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C++ is a broadly used programming language known for its versatility in handling diverse tech projects. However, the increasing prevalence of security deficiencies and threats poses significant challenges in the industry, hamper to work processes and technical damage to developers. In this paper we will be studying alleviating security deficiencies in C++ and avoiding deficiencies. [ABSTRACT FROM AUTHOR]

In self-forming Mobile Ad Hoc Networks (MANETs), the work introduces dynamic routing for heterogeneous nodes in an infrastructure-limited environment. Military, healthcare, and disaster recovery applications require efficient communication on these networks. Further, multi-hop MANET communication requires reducing packet loss due to energy limits and buffer overflow. The proposed routing mechanism selects energy-efficient, uncongested intermediary nodes and dynamically optimized routes for communication depending on packet loss likelihood. It greatly lowers packet loss, improving network reliability. Our mechanism outperforms energy-aware, buffer-aware, and reactive protocols in network lifetime and packet delivery. We quantify these improvements to verify our mechanism's effectiveness. The proposed mechanism makes MANETs a reliable solution for important applications by proactively optimizing routes to reduce packet loss. [ABSTRACT FROM AUTHOR]

To evaluate the severity of a security vulnerability a security researcher usually tries to prove its exploitability by writing an actual exploit. In the case of buffer overflows on the heap, a necessary part of this is manipulating the heap layout in a way that creates an exploitable state, usually by placing a vulnerable object adjacent to a target object. This requires manual effort and extensive knowledge of the target. With a target as complex as the Linux kernel, this problem becomes highly non-trivial. At the current time, there has been little research in terms of employing algorithmic solutions for this. In this work, we present Kernel-SIEVE, a framework for evaluating heap layout manipulation algorithms that target the SLAB/SLUB allocator in the Linux kernel. Inspired by previous work that targets user-space allocators [33–35] it provides an interface for triggering allocations/deallocations in the kernel and contains a feedback loop that returns the resulting distance of two target objects. With this, we create the (to our knowledge) first performance benchmarks for heap layout manipulation algorithms in the Linux kernel. We present and evaluate two algorithms: A pseudo-random search, whose performance serves as a baseline, and KEvoHeap, a genetic algorithm based on Heelan’s EvoHeap [33, 35]. We show that KEvoHeap is successful at creating the desired heap layout in all test cases and also surpasses the user-space performance benchmarks of EvoHeap. Finally, we discuss the challenges of applying these kinds of algorithms in real-world scenarios and weigh different possible approaches to tackle the problems that arise. Our research results are publicly available on GitHub [43].

Abstract Buffer overflow poses a serious threat to the memory security of modern operating systems. It overwrites the contents of other memory areas by breaking through the buffer capacity limit, destroys the system execution environment, and provides implementation space for various system attacks such as program control flow hijacking. That makes it a wide range of harms. A variety of security technologies have been proposed to deal with system security problems including buffer overflow. For example, No eXecute (NX for short) is a memory management technology commonly used in Harvard architecture. It can refuse the execution of code which residing in a specific memory, and can effectively suppress the abnormal impact of buffer overflow on control flow. Therefore, in recent years, it has also been used in the field of system security, deriving a series of solutions based on NX technology, such as ExecShield, DEP, StackGuard, etc. However, these security solutions often rely too much on the processor architecture so that the protection coverage is insufficient and the accuracy is limited. Especially in the emerging system architecture field represented by RISC-V, there is still a lack of effective solutions for buffer overflow vulnerabilities. With the continuous rapid development of the system architecture, it is urgent to develop defense methods that are applicable to different system application environments and oriented to all executable memory spaces to meet the needs of system security development. Therefore, we propose BOP, A new system memory security design method based on RISC-V extended instructions, to build a RISC-V buffer overflow detection and defense system and deal with the buffer overflow threat in RISC-V. According to this method, NX technology can be combined with program control flow analysis, and NX bit mechanism can be used to manage the executability of memory space, so as to achieve a more granular detection and defense of buffer overflow attacks that may occur in RISC-V system environment. In addition, The memory management and control function of BOP is not only very suitable for solving the security problems in the existing single architecture system, but also widely applicable to the combination of multiple heterogeneous systems. Graphical abstract

