Biblio

Continuous development of Network-on-Chip (NoC) enables different types of applications to run efficiently in a Multiprocessor System-on-Chip (MP-SoC). Guaranteed service (GS) can be provided by circuit switching NoC and Best effort service (BES) can be provided by packet switching NoC. A hybrid NoC containing both packet and circuit switching, can provide both types of services to these different applications. But these different applications can be of different security levels and one application can interfere another application's timing characteristics during network transmission. Using this interference, a malicious application can extract secret information from higher security level flows (timing side channel) or two applications can communicate covertly violating the system's security policy (covert timing channel). We propose different mechanisms to protect hybrid routers from timing channel attacks. For design space exploration, we propose three timing channel secure hybrid routers viz. Separate Hybrid (SH), Combined with Separate interface Hybrid (CSH), and Combined Hybrid (CH) routers. Simulation results show that all three routers are secure from timing channel when compared to a conventional hybrid router. Synthesis results show that the area increments compared to a conventional hybrid router are only 7.63, 11.8, and 19.69 percent for SH, CSH, and CH routers respectively. Thus simulation and synthesis results prove the effectiveness of our proposed mechanisms with acceptable area overheads.

A Network consists of many nodes among which there may be a presence of misbehavior nodes. Delay Tolerant Network (DTN) is a network where the disconnections occur frequently. Store, carry and forward method is followed in DTN. The serious threat against routing in DTN is the selfish behavior. The main intention of selfish node is to save its own energy. Detecting the selfish node in DTN is very difficult. In this paper, a probabilistic misbehavior detection scheme called MAXTRUST has been proposed. Trusted Authority (TA) has been introduced in order to detect the behavior of the nodes periodically based on the task, forwarding history and contact history evidence. After collecting all the evidences from the nodes, the TA would check the inspection node about its behavior. The actions such as punishment or compensation would be given to that particular node based on its behavior. The TA performs probabilistic checking, in order to ensure security at a reduced cost. To further improve the efficiency, dynamic probabilistic inspection has been demonstrated using game theory analysis. The simulation results show the effectiveness and efficiency of the MAXTRUST scheme.

The Android mobile platform supports billions of devices across more than 190 countries around the world. This popularity coupled with user data collection by Android apps has made privacy protection a well-known challenge in the Android ecosystem. In practice, app producers provide privacy policies disclosing what information is collected and processed by the app. However, it is difficult to trace such claims to the corresponding app code to verify whether the implementation is consistent with the policy. Existing approaches for privacy policy alignment focus on information directly accessed through the Android platform (e.g., location and device ID), but are unable to handle user input, a major source of private information. In this paper, we propose a novel approach that automatically detects privacy leaks of user-entered data for a given Android app and determines whether such leakage may violate the app's privacy policy claims. For evaluation, we applied our approach to 120 popular apps from three privacy-relevant app categories: finance, health, and dating. The results show that our approach was able to detect 21 strong violations and 18 weak violations from the studied apps.

The interconnection of networks between several techno-socio-economic sectors such as energy, transport, and communication, questions the manageability and resilience of the digital society. System interdependencies alter the fundamental dynamics that govern isolated systems, which can unexpectedly trigger catastrophic instabilities such as cascading failures. This paper envisions a general-purpose, yet simple prototyping of self-management software systems that can turn system interdependencies from a cause of instability to an opportunity for higher resilience. Such prototyping proves to be challenging given the highly interdisciplinary scope of interdependent networks. Different system dynamics and organizational constraints such as the distributed nature of interdependent networks or the autonomy and authority of system operators over their controlled infrastructure perplex the design for a general prototyping approach, which earlier work has not yet addressed. This paper contributes such a modular design solution implemented as an open source software extension of SFINA, the Simulation Framework for Intelligent Network Adaptations. The applicability of the software artifact is demonstrated with the introduction of a novel self-healing mechanism for interdependent power networks, which optimizes power flow exchanges between a damaged and a healer network to mitigate power cascading failures. Results show a significant decrease in the damage spread by self-healing synergies, while the degree of interconnectivity between the power networks indicates a tradeoff between links survivability and load served. The contributions of this paper aspire to bring closer several research communities working on modeling and simulation of different domains with an economic and societal impact on the resilience of real-world interdependent networks.

