with around 98% precision.
With the development of the information and communications technology, new network architecture and applications keep emerging promoted by cloud computing, big data, virtualization technology, etc. As a novel network architecture, Software Defined Network (SDN) realizes separation of the control plane and the data plane, thus controlling hardware by a software platform which is known as the central controller. Through that method SDN realizes the flexible deployment of network resources. In the process of the development and application of SDN, its open architecture has exposed more and more security problem, which triggers a critical focus on how to build a secure SDN. Based on the hierarchical SDN architecture and characteristics, this paper analyzes the security threats that SDN may face in the application layer, the control layer, the resource layer and the interface layer. In order to solve those security threats, the paper presents an SDN security architecture which can provide corresponding defense ability. The paper also puts forward an enhanced access control strategy adopting an attribute-based encryption method in the SDN security architecture.
The majority of business activity of our integrated and connected world takes place in networks based on cloud computing infrastructure that cross national, geographic and jurisdictional boundaries. Such an efficient entity interconnection is made possible through an emerging networking paradigm, Software Defined Networking (SDN) that intends to vastly simplify policy enforcement and network reconfiguration in a dynamic manner. However, despite the obvious advantages this novel networking paradigm introduces, its increased attack surface compared to traditional networking deployments proved to be a thorny issue that creates skepticism when safety-critical applications are considered. Especially when SDN is used to support Internet-of-Things (IoT)-related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. The overall number of connected nodes makes the efficient monitoring of all entities a real challenge, that must be tackled to prevent system degradation and service outage. This position paper provides an overview of common security issues of SDN when linked to IoT clouds, describes the design principals of the recently introduced Blockchain paradigm and advocates the reasons that render Blockchain as a significant security factor for solutions where SDN and IoT are involved.
The majority of business activity of our integrated and connected world takes place in networks based on cloud computing infrastructure that cross national, geographic and jurisdictional boundaries. Such an efficient entity interconnection is made possible through an emerging networking paradigm, Software Defined Networking (SDN) that intends to vastly simplify policy enforcement and network reconfiguration in a dynamic manner. However, despite the obvious advantages this novel networking paradigm introduces, its increased attack surface compared to traditional networking deployments proved to be a thorny issue that creates skepticism when safety-critical applications are considered. Especially when SDN is used to support Internet-of-Things (IoT)-related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. The overall number of connected nodes makes the efficient monitoring of all entities a real challenge, that must be tackled to prevent system degradation and service outage. This position paper provides an overview of common security issues of SDN when linked to IoT clouds, describes the design principals of the recently introduced Blockchain paradigm and advocates the reasons that render Blockchain as a significant security factor for solutions where SDN and IoT are involved.
Machine learning and data mining algorithms typically assume that the training and testing data are sampled from the same fixed probability distribution; however, this violation is often violated in practice. The field of domain adaptation addresses the situation where this assumption of a fixed probability between the two domains is violated; however, the difference between the two domains (training/source and testing/target) may not be known a priori. There has been a recent thrust in addressing the problem of learning in the presence of an adversary, which we formulate as a problem of domain adaption to build a more robust classifier. This is because the overall security of classifiers and their preprocessing stages have been called into question with the recent findings of adversaries in a learning setting. Adversarial training (and testing) data pose a serious threat to scenarios where an attacker has the opportunity to ``poison'' the training or ``evade'' on the testing data set(s) in order to achieve something that is not in the best interest of the classifier. Recent work has begun to show the impact of adversarial data on several classifiers; however, the impact of the adversary on aspects related to preprocessing of data (i.e., dimensionality reduction or feature selection) has widely been ignored in the revamp of adversarial learning research. Furthermore, variable selection, which is a vital component to any data analysis, has been shown to be particularly susceptible under an attacker that has knowledge of the task. In this work, we explore avenues for learning resilient classification models in the adversarial learning setting by considering the effects of adversarial data and how to mitigate its effects through optimization. Our model forms a single convex optimization problem that uses the labeled training data from the source domain and known- weaknesses of the model for an adversarial component. We benchmark the proposed approach on synthetic data and show the trade-off between classification accuracy and skew-insensitive statistics.