Buffer overflow poses a serious threat to the memory security of modern operating systems. It overwrites the contents of other memory areas by breaking through the buffer capacity limit, destroys the system execution environment, and provides implementation space for various system attacks such as program control flow hijacking. That makes it a wide range of harms. A variety of security technologies have been proposed to deal with system security problems including buffer overflow. For example, No eXecute (NX for short) is a memory management technology commonly used in Harvard architecture. It can refuse the execution of code which residing in a specific memory, and can effectively suppress the abnormal impact of buffer overflow on control flow. Therefore, in recent years, it has also been used in the field of system security, deriving a series of solutions based on NX technology, such as ExecShield, DEP, StackGuard, etc. However, these security solutions often rely too much on the processor architecture so that the protection coverage is insufficient and the accuracy is limited. Especially in the emerging system architecture field represented by RISC-V, there is still a lack of effective solutions for buffer overflow vulnerabilities. With the continuous rapid development of the system architecture, it is urgent to develop defense methods that are applicable to different system application environments and oriented to all executable memory spaces to meet the needs of system security development. Therefore, we propose BOP, A new system memory security design method based on RISC-V extended instructions, to build a RISC-V buffer overflow detection and defense system and deal with the buffer overflow threat in RISC-V. According to this method, NX technology can be combined with program control flow analysis, and NX bit mechanism can be used to manage the executability of memory space, so as to achieve a more granular detection and defense of buffer overflow attacks that may occur in RISC-V system environment. In addition, The memory management and control function of BOP is not only very suitable for solving the security problems in the existing single architecture system, but also widely applicable to the combination of multiple heterogeneous systems. [ABSTRACT FROM AUTHOR]

With the increasing popularity of IoT (Internet-of-Things) devices, their security becomes an increasingly important issue. Buffer overflow vulnerabilities have been known for decades, but are still relevant, especially for embedded devices where certain security measures cannot be implemented due to hardware restrictions or simply due to their impact on performance. Therefore, many buffer overflow detection mechanisms check for overflows only before critical data are used. All data that an attacker could use for his own purposes can be considered critical. It is, therefore, essential that all critical data are checked between writing a buffer and its usage. This paper presents a vulnerability of the ESP32 microcontroller, used in millions of IoT devices, that is based on a pointer that is not protected by classic buffer overflow detection mechanisms such as Stack Canaries or Shadow Stacks. This paper discusses the implications of vulnerability and presents mitigation techniques, including a patch, that fixes the vulnerability. The overhead of the patch is evaluated using simulation as well as an ESP32-WROVER-E development board. We showed that, in the simulation with 32 general-purpose registers, the overhead for the CoreMark benchmark ranges between 0.1% and 0.4%. On the ESP32, which uses an Xtensa LX6 core with 64 general-purpose registers, the overhead went down to below 0.01%. A worst-case scenario, modeled by a synthetic benchmark, showed overheads up to 9.68%. [ABSTRACT FROM AUTHOR]

Software exploitation detection remains unresolved problem. Software exploits that target known and unknown vulnerabilities are constantly used in attacks. Signature-based detection techniques are limited to known exploits and susceptible to circumvention. Current research on the use of Machine Learning (ML) for software exploitation detection is limited in quantity and use cases. Existing research lacks the use of public datasets, discussions of feature importance, and elaboration of parameters that affect data preparation and subsequently model performance. This paper presents ML models based on different ensemble algorithms to detect software exploitation using runtime traces. We focus on buffer overflow vulnerabilities in user-space applications within Windows Operating Systems (OS), given the prevalence of the type of vulnerability and the OS. We utilized a publicly available raw dataset of 11 Windows applications under exploitation. Multiple distinct models (based on Random Forest and XGBoost) are created and tested. Testing was performed several times using various aggregation parameters and different testing applications. Our results demonstrate that we can achieve up to 100% recall with 0% false positive rate. We report on the different parameters that must be addressed to curate runtime traces and demonstrate their impact on the performance of the ML models. We demonstrate that the proper training of models on a subset of exploitation techniques enables the model to detect techniques never seen before, such as return-oriented programming. Finally, we conclude with a discussion of the important features that had the highest impact on each of the models, along with the key takeaways.