Self-assembled semiconductor quantum dots possess an intrinsic geometric symmetry due to the crystal periodic structure. In order to systematically analyze the symmetric properties of quantum dots' bound states resulting only from geometric confinement, we apply group representation theory. We label each bound state for two kinds of popular quantum dot shapes: pyramid and half ellipsoid with the irreducible representation of the corresponding symmetric groups, i.e., C4v and C2v, respectively. Our study completes all the possible irreducible representation cases of groups C4v and C2v. Using the character theory of point groups, we predict the selection rule for electric dipole induced transitions. We also investigate the impact of quantum dot aspect ratio on the symmetric properties of the state wavefunction. This research provides a solid foundation to continue exploring quantum dot symmetry reduction or broken phenomena because of strain, band-mixing and shape irregularity. The results will benefit the researchers who are interested in quantum dot symmetry related effects such as absorption or emission spectra, or those who are studying quantum dots using analytical or numerical simulation approaches.

The increasing amount of malware variants seen in the wild is causing problems for Antivirus Software vendors, unable to keep up by creating signatures for each. The methods used to develop a signature, static and dynamic analysis, have various limitations. Machine learning has been used by Antivirus vendors to detect malware based on the information gathered from the analysis process. However, adversarial examples can cause machine learning algorithms to miss-classify new data. In this paper we describe a method for malware analysis by converting malware binaries to images and then preparing those images for training within a Generative Adversarial Network. These unsupervised deep neural networks are not susceptible to adversarial examples. The conversion to images from malware binaries should be faster than using dynamic analysis and it would still be possible to link malware families together. Using the Generative Adversarial Network, malware detection could be much more effective and reliable.

The penalization of the objective function due to energy deficits is a key element for determining the operational policy of hydroelectric reservoirs. Its definition impacts not only operations, but also system expansion. Brazil historically defined these penalties with basis on a proxy of the economic deficit cost, a value in \$/MWh obtained with aid of the Input-Output Matrix. We propose an approach where these penalties are obtained in order to minimize the operation cost and cost of rationing of the system, considering a criterion of security of supply. A case study with data from the Brazilian System illustrates its application.

Recent research suggests that 88% of Android applications that use Java cryptographic APIs make at least one mistake, which results in an insecure implementation. It is unclear, however, if these mistakes originate from code written by application or third-party library developers. Understanding the responsible party for a misuse case is important for vulnerability disclosure. In this paper, we bridge this knowledge gap and introduce source attribution to the analysis of cryptographic API misuse. We developed BinSight, a static program analyzer that supports source attribution, and we analyzed 132K Android applications collected in years 2012, 2015, and 2016. Our results suggest that third-party libraries are the main source of cryptographic API misuse. In particular, 90% of the violating applications, which contain at least one call-site to Java cryptographic API, originate from libraries. When compared to 2012, we found the use of ECB mode for symmetric ciphers has significantly decreased in 2016, for both application and third-party library code. Unlike application code, however, third-party libraries have significantly increased their reliance on static encryption keys for symmetric ciphers and static IVs for CBC mode ciphers. Finally, we found that the insecure RC4 and DES ciphers were the second and the third most used ciphers in 2016.

Cyber-physical systems (CPS) and their Internet of Things (IoT) components are repeatedly subject to various attacks targeting weaknesses in their firmware. For that reason emerges an imminent demand for secure update mechanisms that not only include specific systems but cover all parts of the critical infrastructure. In this paper we introduce a theoretical concept for a secure CPS device update and verification mechanism and provide information on handling hardware-based security incorporating trusted platform modules (TPM) on those CPS devices. We will describe secure communication channels by state of the art technology and also integrity measurement mechanisms to ensure the system is in a known state. In addition, a multi-level fail-over concept is presented, ensuring continuous patching to minimize the necessity of restarting those systems.

