Biblio

Nowadays, computers are used everywhere to carry out daily routine tasks. The input devices i.e. keyboard or mouse are used to feed input to computers. The surveillance of input devices is much important as monitoring the users logging activity. A keylogger also referred as a keystroke logger, is a software or hardware device which monitors every keystroke typed by a user. Keylogger runs in the background that user cannot identify its presence. It can be used as monitoring software for parents to keep an eye on children activity on computers and for the owner to monitor their employees. A keylogger (which can be either spyware or software) is a kind of surveillance software that has the ability to store every keystroke in a log file. It is very dangerous for those systems which use their system for daily transaction purpose i.e. Online Banking Systems. A keylogger is a tool, made to save all the keystroke generated through the machine which sanctions hackers to steal sensitive information without user's intention. Privileged also relies on the access for both implementation and placement by Kernel keylogger, the entire message transmitted from the keyboard drivers, while the programmer simply relies on kernel level facilities that interrupt. This certainly needs a large power and expertise for real and error-free execution. However, it has been observed that 90% of the current keyloggers are running in userspace so they do not need any permission for execution. Our aim is focused on detecting userspace keylogger. Our intention is to forbid userspace keylogger from stealing confidential data and information. For this purpose, we use a strategy which is clearly based on detection manner techniques for userspace keyloggers, an essential category of malware packages. We intend to achieve this goal by matching I/O of all processes with some simulated activity of the user, and we assert detection in case the two are highly correlated. The rationale behind this is that the more powerful stream of keystrokes, the more I/O operations are required by the keylogger to log the keystrokes into the file.

Modern operating systems for personal computers (including Linux, MAC, and Windows) provide user-level APIs for an application to access the I/O paths of another application. This design facilitates information sharing between applications, enabling applications such as screenshots. However, it also enables user-level malware to log a user's keystrokes or scrape a user's screen output. In this work, we explore a design called SwitchMan to protect a user's I/O paths against user-level malware attacks. SwitchMan assigns each user with two accounts: a regular one for normal operations and a protected one for inputting and outputting sensitive data. Each user account runs under a separate virtual terminal. Malware running under a user's regular account cannot access sensitive input/output under a user's protected account. At the heart of SwitchMan lies a secure protocol that enables automatic account switching when an application requires sensitive input/output from a user. Our performance evaluation shows that SwitchMan adds acceptable performance overhead. Our security and usability analysis suggests that SwitchMan achieves a better tradeoff between security and usability than existing solutions.

The privacy of people on internet is getting reduced day by day. Data records of many prestigious organizations are getting corrupted due to computer malwares. Computer viruses are becoming more advanced. Hackers are able penetrate into a network and able to manipulate data. In this paper, describes the types of malwares like Trojans, boot sector virus, polymorphic virus, etc., and some of the hacking techniques which include DOS attack, DDoS attack, brute forcing, man in the middle attack, social engineering, information gathering tools, spoofing, sniffing. Counter measures for cyber attacks include VPN, proxy, tor (browser), firewall, antivirus etc., to understand the need of cyber security.

The concept of a decoy process is a new development of defensive deception beyond traditional honeypots. Decoy processes can be exceptionally effective in detecting malware, directly upon contact or by redirecting malware to decoy I/O. A key requirement is that they resemble their real counterparts very closely to withstand adversarial probes by threat actors. To be usable, decoy processes need to consume only a small fraction of the resources consumed by their real counterparts. Our contribution in this paper is twofold. We attack the resource utilization consistency of decoy processes provided by a neural network with a heatmap training mechanism, which we find to be insufficiently trained. We then devise machine learning over control flow graphs that improves the heatmap training mechanism. A neural network retrained by our work shows higher accuracy and defeats our attacks without a significant increase in its own resource utilization.

This work proposes a scheme to detect, isolate and mitigate malicious disruption of electro-mechanical processes in legacy PLCs where each PLC works as a finite state machine (FSM) and goes through predefined states depending on the control flow of the programs and input-output mechanism. The scheme generates a group-signature for a particular state combining the signature shares from each of these PLCs using \$(k,\textbackslashtextbackslash l)\$-threshold signature scheme.If some of them are affected by the malicious code, signature can be verified by k out of l uncorrupted PLCs and can be used to detect the corrupted PLCs and the compromised state. We use OpenPLC software to simulate Legacy PLC system on Raspberry Pi and show İ/O\$ pin configuration attack on digital and pulse width modulation (PWM) pins. We describe the protocol using a small prototype of five instances of legacy PLCs simultaneously running on OpenPLC software. We show that when our proposed protocol is deployed, the aforementioned attacks get successfully detected and the controller takes corrective measures. This work has been developed as a part of the problem statement given in the Cyber Security Awareness Week-2017 competition.