The world of telecommunications, from some years, is revitalizing an high interest to Deep Space Communication (DSC) and Delay Tolerant Network (DTN) architecture as enabling technologies to offer internet services for aerospace vehicles or orbital stations. The communications between the planets in the solar system is well known to present a series of problems that need to be faced. The fundamental challenge with InterPlanetary Communication (IPC) is the intermittent Line of Sight (LoS) caused by planets movement and the significant propagation time. A possible way to face with it is the use of a DTN approach where routers use a store-and-forward transmission in a hop-by-hop message exchange mechanism. Thanks to this approach, it is possible to face many issues related to the transport layer. The main contribution of this work concerns the proposal of a bundle management layer able to assign at the receiver the appropriate bundle rate on the basis of concurrent bundle transmissions, the output link capacity and buffer resources. The proposed strategy can be integrated with a routing algorithm for InterPlanetary Networks (IPNs) and DTN networks. In our case we consider the Earliest Arrival Optimal Delivery Ratio (EAODR) routing that applies a modified temporal graph to select the IPN nodes. Performance evaluation have been led out to show the benefits of our proposal on the single links and also on the routing scheme in terms of bundle delivery ratio and average bundle delivery time.

With the increasing popularity of IoT (Internet-of-Things) devices, their security becomes an increasingly important issue. Buffer overflow vulnerabilities have been known for decades, but are still relevant, especially for embedded devices where certain security measures cannot be implemented due to hardware restrictions or simply due to their impact on performance. Therefore, many buffer overflow detection mechanisms check for overflows only before critical data are used. All data that an attacker could use for his own purposes can be considered critical. It is, therefore, essential that all critical data are checked between writing a buffer and its usage. This paper presents a vulnerability of the ESP32 microcontroller, used in millions of IoT devices, that is based on a pointer that is not protected by classic buffer overflow detection mechanisms such as Stack Canaries or Shadow Stacks. This paper discusses the implications of vulnerability and presents mitigation techniques, including a patch, that fixes the vulnerability. The overhead of the patch is evaluated using simulation as well as an ESP32-WROVER-E development board. We showed that, in the simulation with 32 general-purpose registers, the overhead for the CoreMark benchmark ranges between 0.1% and 0.4%. On the ESP32, which uses an Xtensa LX6 core with 64 general-purpose registers, the overhead went down to below 0.01%. A worst-case scenario, modeled by a synthetic benchmark, showed overheads up to 9.68%.

This paper examines the security provided by different implementations of Address Space Layout Randomization (ASLR). ASLR is a security mechanism that increases control-flow integrity by making it more difficult for an attacker to properly execute a buffer-overflow attack, even in systems with vulnerable software. The strength of ASLR lies in the randomness of the offsets it produces in memory layouts. We compare multiple operating systems, each compiled for two different hardware architectures, and measure the amount of entropy provided to a vulnerable application. Our paper is the first publication that we are aware of that quantitatively compares the entropy of different ASLR implementations. In addition, we provide a method for remotely assessing the efficacy of a particular security feature on systems that are otherwise unavailable for analysis, and highlight the need for independent evaluation of security mechanisms.

