Biblio

The present paper describes some of the results obtained in the Faculty of Computer Systems and Technology at Technical University of Sofia in the implementation of project related to the application of intelligent methods for increasing the security in computer networks. Also is made a survey about existing hybrid methods, which are using several artificial intelligent methods for cyber defense. The paper introduces a model for intrusion detection systems where multi agent systems are the bases and artificial intelligence are applicable by the means simple real-time models constructed in laboratory environment.

Data have become an important asset for analysis and behavioral prediction, especially correlations between data. Privacy protection has aroused academic and social concern given the amount of personal sensitive information involved in data. However, existing works assume that the records are independent of each other, which is unsuitable for associated data. Many studies either fail to achieve privacy protection or lead to excessive loss of information while applying data correlations. Differential privacy, which achieves privacy protection by injecting random noise into the statistical results given the correlation, will improve the background knowledge of adversaries. Therefore, this paper proposes an information entropy differential privacy solution for correlation data privacy issues based on rough set theory. Under the solution, we use rough set theory to measure the degree of association between attributes and use information entropy to quantify the sensitivity of the attribute. The information entropy difference privacy is achieved by clustering based on the correlation and adding personalized noise to each cluster while preserving the correlations between data. Experiments show that our algorithm can effectively preserve the correlation between the attributes while protecting privacy.

We propose a new key sharing protocol executed through any constant parameter noiseless public channel (as Internet itself) without any cryptographic assumptions and protocol restrictions on SNR in the eavesdropper channels. This protocol is based on extraction by legitimate users of eigenvalues from randomly generated matrices. A similar protocol was proposed recently by G. Qin and Z. Ding. But we prove that, in fact, this protocol is insecure and we modify it to be both reliable and secure using artificial noise and privacy amplification procedure. Results of simulation prove these statements.

Confidentiality, authentication, privacy and integrity are the pillars of securing data. The most generic way of providing security is setting up passwords and usernames collectively known as login credentials. Operating systems use different techniques to ensure security of login credentials yet brute force attacks and dictionary attacks along with various other types which leads to success in passing or cracking passwords.The objective of proposed HS model is to enhance the protection of SAM file used by Windows Registry so that the system is preserved from intruders.

Insider threats refer to threats posed by individuals who intentionally or unintentionally destroy, exfiltrate, or leak sensitive information, or expose their organization to outside attacks. Surveys of organizations in government and industry consistently show that threats posed by insiders rival those posed by hackers, and that insider attacks are even more costly. Emerging U.S. government guidelines and policies for establishing insider threat programs tend to specify only minimum standards for insider threat monitoring, analysis, and mitigation programs. Arguably, one of the most serious challenges is to identify and integrate behavioral (sociotechnical) indicators of insider threat r isk in addition to cyber/technical indicators. That is, in focusing on data that are most readily obtained, insider threat programs most often miss the human side of the problem. This talk briefly describes research aiming to catalog human as well as technical factors associated with insider threat risk and summarizes several recent studies that seek to inform the development of more comprehensive, proactive approaches to insider threat assessment.

The borderless, dynamic, high dimensional and virtual natures of cyberspace have brought unprecedented hard situation for defenders. To fight uncertain challenges in versatile cyberspace, a security framework based on the cloud computing platform that facilitates containerization technology to create a security capability pool to generate and distribute security payload according to system needs. Composed by four subsystems of the security decision center, the image and container library, the decision rule base and the security event database, this framework distills structured knowledge from aggregated security events and then deliver security load to the managed network or terminal nodes directed by the decision center. By introducing such unified and standardized top-level security framework that is decomposable, combinable and configurable in a service-oriented manner, it could offer flexibility and effectiveness in reconstructing security resource allocation and usage to reach higher efficiency.

This paper deals with effects of current sensor bandwidth and time delays in a system controlled by a Phase-Shift Self-Oscillating Current Controller (PSSOCC). The robustness of this current controller has been proved in former works showing its good performances in a large range of applications including AC/DC and DC/AC converters, power factor correction, active filters, isolation amplifiers and motor control. As switching frequencies can be upper than 30kHz, time delays and bandwidth limitations cannot be neglected in comparison with former works on this robust current controller. Thus, several models are proposed in this paper to analyze system behaviours. Those models permit to find analytical expressions binding maximum oscillation frequency with time delay and/or additional filter parameters. Through current spectrums analysis, quality of analytical expressions is proved for each model presented in this work. An experimental approach shows that every element of the electronic board having a low-pass effect or delaying command signals need to be included in the model in order to have a perfect match between calculations, simulations and practical results.