Block storage resources are essential in an Infrastructure-as-a-Service(IaaS) cloud computing system. It is used for storing virtual machines' images. It offers persistent storage service even the virtual machine is off. Distribute storage systems are used to provide block storage services in IaaS, such as Amazon EBS, Cinder, Ceph, Sheepdog. Ceph is widely used as the backend block storage service of OpenStack platform. It converts block devices into objects with the same size and saves them on the local file system. The performance of block devices provided by Ceph is only 30% of hard disks in many cases. One of the key issues that affect the performance of Ceph is the three replicas for fault tolerance. But our research finds that replicas are not the real reason slow down the performance. In this paper, we present a new approach to accelerate the IO operations. The experiment results show that by using our storage engine, Ceph can offer faster IO performance than the hard disk in most cases. Our new storage engine provides more than three times up than the original one.

The demand of highly functioning storage systems has led to the evolution of the filesystems which are capable of successfully and effectively carrying out the data management, configures the new storage hardware, proper backup and recovery as well. The research paper aims to find out which file system can serve better in backup storage (e.g. NAS storage) and compute-intensive systems (e.g. database consolidation in cloud computing). We compare such two most potential opensource filesystem ZFS and BTRFS based on their file I/O performance on a storage pool of flash drives, which are made available over iSCSI (internet) for different record sizes. This paper found that ZFS performed better than BTRFS in this arrangement.

The I/O automata model of Lynch and Turtle (1987) is summarized and used to formalize several types of system integrity based on the control of transitions to invalid starts. Type-A integrity is exhibited by systems with no invalid initial states and that disallow transitions from valid reachable to invalid states. Type-B integrity is exhibited by systems that disallow externally-controlled transitions from valid reachable to invalid states, Type-C integrity is exhibited by systems that allow locally-controlled or externally-controlled transitions from reachable to invalid states. Strict-B integrity is exhibited by systems that are Type-B but not Type-A. Strict-C integrity is exhibited by systems that are Type-C but not Type-B. Basic results on the closure properties that hold under composition of systems exhibiting these types of integrity are presented in I/O automata-theoretic terms. Specifically, Type-A, Type-B, and Type-C integrity are shown to be composable, whereas Strict-B and Strict-C integrity are shown to not be generally composable. The integrity definitions and compositional results are illustrated using the familiar vending machine example specified as an I/O automaton and composed with a customer environment. The implications of the integrity definitions and compositional results on practical system design are discussed and a research plan for future work is outlined.

The Software Defined Networking paradigm has enabled dynamic configuration and control of large networks. Although the division of the control and data planes on networks has lead to dynamic reconfigurability of large networks, finding the minimal and optimal set of controllers that can adapt to the changes in the network has proven to be a challenging problem. Recent research tends to favor small solution sets with a focus on either propagation latency or controller load distribution, and struggles to find large balanced solution sets. In this paper, we propose a multi-objective genetic algorithm based approach to the controller placement problem that minimizes inter-controller latency, load distribution and the number of controllers with fitness sharing. We demonstrate that the proposed approach provides diverse and adaptive solutions to real network architectures such as the United States backbone and Japanese backbone networks. We further discuss the relevance and application of a diversity focused genetic algorithm for a moving target defense security model.

Successful attacks on computer networks today do not often owe their victory to directly overcoming strong security measures set up by the defender. Rather, most attacks succeed because the number of possible vulnerabilities are too large for humans to fully protect without making a mistake. Regardless of the security elsewhere, a skilled attacker can exploit a single vulnerability in a defensive system and negate the benefits of those security measures. This paper presents an evolutionary framework for evolving attacker agents in a real, emulated network environment using genetic programming, as a foundation for coevolutionary systems which can automatically discover and mitigate network security flaws. We examine network enumeration, an initial network reconnaissance step, through our framework and present results demonstrating its success, indicating a broader applicability to further cyber-security tasks.

Stability has been one of the most fundamental premises in biometric recognition field. In the last few years, a few achievements have been made on proving this theoretical premises concerning fingerprints, palm prints, iris, face, etc. However, none of related academic results have been published on finger-vein recognition so far. In this paper, we try to study on the stability of finger-vein within a designed timespan (four years). In order to achieve this goal, a proper database for stability was collected with all external influences of finger-vein features (acquiring hardware, user behavior and circumstance situation) eliminated. Then, for the first time, we proposed a steady-state model of finger-vein features indicating that each specific finger owns a stable steady-state which all its finger-vein images would properly converging to, regardless of time. Experiments have been conducted on our 5-year/200,000-finger data set. And results from both genuine match and imposter match demonstrate that the model is well supported. This steady-state model is generic, hence providing a common method on how to evaluate the stability of other types of biometric features.