Energy efficient and completely reliable data gathering in resource constrained sparse Underwater Wireless Sensor Networks (UWSNs) is challenging and requires dedicated routing techniques. Routing having mobility assistance employs a Mobile Sink (MS) or a mobile relay for data gathering. It mitigates transmission power consumption as well as relaying overhead. But multiple MSs should be deployed in order to reduce the load of a single MS. The visiting schedule of each MS should consider the priority of data, data gathering delay and buffer overflow of each sensor. In order to address these issues, a priority-based data gathering scheme using multiple MSs for clustered UWSN is proposed to help in pipeline leakage detection under the water. In this work, each MS is deployed in such a way so that it can move in both the directions i.e., top to bottom or bottom to top. When a Cluster Head (CH) receives critical data, it sends an emergency notification to the nearest MS via other CHs. Upon receiving the emergency notification, MS immediately visits that CH to gather the critical data (oil leakage). [ABSTRACT FROM AUTHOR]

Security vulnerabilities are present in most software systems, especially in projects with a large codebase, with several versions over the years, developed by many developers. Issues with memory management, in particular buffer overflow, are among the most frequently exploited vulnerabilities in software systems developed in C/C++. Nevertheless, most buffer overflow vulnerabilities are not detectable by vulnerability detection tools and static analysis tools (SATs). To improve vulnerability detection, we need to better understand the characteristics of such vulnerabilities and their root causes. In this study, we analyze 159 vulnerable code units from three representative projects (i.e., Linux Kernel, Mozilla, and Xen). First, the vulnerable code is characterized using the Orthogonal Defect Classification (ODC), showing that most buffer overflow vulnerabilities are related to missing or incorrect checking (e.g., missing if construct around statement or incorrect logical expression used as branch condition). Then, we run two widely used C/C++ Static Analysis Tools (SATs) (i.e., CppCheck and Flawfinder) on the vulnerable and neutral (after the vulnerability fix) versions of each code unit, showing the low effectiveness of this type of tool in detecting buffer overflow vulnerabilities. Finally, we characterize the vulnerable and neutral versions of each code unit using software metrics, demonstrating that, although such metrics are frequently used as indicators of software quality, there is no clear correlation between them and the existence of buffer overflow in the code. As a result, we highlight a set of observations that should be considered to improve the detection of buffer overflow vulnerabilities.

Summary: Recent research introduces data gathering using mobile data collectors to conserve energy and elevate the hotspot problem. However, many of them suffer from buffer overflow due to the limited memory capacity of objects, even more, when dealing with heterogeneous Internet of Things (IoT) devices. Emerging bio‐inspiration technologies provide a novel direction for data collection and make it more intelligent and available. In this paper, we present an intelligent data gathering schema by taking into consideration buffer overflow called Center Gravity Mobile data collector based on Salp Swarm Algorithm (CGMSSA). To conduct data collection to improve the network performance, we adopt a hybridized distributed bio‐inspired technique to elect chefs and salp swarm intelligence to control data collector movement. First, we select chefs using grey wolf optimization. Then, several groups are formed. Then, a mobile data collector is adopted to access chefs, following the buffer overflow value and gather information. Considerable experiments are conducted to demonstrate that our solution can efficiently enhance the lifetime of the network and increase data throughput. [ABSTRACT FROM AUTHOR]

In this paper, we present three datasets that have been built from network traffic traces using ASNM (Advanced Security Network Metrics) features, designed in our previous work. The first dataset was built using a state-of-the-art dataset CDX 2009 that was collected during a cyber defense exercise, while the remaining two datasets were collected by us in 2015 and 2018 using publicly available network services containing buffer overflow and other high severity vulnerabilities. These two datasets contain several adversarial obfuscation techniques that were applied onto malicious as well as legitimate traffic samples during “the execution” of their TCP network connections. Adversarial obfuscation techniques were used for evading machine learning-based network intrusion detection classifiers. We show that the performance of such classifiers can be improved when partially augmenting their training data by samples obtained from obfuscation techniques. In detail, we utilized tunneling obfuscation in HTTP(S) protocol and non-payload-based obfuscations modifying various properties of network traffic by, e.g., TCP segmentation, re-transmissions, corrupting and reordering of packets, etc. To the best of our knowledge, this is the first collection of network traffic data that contains adversarial techniques and is intended for non-payload-based network intrusion detection and adversarial classification. Provided datasets enable testing of the evasion resistance of arbitrary machine learning-based classifiers.