The most of the organizations tend to accumulate the data related to security, which goes up-to terabytes in every month. They collect this data to meet the security requirements. The data is mostly in the shape of logs like Dns logs, Pcap files, and Firewall data etc. The data can be related to any communication network like cloud, telecom, or smart grid network. Generally, these logs are stored in databases or warehouses which becomes ultimately gigantic in size. Such a huge size of data upsurge the importance of security analytics in big data. In surveys, the security experts grumble about the existing tools and recommend for special tools and methods for big data security analysis. In this paper, we are using a big data analysis tool, which is known as apache spark. Although this tool is used for general purpose but we have used this for security analysis. It offers a very good library for machine learning algorithms including the clustering which is the main algorithm used in our work. In this work, we have developed a novel model, which combines rule based and clustering analysis for security analysis of big dataset. The dataset we are using in our experiment is the Kddcup99 which is a widely used dataset for intrusion detection. It is of MBs in size but can be used as a test case for big data security analysis.
Traditional information Security Risk Assessment algorithms are mainly used for evaluating small scale of information system, not suitable for massive information systems in Energy Internet. To solve the problem, this paper proposes an Information Security Risk Algorithm based on Dynamic Risk Propagation (ISRADRP). ISRADRP firstly divides information systems in the Energy Internet into different partitions according to their logical network location. Then, ISRADRP computes each partition's risk value without considering threat propagation effect via RM algorithm. Furthermore, ISRADRP calculates inside and outside propagation risk value for each partition according to Dependency Structure Matrix. Finally, the security bottleneck of systems will be identified and the overall risk value of information system will be obtained.
Code signing which at present is the only methodology of trusting a code that is distributed to others. It heavily relies on the security of the software providers private key. Attackers employ targeted attacks on the code signing infrastructure for stealing the signing keys which are used later for distributing malware in disguise of genuine software. Differentiating a malware from a benign software becomes extremely difficult once it gets signed by a trusted software providers private key as the operating systems implicitly trusts this signed code. In this paper, we analyze the growing menace of signed malware by examining several real world incidents and present a threat model for the current code signing infrastructure. We also propose a novel solution that prevents this issue of malicious code signing by requiring additional verification of the executable. We also present the serious threat it poses and it consequences. To our knowledge this is the first time this specific issue of Malicious code signing has been thoroughly studied and an implementable solution is proposed.
Anti-virus vendors receive hundreds of thousands of malware to be analysed each day. Some are new malware while others are variations or evolutions of existing malware. Because analyzing each malware sample by hand is impossible, automated techniques to analyse and categorize incoming samples are needed. In this work, we explore various machine learning features extracted from malware samples through static analysis for classification of malware binaries into already known malware families. We present a new feature based on control statement shingling that has a comparable accuracy to ordinary opcode n-gram based features while requiring smaller dimensions. This, in turn, results in a shorter training time.
In recent years the use of wireless ad hoc networks has seen an increase of applications. A big part of the research has focused on Mobile Ad Hoc Networks (MAnETs), due to its implementations in vehicular networks, battlefield communications, among others. These peer-to-peer networks usually test novel communications protocols, but leave out the network security part. A wide range of attacks can happen as in wired networks, some of them being more damaging in MANETs. Because of the characteristics of these networks, conventional methods for detection of attack traffic are ineffective. Intrusion Detection Systems (IDSs) are constructed on various detection techniques, but one of the most important is anomaly detection. IDSs based only in past attacks signatures are less effective, even more if these IDSs are centralized. Our work focuses on adding a novel Machine Learning technique to the detection engine, which recognizes attack traffic in an online way (not to store and analyze after), re-writing IDS rules on the fly. Experiments were done using the Dockemu emulation tool with Linux Containers, IPv6 and OLSR as routing protocol, leading to promising results.
One of the key objectives of distributed denial of service (DDoS) attack on the smart grid advanced metering infrastructure is to threaten the availability of end user's metering data. This will surely disrupt the smooth operations of the grid and third party operators who need this data for billing and other grid control purposes. In previous work, we proposed a cloud-based Openflow firewall for mitigation against DDoS attack in a smart grid AMI. In this paper, PRISM model checker is used to perform a probabilistic best-and worst-case analysis of the firewall with regard to DDoS attack success under different firewall detection probabilities ranging from zero to 1. The results from this quantitative analysis can be useful in determining the extent the DDoS attack can undermine the correctness and performance of the firewall. In addition, the study can also be helpful in knowing the extent the firewall can be improved by applying the knowledge derived from the worst-case performance of the firewall.
