Biblio

In this paper, a novel game-theoretic framework is introduced to analyze and enhance the security of adversarial Internet of Battlefield Things (IoBT) systems. In particular, a dynamic, psychological network interdiction game is formulated between a soldier and an attacker. In this game, the soldier seeks to find the optimal path to minimize the time needed to reach a destination, while maintaining a desired bit error rate (BER) performance by selectively communicating with certain IoBT devices. The attacker, on the other hand, seeks to find the optimal IoBT devices to attack, so as to maximize the BER of the soldier and hinder the soldier's progress. In this game, the soldier and attacker's first- order and second-order beliefs on each others' behavior are formulated to capture their psychological behavior. Using tools from psychological game theory, the soldier and attacker's intention to harm one another is captured in their utilities, based on their beliefs. A psychological forward induction-based solution is proposed to solve the dynamic game. This approach can find a psychological sequential equilibrium of the game, upon convergence. Simulation results show that, whenever the soldier explicitly intends to frustrate the attacker, the soldier's material payoff is increased by up to 15.6% compared to a traditional dynamic Bayesian game.

Recent behavioral research in information security has focused on increasing employees' motivation to enhance the security performance in an organization. This empirical study investigated employees' information security policy (ISP) compliance intentions using self-determination theory (SDT). Relevant hypotheses were developed to test the proposed research model. Data obtained via a survey (N=3D407) from a Fortune 600 organization in Saudi Arabia provides empirical support for the model. The results confirmed that autonomy, competence and the concept of relatedness all positively affect employees' intentions to comply. The variable 'perceived value congruence' had a negative effect on ISP compliance intentions, and the perceived legitimacy construct did not affect employees' intentions. In general, the findings of this study suggest that SDT has value in research into employees' ISP compliance intentions.

Autonomous systems are gaining momentum in various application domains, such as autonomous vehicles, autonomous transport robotics and self-adaptation in smart homes. Product liability regulations impose high standards on manufacturers of such systems with respect to dependability (safety, security and privacy). Today's conventional engineering methods are not adequate for providing guarantees with respect to dependability requirements in a cost-efficient manner, e.g. road tests in the automotive industry sum up millions of miles before a system can be considered sufficiently safe. System engineers will no longer be able to test and respectively formally verify autonomous systems during development time in order to guarantee the dependability requirements in advance. In this vision paper, we introduce a new holistic software systems engineering approach for autonomous systems, which integrates development time methods as well as operation time techniques. With this approach, we aim to give the users a transparent view of the confidence level of the autonomous system under use with respect to the dependability requirements. We present already obtained results and point out research goals to be addressed in the future.

SMS spam refers to undesired text message. Machine Learning methods for anti-spam filters have been noticeably effective in categorizing spam messages. Dataset used in this research is known as Tiago's dataset. Crucial step in the experiment was data preprocessing, which involved reducing text to lower case, tokenization, removing stopwords. Convolutional Neural Network was the proposed method for classification. Overall model's accuracy was 98.4%. Obtained model can be used as a tool in many applications.

Reliance on off-site untrusted fabrication facilities has given rise to several threats such as intellectual property (IP) piracy, overbuilding and hardware Trojans. Logic locking is a promising defense technique against such malicious activities that is effected at the silicon layer. Over the past decade, several logic locking defenses and attacks have been presented, thereby, enhancing the state-of-the-art. Nevertheless, there has been little research aiming to demonstrate the applicability of logic locking with large-scale multi-million-gate industrial designs consisting of multiple IP blocks with different security requirements. In this work, we take on this challenge to successfully lock a multi-million-gate system-on-chip (SoC) provided by DARPA by taking it all the way to GDSII layout. We analyze how specific features, constraints, and security requirements of an IP block can be leveraged to lock its functionality in the most appropriate way. We show that the blocks of an SoC can be locked in a customized manner at 0.5%, 15.3%, and 1.5% chip-level overhead in power, performance, and area, respectively.

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.

Today, network security is a world hot topic in computer security and defense. Intrusions and attacks in network infrastructures lead mostly in huge financial losses, massive sensitive data leaks, thus decreasing efficiency, competitiveness and the quality of productivity of an organization. Network Intrusion Detection System (NIDS) is valuable tool for the defense-in-depth of computer networks. It is widely deployed in network architectures in order to monitor, to detect and eventually respond to any anomalous behavior and misuse which can threat confidentiality, integrity and availability of network resources and services. Thus, the presence of NIDS in an organization plays a vital part in attack mitigation, and it has become an integral part of a secure organization. In this paper, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel hybrid Framework (GASAA) based on improved Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). GA is improved through an optimization strategy, namely Fitness Value Hashing (FVH), which reduce execution time, convergence time and save processing power. Experimental results on KDD CUP' 99 dataset show that our optimized ANIDS (Anomaly NIDS) based BPNN, called “ANIDS BPNN-GASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. In addition, improvement of GA through FVH has saved processing power and execution time. Thereby, our proposed IDS is very much suitable for network anomaly detection.