Smart cities are driven by huge amount of data collected from sensors deployed across the city. Sensors typically form a multi-hop network with a base station (BS ) in order to send their data to the command and control center. However, sparse deployment of sensors can leave subsets of the network partitioned from the rest of the network. In such a case, isolated partitions cannot forward their data to the BS . Consequently, network coverage and data fidelity decline. A possible solution to link partitions and provide connectivity is employing mobile data collectors (MDCs). A smart vehicle supporting wireless communication can act as an MDC and carry data between sensors and the BS . Using a single MDC extends the average tour length. To minimize the maximum tour length, multiple MDCs can be employed. To identify sensors to be visited by each MDC, this paper clusters partitions as many as the number of MDCs and assigns an MDC for each cluster. Then two different cooperative data collection schemes are considered based on the availability of inter-MDC data exchange. If MDCs collaborate in data delivery, they meet at certain meeting points for data exchange. Such a cooperation avoids the requirement of visiting the BS for some MDCs and reduces tour lengths. On the other hand, MDCs closer to the BS can experience data loss due to buffer overflow given the higher volume of the accumulated data. Presented approaches are evaluated in terms of maximum tour length, data latency, and data loss. The smart city application is simulated with deployment of sensors on certain amenity types. Geographic data is obtained from a volunteered geographic information system and MDC mobility is restricted with the road network. Obtained results indicate that MDC cooperation decreases maximum tour length at the expense of increased rate of data loss and data latency.

Previous work had studied how to ensure the transmission time of delay-sensitive flows,but falled short in its effectiveness for a given period.Motivated by that and based on SDN/OpenFlow framework,a SDN-based source rate control (SSRC) algorithm was proposed.With a global view of network,SSRC can quickly locate the potential congestion node,adjust the transmission rate of source and reduce the response time of SSRC.The experiment results show that compared with DCTCP and other algorithms,SSRC can shorten the completion time of flows by 75% in average,which can ensure the transmission time of delay-sensitive and solve the problem of Incast well.

RISC-V is a promising open-source architecture that targets low-power embedded devices and system-on-chips (SoCs). However, there is a dearth of practical and low-overhead security solutions in the RISC-V architecture. Programs compiled using RISC-V toolchains are still vulnerable to code injection and code reuse attacks, such as buffer overflow and return-oriented programming (ROP). In this article, we propose two hardware-implemented security extensions to RISC-V that provides a defense mechanism against such attacks. We first employ a physically unclonable function (PUF)-based randomized canary generation technique that removes the need to store the sensitive canary words in memory or CPU registers, thereby being more secure, while incurring low overheads. We implement the proposed Canary Engine in RISC-V RocketChip with rocket custom coprocessor (RoCC). The simulation results show 2.2% average execution overhead with a single buffer protection, while a $10\times $ increase in buffer count only increases the overhead by $1.5\times $ when protection is extended to all buffers. We further improve upon this with a dedicated security coprocessor flow integrity extensions for embedded RISC-V (FIXER), implemented on the RoCC. FIXER enforces fine-grained control-flow integrity (CFI) of running programs on backward edges (returns) and forward edges (calls) without requiring any architectural modifications to the processor core. Compared to software-based solutions, FIXER reduces energy overhead by 60% at minimal execution time (1.5%) and area (2.9%) overheads. [ABSTRACT FROM AUTHOR]

A model of a single-machine production system with finite magazine capacity is investigated. The input flow of jobs is organized according to geometric distribution of interarrival times, while processing times are assumed to be generally distributed. The closed-form formula for the generating function of the time to the first buffer overflow distribution conditioned by the initial buffer state is found. The analytical approach based on the idea of embedded Markov chain, the formula of total probability and linear algebra is applied. The corresponding result for next buffer overflows is also given. Numerical examples are attached as well.