To combat susceptibility of modern computing systems to cyberattack, identifying and disrupting malicious traffic without human intervention is essential. To accomplish this, three main tasks for an effective intrusion detection system have been identified: monitor network traffic, categorize and identify anomalous behavior in near real time, and take appropriate action against the identified threat. This system leverages distributed SDN architecture and the principles of Artificial Immune Systems and Self-Organizing Maps to build a network-based intrusion detection system capable of detecting and terminating DDoS attacks in progress.

DNA synthesis has become increasingly common, and many synthetic DNA molecules are licensed intellectual property (IP). DNA samples are shared between academic labs, ordered from DNA synthesis companies and manipulated for a variety of different purposes, mostly to study their properties and improve upon them. However, it is not uncommon for a sample to change hands many times with very little accompanying information and no proof of origin. This poses significant challenges to the original inventor of a DNA molecule, trying to protect her IP rights. More importantly, following the anthrax attacks of 2001, there is an increased urgency to employ microbial forensic technologies to trace and track agent inventories. However, attribution of physical samples is next to impossible with existing technologies. In this paper, we describe our efforts to solve this problem by embedding digital signatures in DNA molecules synthesized in the laboratory. We encounter several challenges that we do not face in the digital world. These challenges arise primarily from the fact that changes to a physical DNA molecule can affect its properties, random mutations can accumulate in DNA samples over time, DNA sequencers can sequence (read) DNA erroneously and DNA sequencing is still relatively expensive (which means that laboratories would prefer not to read and re-read their DNA samples to get error-free sequences). We address these challenges and present a digital signature technology that can be applied to synthetic DNA molecules in living cells.

Trusted computing is one of the main development trend in information security. However, there are still two limitations in existing trusted computing model. One is that the measurement process of the existing trusted computing model can be bypassed. Another is it lacks of effective runtime detection methods to protect the system, even the measurement process itself. In this paper, we introduce an active trusted model which can solve those two problems. Our active trusted computing model is comprised of two components: normal computation world and isolated security world. All the security tasks of active trusted computing model are assigned to the isolated security world. In this model, the static trusted measurement measures BIOS and operating system at the start-up of the computer system; and the dynamic trusted measurement measures the code segment, the data segment, and other critical structures actively and periodically at runtime. We have implemented a prototype of the active trusted computing model and done preliminary evaluation. Our experimental results show that this prototype can perform trusted computing on-the-fly effectively with an acceptable performance overhead.

This paper is concerned with three ensembles of systematic low density generator matrix (LDGM) codes, all of which were provably capacity-achieving in terms of bit error rate (BER). This, however, does not necessarily imply that they achieve the capacity in terms of frame error rate (FER), as seen from a counterexample constructed in this paper. We then show that the first and second ensembles are capacity-achieving under list decoding over binary-input output symmetric (BIOS) memoryless channels. We point out that, in principle, the equivocation due to list decoding can be removed with negligible rate loss by the use of the concatenated codes. Simulation results show that the considered convolutional (spatially-coupled) LDGM code is capacity-approaching with an iterative belief propagation decoding algorithm.

An important principle in computational security is to reduce the attack surface, by maintaining the Trusted Computing Base (TCB) small. Even so, no security technique ensures full protection against any adversary. Thus, sensitive applications should be designed with several layers of protection so that, even if a layer might be violated, sensitive content will not be compromised. In 2015, Intel released the Software Guard Extensions (SGX) technology in its processors. This mechanism allows applications to allocate enclaves, which are private memory regions that can hold code and data. Other applications and even privileged code, like the OS kernel and the BIOS, are not able to access enclaves' contents. This paper presents a novel password file protection scheme, which uses Intel SGX to protect authentication credentials in the PAM authentication framework, commonly used in UNIX systems. We defined and implemented an SGX-enabled version of the pam\_unix.so authentication module, called UniSGX. This module uses an SGX enclave to handle the credentials informed by the user and to check them against the password file. To add an extra security layer, the password file is stored using SGX sealing. A threat model was proposed to assess the security of the proposed solution. The obtained results show that the proposed solution is secure against the threat model considered, and that its performance overhead is acceptable from the user point of view. The scheme presented here is also suitable to other authentication frameworks.