The Internet of Things is stepping out of its infancy into full maturity, requiring massive data processing and storage. Unfortunately, because of the unique characteristics of resource constraints, short-range communication, and self-organization in IoT, it always resorts to the cloud or fog nodes for outsourced computation and storage, which has brought about a series of novel challenging security and privacy threats. For this reason, one of the critical challenges of having numerous IoT devices is the capacity to manage them and their data. A specific concern is from which devices or Edge clouds to accept join requests or interaction requests. This paper discusses a design concept for developing the IoT data management platform, along with a data management and lineage traceability implementation of the platform based on blockchain and smart contracts, which approaches the two major challenges: how to implement effective data management and enrich rational interoperability for trusted groups of linked Things; And how to settle conflicts between untrusted IoT devices and its requests taking into account security and privacy preserving. Experimental results show that the system scales well with the loss of computing and communication performance maintaining within the acceptable range, works well to effectively defend against unauthorized access and empower data provenance and transparency, which verifies the feasibility and efficiency of the design concept to provide privacy, fine-grained, and integrity data management over the IoT devices by introducing the blockchain-based data management platform.

the more (IoT) scales up with promises, the more security issues raise to the surface and must be tackled down. IoT is very vulnerable against DoS attacks. In this paper, we propose a hybrid design of signature-based IDS and anomaly-based IDS. The proposed hybrid design intends to enhance the intrusion detection and prevention systems (IDPS) to detect any DoS attack at early stages by classifying the network packets based on user behavior. Simulation results prove successful detection of DoS attack at earlier stages.

Sybil attacks, wherein a network is subverted by forging node identities, remains an open issue in wireless sensor networks (WSNs). This paper proposes a scheme, called Location and Communication ID (LCID) based detection, which employs residual energy, communication ID and location information of sensor nodes for Sybil attacks prevention. Moreover, LCID takes into account the resource constrained nature of WSNs and enhances energy conservation through hierarchical routing. Sybil nodes are purged before clusters formation to ensure that only legitimate nodes participate in clustering and data communication. CH selection is based on the average energy of the entire network to load-balance energy consumption. LCID selects a CH if its residual energy is greater than the average network energy. Furthermore, the workload of CHs is equally distributed among sensor nodes. A CH once selected cannot be selected again for 1/p rounds, where p is the CH selection probability. Simulation results demonstrate that, as compared to an eminent scheme, LCID has a higher Sybil attacks detection ratio, higher network lifetime, higher packet reception rate at the BS, lower energy consumption, and lower packet loss ratio.

OS kernel is the core part of the operating system, and it plays an important role for OS resource management. A popular way to compromise OS kernel is through a kernel rootkit (i.e., malicious kernel module). Once a rootkit is loaded into the kernel space, it can carry out arbitrary malicious operations with high privilege. To defeat kernel rootkits, many approaches have been proposed in the past few years. However, existing methods suffer from some limitations: 1) most methods focus on user-mode rootkit detection; 2) some methods are limited to detect obfuscated kernel modules; and 3) some methods introduce significant performance overhead. To address these problems, we propose VKRD, a kernel rootkit detection system based on the hardware assisted virtualization technology. Compared with previous methods, VKRD can provide a transparent and an efficient execution environment for the target kernel module to reveal its run-time behavior. To select the important run-time features for training our detection models, we utilize the TF-IDF method. By combining the hardware assisted virtualization and machine learning techniques, our kernel rootkit detection solution could be potentially applied in the cloud environment. The experiments show that our system can detect windows kernel rootkits with high accuracy and moderate performance cost.

Nowadays, a major challenge to network security is malicious codes. However, manual extraction of features is one of the characteristics of traditional detection techniques, which is inefficient. On the other hand, the features of the content and behavior of the malicious codes are easy to change, resulting in more inefficiency of the traditional techniques. In this paper, a K-Means Clustering Analysis is proposed based on Adaptive Weights (AW-MMKM). Identifying malicious codes in the proposed method is based on four types of network behavior that can be extracted from network traffic, including active, fault, network scanning, and page behaviors. The experimental results indicate that the AW-MMKM can detect malicious codes efficiently with higher accuracy.

Modern computer peripherals are diverse in their capabilities and functionality, ranging from keyboards and printers to smartphones and external GPUs. In recent years, peripherals increasingly connect over a small number of standardized communication protocols, including USB, Bluetooth, and NFC. The host operating system is responsible for managing these devices; however, malicious peripherals can request additional functionality from the OS resulting in system compromise, or can craft data packets to exploit vulnerabilities within OS software stacks. Defenses against malicious peripherals to date only partially cover the peripheral attack surface and are limited to specific protocols (e.g., USB). In this paper, we propose Linux (e)BPF Modules (LBM), a general security framework that provides a unified API for enforcing protection against malicious peripherals within the Linux kernel. LBM leverages the eBPF packet filtering mechanism for performance and extensibility and we provide a high-level language to facilitate the development of powerful filtering functionality. We demonstrate how LBM can provide host protection against malicious USB, Bluetooth, and NFC devices; we also instantiate and unify existing defenses under the LBM framework. Our evaluation shows that the overhead introduced by LBM is within 1 μs per packet in most cases, application and system overhead is negligible, and LBM outperforms other state-of-the-art solutions. To our knowledge, LBM is the first security framework designed to provide comprehensive protection against malicious peripherals within the Linux kernel.