At present, the IoT devices face many kinds of software and hardware attacks, especially buffer overflow attacks. This paper presents an architectural-enhanced security hardware design to detect buffer overflow attacks. One part of the design is instructions monitoring and verification used to trace the execution behavior of programs. Another one is secure tag validation used to monitor the attributes of every memory segment. The automated extraction tools extract the monitoring model and secure tag of each memory segment at the compile time. At run-time, the designed hardware observes its dynamic execution trace and checks whether the trace conforms to the permissible behavior, if not the appropriate response mechanisms will be triggered. The proposed schemes don’t change the compiler or the existing instruction set and imposes no restriction to the software developer. The architectural design is implemented on an actual OR1200-FPGA platform. The experimental analysis shows that the proposed techniques can detect a wide range of buffer overflow attacks. And it takes low performance penalties and minimal overheads.

Static buffer overflow detection techniques tend to report too many false positives fundamentally due to the lack of software execution information. It is very time consuming to manually inspect all the static warnings. In this paper, we propose BovInspector, a framework for automatically validating static buffer overflow warnings and providing suggestions for automatic repair of true buffer overflow warnings for C programs. Given the program source code and the static buffer overflow warnings, BovInspector first performs warning reachability analysis. Then, BovInspector executes the source code symbolically under the guidance of reachable warnings. Each reachable warning is validated and classified by checking whether all the path conditions and the buffer overflow constraints can be satisfied simultaneously. For each validated true warning, BovInspector provides suggestions to automatically repair it with 11 repair strategies. BovInspector is complementary to prior static buffer overflow discovery schemes. Experimental results on real open source programs show that BovInspector can automatically validate on average 60% of total warnings reported by static tools. [ABSTRACT FROM AUTHOR]

A model of a single-machine production system with finite magazine capacity is investigated. The input flow of jobs is organized according to geometric distribution of interarrival times, while processing times are assumed to be generally distributed. The closed-form formula for the generating function of the time to the first buffer over ow distribution conditioned by the initial buffer state is found. The analytical approach based on the idea of embedded Markov chain, the formula of total probability and linear algebra is applied. The corresponding result for next buffer over ows is also given. Numerical examples are attached as well. [ABSTRACT FROM AUTHOR]

A single-channel FIFO queueing model with finite buffer capacity and the multiple vacation policy is investigated, in which jobs arrive according to a compound Poisson process and are being processed individually with a general-type distribution function of the service time. A multiple vacation period, consisting of a number of independent generally-distributed server vacations, is being started each time when the system becomes empty. During this period, the processing of jobs is suspended. Successive server vacations are being initialised until at least one job is present in the buffer at the completion epoch of one of them. A compact formula for the Laplace transform of the distribution of the time to the first buffer overflow, conditioned by initial number of packets present in the buffer, is found. The analytical approach is based on the paradigm of embedded Markov chain, integral equations and Korolyuk's potential idea. Numerical illustrating examples are attached as well. [ABSTRACT FROM AUTHOR]

Different Machine Learning techniques to detect software vulnerabilities have emerged in scientific and industrial scenarios. Different actors in these scenarios aim to develop algorithms for predicting security threats without requiring human intervention. However, these algorithms require data-driven engines based on the processing of huge amounts of data, known as datasets. This paper introduces the SonarCloud Vulnerable Code Prospector for C (SVCP4C). This tool aims to collect vulnerable source code from open source repositories linked to SonarCloud, an online tool that performs static analysis and tags the potentially vulnerable code. The tool provides a set of tagged files suitable for extracting features and creating training datasets for Machine Learning algorithms. This study presents a descriptive analysis of these files and overviews current status of C vulnerabilities, specifically buffer overflow, in the reviewed public repositories. [ABSTRACT FROM AUTHOR]