Unlike traditional processors, embedded Internet of Things (IoT) devices lack resources to incorporate protection against modern sophisticated attacks resulting in critical consequences. Remote attestation (RA) is a security service to establish trust in the integrity of a remote device. While conventional RA is static and limited to detecting malicious modification to software binaries at load-time, recent research has made progress towards runtime attestation, such as attesting the control flow of an executing program. However, existing control-flow attestation schemes are inefficient and vulnerable to sophisticated data-oriented programming (DOP) attacks subvert these schemes and keep the control flow of the code intact. In this paper, we present LiteHAX, an efficient hardware-assisted remote attestation scheme for RISC-based embedded devices that enables detecting both control-flow attacks as well as DOP attacks. LiteHAX continuously tracks both the control-flow and data-flow events of a program executing on a remote device and reports them to a trusted verifying party. We implemented and evaluated LiteHAX on a RISC-V System-on-Chip (SoC) and show that it has minimal performance and area overhead.

A Stoner-Wohlfarth-type model is used to demonstrate the effect of the buildup of magnetic charges near the grain boundaries of low anisotropy polycrystalline materials, revealed by measuring the magnetization during positive-field warming after negative-field cooling. The remnant magnetization after negative-field cooling has two different contributions. The temperature-dependent component is modeled as an assembly of particles with thermal relaxation. The temperature-independent component is modeled as an assembly of particles overcoming variable phenomenological energy barriers corresponding to the change in susceptibility when the anisotropy constant changes its sign. The model is applicable to soft-magnetic materials where the buildup of the magnetic charges near the grain boundaries creates demagnetizing fields opposing, and comparable in magnitude to, the anisotropy field. The results of the model are in qualitative agreement with published data revealing the magneto-thermal characteristics of polycrystalline gadolinium.

For sharing resources using ad hoc communication MANET are quite effective and scalable medium. MANET is a distributed, decentralized, dynamic network with no fixed infrastructure, which are self- organized and self-managed. Achieving high security level is a major challenge in case of MANET. Layered architecture is one of the ways for handling security challenges, which enables collection and analysis of data from different security dimensions. This work proposes a novel multi-layered outlier detection algorithm using hierarchical similarity metric with hierarchical categorized data. Network performance with and without the presence of outlier is evaluated for different quality-of-service parameters like percentage of APDR and AT for small (100 to 200 nodes), medium (200 to 1000 nodes) and large (1000 to 3000 nodes) scale networks. For a network with and without outliers minimum improvements observed are 9.1 % and 0.61 % for APDR and AT respectively while the maximum improvements of 22.1 % and 104.1 %.

Dynamic network security services have been proposed exploiting the benefits of Software Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies. However, many of these services rely on controller interaction, which presents a performance and scalability challenge, and a threat vector. To overcome the performance issue, stateful data-plane designs have been proposed. Unfortunately, these solutions do not offer protection from attacks that exploit the SDN implementation of network functions such as topology and path update, or services such as the Address Resolution Protocol (ARP). In this work, we propose state-based SDN security protection mechanisms. Our stateful security data plane solution, OFMTL-SEC, is designed to provide protection against attacks on SDN and traditional network services. Specifically, we present a novel data plane protection against configuration-based attacks in SDN and against ARP spoofing. OFMTL-SEC is compared with the state-of-the-art solutions and offers increased security to SDNs with negligible performance impact.