The confidentiality of data stored in embedded and handheld devices has become an urgent necessity more than ever before. Encryption of sensitive data is a well-known technique to preserve their confidentiality, however it comes with certain costs that can heavily impact the device processing resources. Utilizing multicore processors, which are equipped with current embedded devices, has brought a new era to enhance data confidentiality while maintaining suitable device performance. Encrypting the complete storage area, also known as Full Disk Encryption (FDE) can still be challenging, especially with newly emerging massive storage systems. Alternatively, since the most user sensitive data are residing inside persisting databases, it will be more efficient to focus on securing SQLite databases, through encryption, where SQLite is the most common RDBMS in handheld and embedded systems. This paper addresses the problem of ensuring data protection in embedded and mobile devices while maintaining suitable device performance by mitigating the impact of encryption. We presented here a proposed design for a parallel database encryption system, called SQLite-XTS. The proposed system encrypts data stored in databases transparently on-the-fly without the need for any user intervention. To maintain a proper device performance, the system takes advantage of the commodity multicore processors available with most embedded and mobile devices.

The ubiquitous internetworking of devices in all areas of life is boosted by various trends for instance the Internet of Things. Promising technologies that can be used for such future environments come from Wireless Sensor Networks. It ensures connectivity between distributed, tiny and simple sensor nodes as well as sensor nodes and base stations in order to monitor physical or environmental conditions such as vibrations, temperature or motion. Security plays an increasingly important role in the coming decades in which attacking strategies are becoming more and more sophisticated. Contemporary cryptographic mechanisms face a great threat from quantum computers in the near future and together with Intrusion Detection Systems are hardly applicable on sensors due to strict resource constraints. Thus, in this work a future-proof lightweight and resource-aware security concept for sensor networks with a processing stage permeated filtering mechanism is proposed. A special focus in the concepts evaluation lies on the novel Magic Number filter to mitigate a special kind of Denial-of-Service attack performed on CC1350 LaunchPad ARM Cortex-M3 microcontroller boards.

With the continuously development of smart meter-reading technologies for decades, remote information collection of electricity, water, gas and heat meters have been realized. Due to the difference of electrical interfaces and communication protocols among various types of meters, communication modes of meter terminals are not so compatible, it is difficult to realize communication optimization of electricity, water, gas and heat meters information collection services. In addition, with the development of power consumption information acquisition system, the number of acquisition terminals soars greatly and the data of terminal access is highly concurrent. Therefore, the risk of security access is increasing. This paper presents a light-weighted security access scheme of power line communication based on multi-source data acquisition of electricity, water, gas and heat meters, which separates multi-source data acquisition services and achieve services security isolation and channel security isolation. The communication reliability and security of the meter-reading service of "electricity, water, gas and heat" will be improved and the integrated meter service will be realized reliably.

Ransomware attacks are a prevalent cybersecurity threat to every user and enterprise today. This is attributed to their polymorphic behaviour and dispersion of inexhaustible versions due to the same ransomware family or threat actor. A certain ransomware family or threat actor repeatedly utilises nearly the same style or codebase to create a vast number of ransomware versions. Therefore, it is essential for users and enterprises to keep well-informed about this threat landscape and adopt proactive prevention strategies to minimise its spread and affects. This requires a technique to detect ransomware samples to determine the similarity and link with the known ransomware family or threat actor. Therefore, this paper presents a detection method for ransomware by employing a combination of a similarity preserving hashing method called fuzzy hashing and a clustering method. This detection method is applied on the collected WannaCry/WannaCryptor ransomware samples utilising a range of fuzzy hashing and clustering methods. The clustering results of various clustering methods are evaluated through the use of the internal evaluation indexes to determine the accuracy and consistency of their clustering results, thus the effective combination of fuzzy hashing and clustering method as applied to the particular ransomware corpus. The proposed detection method is a static analysis method, which requires fewer computational overheads and performs rapid comparative analysis with respect to other static analysis methods.