The safety of industrial control systems (ICS) depends not only on comprehensive solutions for protecting information, but also on the timing and closure of vulnerabilities in the software of the ICS. The investigation of security incidents in the ICS is often greatly complicated by the fact that malicious software functions only within the computer's volatile memory. Obtaining the contents of the volatile memory of an attacked computer is difficult to perform with a guaranteed reliability, since the data collection procedure must be based on a reliable code (the operating system or applications running in its environment). The paper proposes a new instrumental method for obtaining the contents of volatile memory, general rules for implementing the means of collecting information stored in memory. Unlike software methods, the proposed method has two advantages: firstly, there is no problem in terms of reading the parts of memory, blocked by the operating system, and secondly, the resulting contents are not compromised by such malicious software. The proposed method is relevant for investigating security incidents of ICS and can be used in continuous monitoring systems for the security of ICS.

The current era can be characterized by the massive reliance on computing platforms in almost all domains, such as manufacturing, defense, healthcare, government. However, with the increased productivity, flexibility, and effectiveness that computers provide, comes the vulnerability to cyber-attacks where software, or even firmware, gets subtly modified by a hacker. The integration of a Trusted Platform Module (TPM) opts to tackle this issue by aiding in the detection of unauthorized modifications so that devices get remediation as needed. Nonetheless, the use of a TPM is impractical for resource-constrained devices due to power, space and cost limitations. With the recent proliferation of miniaturized devices along with the push towards the Internet-of Things (IoT) there is a need for a lightweight and practical alternative to the TPM. This paper proposes a cost-effective solution that incorporates modest amounts of integrated roots-of-trust logic and supports attestation of the integrity of the device's boot-up state. Our solution leverages crypto-acceleration modules found on many microprocessor and microcontroller based IoT devices nowadays, and introduces little additional overhead. The basic concepts have been validated through implementation on an SoC with an FPGA and a hard microcontroller. We report the validation results and highlight the involved tradeoffs.

Establishing a digital identity plays a vital part in the digital era. It is crucial to authenticate and identify the users in order to perform online transactions securely. For example, internet banking applications normally require a user to present a digital identity, e.g., username and password, to allow users to perform online transactions. However, the username-password approach has several downsides, e.g., susceptible to the brute-force attack. Public key binding using Certificate Authority (CA) is another common alternative to provide digital identity. Yet, the public key approach has a serious drawback: all CAs in the browser/OS' CA list are treated equally, and consequently, all trusts on the certificates could be invalidated by compromising only a single root CA's private key. We propose a Real Digital Identity based approach, or RDI, on decentralized PKI scheme. The core idea relies on a combination of well-known parties (e.g., a bank, a government agency) to certify the identity, instead of relying on a single CA. These parties, collectively known as Trusted Source Certificate Authorities (TSCA), formed a network of CAs. The generated certificates are stored in the blockchain controlled by smart contract. RDI creates a digital identity that can be trusted based on the TSCAs' challenge/response and it is also robust against a single point of trust attack on traditional CAs.

In the Self-Organizing Networks (SON) concept, self-healing functions are used to detect, diagnose and correct degraded states in the managed network functions or other resources. Such methods are increasingly important in future network deployments, since ultra-high reliability is one of the key requirements for the future 5G mobile networks, e.g. in critical machine-type communication. In this paper, we discuss the considerations for improving the resiliency of future cognitive autonomous mobile networks. In particular, we present an automated anomaly detection and diagnosis function for SON self-healing based on multi-dimensional statistical methods, case-based reasoning and active learning techniques. Insights from both the human expert and sophisticated machine learning methods are combined in an iterative way. Additionally, we present how a more holistic view on mobile network self-healing can improve its performance.

With the advent of blockchain technology, multiple avenues of use are being explored. The immutability and security afforded by blockchain are the key aspects of exploitation. Extending this to legal contracts involving digital intellectual properties provides a way to overcome the use of antiquated paperwork to handle digital assets.

We formulate a tracker which performs incessant decision making in order to track objects where the objects may undergo different challenges such as partial occlusions, moving camera, cluttered background etc. In the process, the agent must make a decision on whether to keep track of the object when it is occluded or has moved out of the frame temporarily based on its prediction from the previous location or to reinitialize the tracker based on the belief that the target has been lost. Instead of the heuristic methods we depend on reward and penalty based training that helps the agent reach an optimal solution via this partially observable Markov decision making (POMDP). Furthermore, we employ deeply learned compositional model to estimate human pose in order to better handle occlusion without needing human inputs. By learning compositionality of human bodies via deep neural network the agent can make better decision on presence of human in a frame or lack thereof under occlusion. We adapt skeleton based part representation and do away with the large spatial state requirement. This especially helps in cases where orientation of the target in focus is unorthodox. Finally we demonstrate that the deep reinforcement learning based training coupled with pose estimation capabilities allows us to train and tag multiple large video datasets much quicker than previous works.