Many governments organizations in Libya have started transferring traditional government services to e-government. These e-services will benefit a wide range of public. However, deployment of e-government bring many new security issues. Attackers would take advantages of vulnerabilities in these e-services and would conduct cyber attacks that would result in data loss, services interruptions, privacy loss, financial loss, and other significant loss. The number of vulnerabilities in e-services have increase due to the complexity of the e-services system, a lack of secure programming practices, miss-configuration of systems and web applications vulnerabilities, or not staying up-to-date with security patches. Unfortunately, there is a lack of study being done to assess the current security level of Libyan government websites. Therefore, this study aims to assess the current security of 16 Libyan government websites using penetration testing framework. In this assessment, no exploits were committed or tried on the websites. In penetration testing framework (pen test), there are four main phases: Reconnaissance, Scanning, Enumeration, Vulnerability Assessment and, SSL encryption evaluation. The aim of a security assessment is to discover vulnerabilities that could be exploited by attackers. We also conducted a Content Analysis phase for all websites. In this phase, we searched for security and privacy policies implementation information on the government websites. The aim is to determine whether the websites are aware of current accepted standard for security and privacy. From our security assessment results of 16 Libyan government websites, we compared the websites based on the number of vulnerabilities found and the level of security policies. We only found 9 websites with high and medium vulnerabilities. Many of these vulnerabilities are due to outdated software and systems, miss-configuration of systems and not applying the latest security patches. These vulnerabilities could be used by cyber hackers to attack the systems and caused damages to the systems. Also, we found 5 websites didn't implement any SSL encryption for data transactions. Lastly, only 2 websites have published security and privacy policies on their websites. This seems to indicate that these websites were not concerned with current standard in security and privacy. Finally, we classify the 16 websites into 4 safety categories: highly unsafe, unsafe, somewhat unsafe and safe. We found only 1 website with a highly unsafe ranking. Based on our finding, we concluded that the security level of the Libyan government websites are adequate, but can be further improved. However, immediate actions need to be taken to mitigate possible cyber attacks by fixing the vulnerabilities and implementing SSL encryption. Also, the websites need to publish their security and privacy policy so the users could trust their websites.

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 rapid growth of the cyber attacks, cyber threat intelligence (CTI) sharing becomes essential for providing advance threat notice and enabling timely response to cyber attacks. Our goal in this paper is to develop an approach to extract low-level cyber threat actions from publicly available CTI sources in an automated manner to enable timely defense decision making. Specifically, we innovatively and successfully used the metrics of entropy and mutual information from Information Theory to analyze the text in the cybersecurity domain. Combined with some basic NLP techniques, our framework, called ActionMiner has achieved higher precision and recall than the state-of-the-art Stanford typed dependency parser, which usually works well in general English but not cybersecurity texts.

Although Stylometry has been effectively used for Authorship Attribution, there is a growing number of methods being developed that allow authors to mask their identity [2, 13]. In this paper, we investigate the usage of non-traditional feature sets for Authorship Attribution. By using non-traditional feature sets, one may be able to reveal the identity of adversarial authors who are attempting to evade detection from Authorship Attribution systems that are based on more traditional feature sets. In addition, we demonstrate how GEFeS (Genetic & Evolutionary Feature Selection) can be used to evolve high-performance hybrid feature sets composed of two non-traditional feature sets for Authorship Attribution: LIWC (Linguistic Inquiry & Word Count) and Sentiment Analysis. These hybrids were able to reduce the Adversarial Effectiveness on a test set presented in [2] by approximately 33.4%.

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.

Confidentiality, Integrity, and Availability are principal keys to build any secure software. Considering the security principles during the different software development phases would reduce software vulnerabilities. This paper measures the impact of the different software quality metrics on Confidentiality, Integrity, or Availability for any given object-oriented PHP application, which has a list of reported vulnerabilities. The National Vulnerability Database was used to provide the impact score on confidentiality, integrity, and availability for the reported vulnerabilities on the selected applications. This paper includes a study for these scores and its correlation with 25 code metrics for the given vulnerable source code. The achieved results were able to correlate 23.7% of the variability in `Integrity' to four metrics: Vocabulary Used in Code, Card and Agresti, Intelligent Content, and Efferent Coupling metrics. The Length (Halstead metric) could alone predict about 24.2 % of the observed variability in ` Availability'. The results indicate no significant correlation of `Confidentiality' with the tested code metrics.

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.

With the globalization of manufacturing and supply chains, ensuring the security and trustworthiness of ICs has become an urgent challenge. Split manufacturing (SM) and layout camouflaging (LC) are promising techniques to protect the intellectual property (IP) of ICs from malicious entities during and after manufacturing (i.e., from untrusted foundries and reverse-engineering by end-users). In this paper, we strive for “the best of both worlds,” that is of SM and LC. To do so, we extend both techniques towards 3D integration, an up-and-coming design and manufacturing paradigm based on stacking and interconnecting of multiple chips/dies/tiers. Initially, we review prior art and their limitations. We also put forward a novel, practical threat model of IP piracy which is in line with the business models of present-day design houses. Next, we discuss how 3D integration is a naturally strong match to combine SM and LC. We propose a security-driven CAD and manufacturing flow for face-to-face (F2F) 3D ICs, along with obfuscation of interconnects. Based on this CAD flow, we conduct comprehensive experiments on DRC-clean layouts. Strengthened by an extensive security analysis (also based on a novel attack to recover obfuscated F2F interconnects), we argue that entering the next, third dimension is eminent for effective and efficient IP protection.