In order to examine malicious activity that occurs in a network or a system, intrusion detection system is used. Intrusion Detection is software or a device that scans a system or a network for a distrustful activity. Due to the growing connectivity between computers, intrusion detection becomes vital to perform network security. Various machine learning techniques and statistical methodologies have been used to build different types of Intrusion Detection Systems to protect the networks. Performance of an Intrusion Detection is mainly depends on accuracy. Accuracy for Intrusion detection must be enhanced to reduce false alarms and to increase the detection rate. In order to improve the performance, different techniques have been used in recent works. Analyzing huge network traffic data is the main work of intrusion detection system. A well-organized classification methodology is required to overcome this issue. This issue is taken in proposed approach. Machine learning techniques like Support Vector Machine (SVM) and Naïve Bayes are applied. These techniques are well-known to solve the classification problems. For evaluation of intrusion detection system, NSL- KDD knowledge discovery Dataset is taken. The outcomes show that SVM works better than Naïve Bayes. To perform comparative analysis, effective classification methods like Support Vector Machine and Naive Bayes are taken, their accuracy and misclassification rate get calculated.

In this paper, we consider one of the approaches to the study of the characteristics of an information system that is under the influence of various factors, and their management using neural networks and wavelet transforms based on determining the relationship between the modified state of the information system and the possibility of dynamic analysis of effects. At the same time, the process of influencing the information system includes the following components: impact on the components providing the functions of the information system; determination of the result of exposure; analysis of the result of exposure; response to the result of exposure. As an input signal, the characteristics of the means that affect are taken. The system includes an adaptive response unit, the input of which receives signals about the prerequisites for changes, and at the output, this unit generates signals for the inclusion of appropriate means to eliminate or compensate for these prerequisites or directly the changes in the information system.

Image encryption is an essential part of a Visual Cryptography. Existing traditional sequential encryption techniques are infeasible to real-time applications. High-performance reformulations of such methods are increasingly growing over the last decade. These reformulations proved better performances over their sequential counterparts. A rotational encryption scheme encrypts the images in such a way that the decryption is possible with the rotated encrypted images. A parallel rotational encryption technique makes use of a high-performance device. But it less-leverages the optimizations offered by them. We propose a rotational image encryption technique which makes use of memory coalescing provided by the Compute Unified Device Architecture (CUDA). The proposed scheme achieves improved global memory utilization and increased efficiency.

The analysis of applied tasks and methods of entropy signal processing are carried out in this article. The theoretical comments about the specific schemes of special processors for the determination of probability and correlation activity are given. The perspective of the influence of probabilistic entropy of C. Shannon as cipher signal receivers is reviewed. Examples of entropy-manipulated signals and system characteristics of the proposed special processors are given.

Virtual reality (VR) recently is a promising technique in both industry and academia due to its potential applications in immersive experiences including website, game, tourism, or museum. VR technique provides an amazing 3-Dimensional (3D) experiences by requiring a very high amount of elements such as images, texture, depth, focus length, etc. However, in order to apply VR technique to various devices, especially in mobiles, ultra-high transmission rate and extremely low latency are really big challenge. Considering this problem, this paper proposes a novel combination model by transforming the computing capability of VR device into an equivalent caching amount while remaining low latency and fast transmission. In addition, Classic caching models are used to computing and catching capabilities which is easily apply to multi-user models.

A new approach of a formalism of hybrid automatons has been proposed for the analysis of conflict processes between the information system and the information's security malefactor. An example of probability-based assessment on malefactor's victory has been given and the possibility to abstract from a specific type of probability density function for the residence time of parties to the conflict in their possible states. A model of the distribution of destructive informational influences in the information system to connect the process of spread of destructive information processes and the process of changing subjects' states of the information system has been proposed. An example of the destructive information processes spread analysis has been given.

This paper presents a multilayer protection approach to guard programs against Return-Oriented Programming (ROP) attacks. Upper layers validate most of a program's control flow at a low computational cost; thus, not compromising runtime. Lower layers provide strong enforcement guarantees to handle more suspicious flows; thus, enhancing security. Our multilayer system combines techniques already described in the literature with verifications that we introduce in this paper. We argue that modern versions of x86 processors already provide the microarchitectural units necessary to implement our technique. We demonstrate the effectiveness of our multilayer protection on a extensive suite of benchmarks, which includes: SPEC CPU2006; the three most popular web browsers; 209 benchmarks distributed with LLVM and four well-known systems shown to be vulnerable to ROP exploits. Our experiments indicate that we can protect programs with almost no overhead in practice, allying the good performance of lightweight security techniques with the high dependability of heavyweight approaches.

This paper discusses two pieces of software designed for intelligence analysis, the brainstorming tool and the Scenario Planning Advisor. These tools were developed in the Cognitive Immersive Systems Lab (CISL) in conjunction with IBM. We discuss the immersive environment the tools are situated in, and the proposed benefit for intelligence analysis.