Complex systems are prevalent in many fields such as finance, security and industry. A fundamental problem in system management is to perform diagnosis in case of system failure such that the causal anomalies, i.e., root causes, can be identified for system debugging and repair. Recently, invariant network has proven a powerful tool in characterizing complex system behaviors. In an invariant network, a node represents a system component, and an edge indicates a stable interaction between two components. Recent approaches have shown that by modeling fault propagation in the invariant network, causal anomalies can be effectively discovered. Despite their success, the existing methods have a major limitation: they typically assume there is only a single and global fault propagation in the entire network. However, in real-world large-scale complex systems, it's more common for multiple fault propagations to grow simultaneously and locally within different node clusters and jointly define the system failure status. Inspired by this key observation, we propose a two-phase framework to identify and rank causal anomalies. In the first phase, a probabilistic clustering is performed to uncover impaired node clusters in the invariant network. Then, in the second phase, a low-rank network diffusion model is designed to backtrack causal anomalies in different impaired clusters. Extensive experimental results on real-life datasets demonstrate the effectiveness of our method.
In Energy Internet mode, a large number of alarm information is generated when equipment exception and multiple faults in large power grid, which seriously affects the information collection, fault analysis and delays the accident treatment for the monitors. To this point, this paper proposed a method for power grid monitoring to monitor and diagnose fault in real time, constructed the equipment fault logical model based on five section alarm information, built the standard fault information set, realized fault information optimization, fault equipment location, fault type diagnosis, false-report message and missing-report message analysis using matching algorithm. The validity and practicality of the proposed method by an actual case was verified, which can shorten the time of obtaining and analyzing fault information, accelerate the progress of accident treatment, ensure the safe and stable operation of power grid.
Software Defined Networks (SDNs) is a new networking paradigm that has gained a lot of attention in recent years especially in implementing data center networks and in providing efficient security solutions. The popularity of SDN and its attractive security features suggest that it can be used in the context of smart grid systems to address many of the vulnerabilities and security problems facing such critical infrastructure systems. This paper studies the impact of different cyber attacks that can target smart grid communication network which is implemented as a software defined network on the operation of the smart grid system in general. In particular, we perform different attack scenarios including DDoS attacks, location highjacking and link overloading against SDN networks of different controller types that include POX, Floodlight and RYU. Our experiments were carried out using the mininet simulator. The experiments show that SDN-enabled smartgrid systems are vulnerable to different types of attacks.
Nowadays, robots are widely ubiquitous and integral part in our daily lives, which can be seen almost everywhere in industry, hospitals, military, etc. To provide remote access and control, usually robots are connected to local network or to the Internet through WiFi or Ethernet. As such, it is of great importance and of a critical mission to maintain the safety and the security access of such robots. Security threats may result in completely preventing the access and control of the robot. The consequences of this may be catastrophic and may cause an immediate physical damage to the robot. This paper aims to present a security risk assessment of the well-known PeopleBot; a mobile robot platform from Adept MobileRobots Company. Initially, we thoroughly examined security threats related to remote accessing the PeopleBot robot. We conducted an impact-oriented analysis approach on the wireless communication medium; the main method considered to remotely access the PeopleBot robot. Numerous experiments using SSH and server-client applications were conducted, and they demonstrated that certain attacks result in denying remote access service to the PeopleBot robot. Consequently and dangerously the robot becomes unavailable. Finally, we suggested one possible mitigation and provided useful conclusions to raise awareness of possible security threats on the robotic systems; especially when the robots are involved in critical missions or applications.
to appear
This paper introduces SONA (Spatiotemporal system Organized for Natural Analysis), a tabletop and tangible controller system for exploring geotagged information, and more specifically, CCTV. SONA's goal is to support a more natural method of interacting with data. Our new interactions are placed in the context of a physical security environment, closed circuit television (CCTV). We present a three-layered detail on demand set of view filters for CCTV feeds on a digital map. These filters are controlled with a novel tangible device for direct interaction. We validate SONA's tangible controller approach with a user study comparing SONA with the existing CCTV multi-screen method. The results of the study show that SONA's tangible interaction method is superior to the multi-screen approach, both in terms of quantitative results, and is preferred by users.
The assessment of networks is frequently accomplished by using time-consuming analysis tools based on simulations. For example, the blocking probability of networks can be estimated by Monte Carlo simulations and the network resilience can be assessed by link or node failure simulations. We propose in this paper to use Artificial Neural Networks (ANN) to predict the robustness of networks based on simple topological metrics to avoid time-consuming failure simulations. We accomplish the training process using supervised learning based on a historical database of networks. We compare the results of our proposal with the outcome provided by targeted and random failures simulations. We show that our approach is faster than failure simulators and the ANN can mimic the same robustness evaluation provide by these simulators. We obtained an average speedup of 300 times.