With an increase in targeted attacks such as advanced persistent threats (APTs), enterprise system defenders require comprehensive frameworks that allow them to collaborate and evaluate their defense systems against such attacks. MITRE has developed a framework which includes a database of different kill-chains, tactics, techniques, and procedures that attackers employ to perform these attacks. In this work, we leverage natural language processing techniques to extract attacker actions from threat report documents generated by different organizations and automatically classify them into standardized tactics and techniques, while providing relevant mitigation advisories for each attack. A naïve method to achieve this is by training a machine learning model to predict labels that associate the reports with relevant categories. In practice, however, sufficient labeled data for model training is not always readily available, so that training and test data come from different sources, resulting in bias. A naïve model would typically underperform in such a situation. We address this major challenge by incorporating an importance weighting scheme called bias correction that efficiently utilizes available labeled data, given threat reports, whose categories are to be automatically predicted. We empirically evaluated our approach on 18,257 real-world threat reports generated between year 2000 and 2018 from various computer security organizations to demonstrate its superiority by comparing its performance with an existing approach.

In this paper we look into 5G requirements for channel coding and review candidate channel coding schemes for 5G. A comparative study is presented for possible channel coding candidates of 5G covering Convolutional, Turbo, Low Density Parity Check (LDPC), and Polar codes. It seems that polar code with Successive Cancellation List (SCL) decoding using small list length (such as 8) is a promising choice for short message lengths (≤128 bits) due to its error performance and relatively low complexity. Also adopting non-binary LDPC can provide good performance on the expense of increased complexity but with better spectral efficiency. Considering the implementation, polar code with decoding algorithms based on SCL required small area and low power consumption when compared to LDPC codes. For larger message lengths (≥256 bits) turbo code can provide better performance at low coding rates (\textbackslashtextless;1/2).

Several algorithms were introduced in data encryption and decryptionsto protect threats and intruders from stealing and destroying data. A DNA cryptography is a new concept that has attracted great interest in the information security. In this paper, we propose a new enhanced polyalphabetic cipher algorithm (EPCA) as enhanced algorithm for the Vigenere cipher to avoid the limitations and the weakness of Vigenere cipher. A DNA technology is used to convert binary data to DNA strand. We compared the EPCA with Vigenere cipher in terms of memory space and run time. The EPCA has theoretical run time of O(N), at worst case. The EPCA shows better performance in average memory space and closed results in average running time, for the tested data.

Surveillance is the monitoring of the behavior, activities or other changing information whereas security means the state of being protected from harmful activities. Nowadays proper surveillance security is considered as a challenging issue in the world and security has become a major concern from real life to virtual life. Tech-giants are implementing new solutions & techniques for better security assessment. This paper illustrates the design and implementation of a Belief Rule Based Expert System (BRBES) to overcome the uncertainty problems during bank security assessment. The proposed expert system has been developed based on generic Belief Rule Based (BRB) inference methodology using Evidential Reasoning algorithm (RIMER). Real-time security data has been taken from several banks of Bangladesh in conjunction with the expert's opinion to construct the knowledge base. This expert system provides more reliable and effective result under uncertainties which is better than any other traditional expert's prediction. Real life case studies were used for the validation of this system. Also, the outcome is compared with the real-life security system. Furthermore, the architectural design, implementation and utilization of an expert system to assess bank security under uncertainty are also discussed in this paper.

We conduct formal verification of the divide and conquer key distribution scheme (DC DHKE)-a contributory group key agreement that uses a quasilinear amount of exponentiations with respect to the number of communicating parties. The verification is conducted using both ProVerif and TLA+ as tools. ProVerif is used to verify the protocol correctness as well as its security against passive attacker; while TLA+ is utilized to verify whether all participants in the protocol retrieve the mutual key simultaneously. We also verify the ING and GDH.3 protocol for comparative purposes. The verification results show that the ING, GDH.3, and DC DHKE protocols satisfy the pre-meditated correctness, security, and liveness properties. However, the GDH.3 protocol does not satisfy the liveness property stating that all participants obtain the mutual key at the same time.