The Internet of Battlefield Things (IoBT) might be one of the most expensive cyber-physical systems of the next decade, yet much research remains to develop its fundamental enablers. A challenge that distinguishes the IoBT from its civilian counterparts is resilience to a much larger spectrum of threats.

The idea to use multiple paths to transport TCP traffic seems very attractive due to its potential benefits it may offer for both redundancy and better utilization of available resources by load balancing. Fixed and mobile network providers employ frequently load-balancers that use multiple paths on either per-flow or per-destination level, but very seldom on per-packet level. Despite of the benefits of packet-level load balancing mechanisms (e.g., low computational complexity and high bandwidth utilization) network providers can't use them mainly because of TCP packet reorderings that harm TCP performance. Emerging network architectures also support multiple paths, but they face with the same obstacle in balancing their load to multiple paths. Indeed, packet level load balancing research is paralyzed by the reordering vulnerability of TCP.A couple of TCP variants exist that deal with TCP packet reordering problem, but due to lack of end-to-end transparency they were not widely deployed and adopted. In this paper, we revisit TCP's packet reorderings problem and present a transparent and light-weight algorithm, Out-of-Order Robustness for TCP with Transparent Acknowledgment (ACK) Intervention (ORTA), to deal with out-of-order deliveries.ORTA works as a transparent thin layer below TCP and hides harmful side-effects of packet-level load balancing. ORTA monitors all TCP flow packets and uses ACK traffic shaping, without any modifications to either TCP sender or receiver sides. Since it is transparent to TCP end-points, it can be easily deployed on TCP sender end-hosts (EHs), gateway (GW) routers, or access points (APs). ORTA opens a door for network providers to use per-packet load balancing.The proposed ORTA algorithm is implemented and tested in NS-2. The results show that ORTA can prevent TCP performance decrease when per-packet load balancing is used.

Machine learning, specifically deep learning is becoming a key technology component in application domains such as identity management, finance, automotive, and healthcare, to name a few. Proprietary machine learning models - Machine Learning IP - are developed and deployed at the network edge, end devices and in the cloud, to maximize user experience. With the proliferation of applications embedding Machine Learning IPs, machine learning models and hyper-parameters become attractive to attackers, and require protection. Major players in the semiconductor industry provide mechanisms on device to protect the IP at rest and during execution from being copied, altered, reverse engineered, and abused by attackers. In this work we explore system security architecture mechanisms and their applications to Machine Learning IP protection.

Internet of Things refers to a paradigm consisting of a variety of uniquely identifiable day to day things communicating with one another to form a large scale dynamic network. Securing access to this network is a current challenging issue. This paper proposes an encryption system suitable to IoT features. In this system we integrated the fuzzy commitment scheme in DCT-based recognition method for fingerprint. To demonstrate the efficiency of our scheme, the obtained results are analyzed and compared with direct matching (without encryption) according to the most used criteria; FAR and FRR.

In this paper, we consider the application of Reed-Solomon (RS) channel coding for joint error correction and cooperative network coding on non-binary phase shift keying (PSK) modulated signals. The relay first decodes the RS channel coded messages received each in a time slot from all sources before applying network coding (NC) by the use of bit-level exclusive OR (XOR) operation. The network coded resulting message is then channel encoded before its transmission to the next relay or to the destination according to the network configuration. This scenario shows superior performance in comparison with the case where the relay does not perform channel coding/decoding. For different orders of PSK modulation and different wireless configurations, simulation results demonstrate the improvements resulting from the use of RS channel codes in terms of symbol error rate (SER) versus signal-to-noise ratio (SNR).