In light of recent advances in genetic-algorithm-driven automated program modification, our team has been actively exploring the art, engineering, and discovery of novel semantics-preserving transforms. While modern compilers represent some of the best ideas we have for automated program modification, current approaches represent only a small subset of the types of transforms which can be achieved. In the wilderness of post-apocalyptic software ecosystems of genetically-modified and mutant programs, there exist a broad array of potentially useful software mutations, including semantics-preserving transforms that may play an important role in future software design, development, and most importantly, evolution.

Highly privileged software, such as firmware, is an attractive target for attackers. Thus, BIOS vendors use cryptographic signatures to ensure firmware integrity at boot time. Nevertheless, such protection does not prevent an attacker from exploiting vulnerabilities at runtime. To detect such attacks, we propose an event-based behavior monitoring approach that relies on an isolated co-processor. We instrument the code executed on the main CPU to send information about its behavior to the monitor. This information helps to resolve the semantic gap issue. Our approach does not depend on a specific model of the behavior nor on a specific target. We apply this approach to detect attacks targeting the System Management Mode (SMM), a highly privileged x86 execution mode executing firmware code at runtime. We model the behavior of SMM using invariants of its control-flow and relevant CPU registers (CR3 and SMBASE). We instrument two open-source firmware implementations: EDKII and coreboot. We evaluate the ability of our approach to detect state-of-the-art attacks and its runtime execution overhead by simulating an x86 system coupled with an ARM Cortex A5 co-processor. The results show that our solution detects intrusions from the state of the art, without any false positives, while remaining acceptable in terms of performance overhead in the context of the SMM (i.e., less than the 150 us threshold defined by Intel).

Algorithms for oblivious random access machine (ORAM) simulation allow a client, Alice, to obfuscate a pattern of data accesses with a server, Bob, who is maintaining Alice's outsourced data while trying to learn information about her data. We present a novel ORAM scheme that improves the asymptotic I/O overhead of previous schemes for a wide range of size parameters for clientside private memory and message blocks, from logarithmic to polynomial. Our method achieves statistical security for hiding Alice's access pattern and, with high probability, achieves an I/O overhead that ranges from O(1) to O(log2 n/(log logn)2), depending on these size parameters, where n is the size of Alice's outsourced memory. Our scheme, which we call BIOS ORAM, combines multiple uses of B-trees with a reduction of ORAM simulation to isogrammic access sequences.

Mobile tracking is a key challenge that has been investigated from both practical and theoretical aspects. This paper proposes an anti-theft mobile phone security system using basic input/output system (BIOS). This mobile phone security system allows us to determine the position of mobile device. The proposed security system is based on hardware implementation technique in which mobile is designed in such a way that a mobile can be traced out even if battery and Subscriber Identity Module (SIM) are plug-out. Furthermore, we also consider the usage of BIOS and its importance in our daily life. Our proposed solution will help the designers in improving the device security.

Malware sandboxes, widely used by antivirus companies, mobile application marketplaces, threat detection appliances, and security researchers, face the challenge of environment-aware malware that alters its behavior once it detects that it is being executed on an analysis environment. Recent efforts attempt to deal with this problem mostly by ensuring that well-known properties of analysis environments are replaced with realistic values, and that any instrumentation artifacts remain hidden. For sandboxes implemented using virtual machines, this can be achieved by scrubbing vendor-specific drivers, processes, BIOS versions, and other VM-revealing indicators, while more sophisticated sandboxes move away from emulation-based and virtualization-based systems towards bare-metal hosts. We observe that as the fidelity and transparency of dynamic malware analysis systems improves, malware authors can resort to other system characteristics that are indicative of artificial environments. We present a novel class of sandbox evasion techniques that exploit the "wear and tear" that inevitably occurs on real systems as a result of normal use. By moving beyond how realistic a system looks like, to how realistic its past use looks like, malware can effectively evade even sandboxes that do not expose any instrumentation indicators, including bare-metal systems. We investigate the feasibility of this evasion strategy by conducting a large-scale study of wear-and-tear artifacts collected from real user devices and publicly available malware analysis services. The results of our evaluation are alarming: using simple decision trees derived from the analyzed data, malware can determine that a system is an artificial environment and not a real user device with an accuracy of 92.86%. As a step towards defending against wear-and-tear malware evasion, we develop statistical models that capture a system's age and degree of use, which can be used to aid sandbox operators in creating system i- ages that exhibit a realistic wear-and-tear state.