In a scenario where user files are stored in a network shared volume, a single computer infected by ransomware could encrypt the whole set of shared files, with a large impact on user productivity. On the other hand, medium and large companies maintain hardware or software probes that monitor the traffic in critical network links, in order to evaluate service performance, detect security breaches, account for network or service usage, etc. In this paper we suggest using the monitoring capabilities in one of these tools in order to keep a trace of the traffic between the users and the file server. Once the ransomware is detected, the lost files can be recovered from the traffic trace. This includes any user modifications posterior to the last snapshot of periodic backups. The paper explains the problems faced by the monitoring tool, which is neither the client nor the server of the file sharing operations. It also describes the data structures in order to process the actions of users that could be simultaneously working on the same file. A proof of concept software implementation was capable of successfully recovering the files encrypted by 18 different ransomware families.

Naval Battlefield Network communications rely on wireless network technologies to transmit data between different naval entities, such as ships and shore nodes. Existing naval battle networks heavily depend on the satellite communication system using single-path TCP for reliable, non-interactive data. While satisfactory for traditional use cases, this communication model may be inadequate for outlier cases, such as those arising from satellite failure and wireless signal outage. To promote network stability and assurance in such scenarios, the addition of unmanned aerial vehicles to function as relay points can complement network connectivity and alleviate potential strains in adverse conditions. The inherent mobility of aerial vehicles coupled with existing source node movements, however, leads to frequent network handovers with non-negligible overhead and communication interruption, particularly in the present single-path model. In this paper, we propose a solution based on multi-path TCP and software-defined networking, which, when applied to mobile wireless heterogeneous networks, reduces the network handover delay and improves the total throughput for transmissions among various naval entities at sea and littoral. In case of single link failure, the presence of a connectable relay point maintains TCP connectivity and reduces the risk of service interruption. To validate feasibility and to evaluate performance of our solution, we constructed a Mininet- WiFi emulation testbed. Compared against single-path TCP communication methods, execution of the testbed when configured to use multi-path TCP and UAV relays yields demonstrably more stable network handovers with relatively low overhead, greater reliability of network connectivity, and higher overall end-to-end throughput. Because the SDN global controller dynamically adjusts allocations per user, the solution effectively eliminates link congestion and promotes more efficient bandwidth utilization.

Everyday., the DoS/DDoS attacks are increasing all over the world and the ways attackers are using changing continuously. This increase and variety on the attacks are affecting the governments, institutions, organizations and corporations in a bad way. Every successful attack is causing them to lose money and lose reputation in return. This paper presents an introduction to a method which can show what the attack and where the attack based on. This is tried to be achieved with using clustering algorithm DBSCAN on network traffic because of the change and variety in attack vectors.

This Innovative Practice Work in Progress paper makes the case for using concept inventories in cybersecurity education and presents an example of the development of a concept inventory in the field of secure programming. The secure programming concept inventory is being developed by a team of researchers from four universities. We used a Delphi study to define the content area to be covered by the concept inventory. Participants in the Delphi study included ten experts from academia, government, and industry. Based on the results, we constructed a concept map of secure programming concepts. We then compared this concept map to the Joint Task Force on Cybersecurity Education Curriculum 2017 guidelines to ensure complete coverage of secure programming concepts. Our mapping indicates a substantial match between the concept map and those guidelines.

Adversaries are conducting attack campaigns with increasing levels of sophistication. Additionally, with the prevalence of out-of-the-box toolkits that simplify attack operations during different stages of an attack campaign, multiple new adversaries and attack groups have appeared over the past decade. Characterizing the behavior and the modus operandi of different adversaries is critical in identifying the appropriate security maneuver to detect and mitigate the impact of an ongoing attack. To this end, in this paper, we study two characteristics of an adversary: Risk-averseness and Experience level. Risk-averse adversaries are more cautious during their campaign while fledgling adversaries do not wait to develop adequate expertise and knowledge before launching attack campaigns. One manifestation of these characteristics is through the adversary's choice and usage of attack tools. To detect these characteristics, we present multi-level machine learning (ML) models that use network data generated while under attack by different attack tools and usage patterns. In particular, for risk-averseness, we considered different configurations for scanning tools and trained the models in a testbed environment. The resulting model was used to predict the cautiousness of different red teams that participated in the Cyber Shield ‘16 exercise. The predictions matched the expected behavior of the red teams. For Experience level, we considered publicly-available remote access tools and usage patterns. We developed a Markov model to simulate usage patterns of attackers with different levels of expertise and through experiments on CyberVAN, we showed that the ML model has a high accuracy.